hypernets_processor.data_io.hypernets_ds_builder module

HypernetsDSBuilder class

class hypernets_processor.data_io.hypernets_ds_builder.HypernetsDSBuilder(context=None, variables_dict_defs={'CAL': {'err_corr_systematic_corr_rad_irr_gains': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on gains (calibration coefficients) that is correlated between radiance and irradiance', 'standard_name': 'correlation matrix of systematic error on gains (correlated radiance and irradiance)', 'units': '-'}, 'dim': ['calibrationdates', 'wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int16'>, 'offset': 0.0, 'scale_factor': 0.0001}}, 'err_corr_systematic_indep_gains': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on gains (calibration coefficients) that is not correlated between radiance and irradiance', 'standard_name': 'independent correlation matrix of systematic error on gains', 'units': '-'}, 'dim': ['calibrationdates', 'wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int16'>, 'offset': 0.0, 'scale_factor': 0.0001}}, 'gains': {'attributes': {'long_name': 'gains (calibration coefficients)', 'standard_name': 'gains', 'unc_comps': ['u_rel_systematic_indep_gains', 'u_rel_systematic_corr_rad_irr_gains'], 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['calibrationdates', 'wavelength'], 'dtype': <class 'numpy.float32'>}, 'non_linearity_coefficients': {'attributes': {'long_name': 'non linearity coefficients', 'standard_name': 'non linearity coefficients', 'units': '-'}, 'dim': ['nonlineardates', 'nonlinearcoef'], 'dtype': <class 'numpy.float64'>}, 'u_rel_systematic_corr_rad_irr_gains': {'attributes': {'err_corr': [{'dim': 'calibrationdates', 'form': 'random', 'params': [], 'units': []}, {'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_corr_rad_irr_gains'], 'units': []}], 'long_name': 'the systematic relative uncertainty component on gains (calibration coefficients) that is correlated between radiance and irradiance, in percent of the gains', 'standard_name': 'systematic relative uncertainty on gains (correlated radiance and irradiance)', 'units': '%'}, 'dim': ['calibrationdates', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_gains': {'attributes': {'err_corr': [{'dim': 'calibrationdates', 'form': 'random', 'params': [], 'units': []}, {'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_gains'], 'units': []}], 'long_name': 'the systematic relative uncertainty component on gains (calibration coefficients) that is not correlated between radiance and irradiance, in percent of the gains', 'standard_name': 'independent systematic relative uncertainty on gains', 'units': '%'}, 'dim': ['calibrationdates', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'wavelength': {'attributes': {'long_name': 'Wavelength', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}, 'wavelength_coefficients': {'attributes': {'long_name': 'Polynomial coefficients for wavelength scale', 'standard_name': 'wavelength coefficients', 'units': '-'}, 'dim': ['wavdates', 'wavcoef'], 'dtype': <class 'numpy.float32'>}, 'wavpix': {'attributes': {'long_name': 'Wavelength pixel in L0 data that corresponds to the given wavelength', 'standard_name': 'wavelength pixel'}, 'dim': ['calibrationdates', 'wavelength'], 'dtype': <class 'numpy.uint16'>}}, 'L0A_BLA': {'acceleration_x_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_x_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'digital_number': {'attributes': {'long_name': 'Digital number, raw data', 'standard_name': 'digital_number', 'units': '-'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.int32'>}, 'integration_time': {'attributes': {'long_name': 'Integration time during measurement', 'standard_name': 'integration_time', 'units': 's'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint32'>}, 'paa_abs': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_abs', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_abs as in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ask': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ask', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ask as in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ref': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ref', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ref in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence'], 'long_name': 'A variable with the standard name of quality_flag contains an indication of assessed quality information of another data variable. The linkage between the data variable and the variable or variables with the standard_name of quality_flag is achieved using the ancillary_variables attribute.', 'standard_name': 'quality_flag'}, 'dim': ['scan'], 'dtype': 'flag'}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'temperature': {'attributes': {'long_name': 'temperature of instrument', 'standard_name': 'temperature', 'units': 'degrees C'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'L0A_IRR': {'acceleration_x_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_x_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'digital_number': {'attributes': {'long_name': 'Digital number, raw data', 'standard_name': 'digital_number', 'units': '-'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.int32'>}, 'integration_time': {'attributes': {'long_name': 'Integration time during measurement', 'standard_name': 'integration_time', 'units': 's'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint32'>}, 'paa_abs': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_abs', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_abs as in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ask': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ask', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ask as in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ref': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ref', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ref in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence'], 'long_name': 'A variable with the standard name of quality_flag contains an indication of assessed quality information of another data variable. The linkage between the data variable and the variable or variables with the standard_name of quality_flag is achieved using the ancillary_variables attribute.', 'standard_name': 'quality_flag'}, 'dim': ['scan'], 'dtype': 'flag'}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'temperature': {'attributes': {'long_name': 'temperature of instrument', 'standard_name': 'temperature', 'units': 'degrees C'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'L0A_RAD': {'acceleration_x_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_x_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'digital_number': {'attributes': {'long_name': 'Digital number, raw data', 'standard_name': 'digital_number', 'units': '-'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.int32'>}, 'integration_time': {'attributes': {'long_name': 'Integration time during measurement', 'standard_name': 'integration_time', 'units': 's'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint32'>}, 'paa_abs': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_abs', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_abs as in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ask': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ask', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ask as in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ref': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ref', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ref in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence'], 'long_name': 'A variable with the standard name of quality_flag contains an indication of assessed quality information of another data variable. The linkage between the data variable and the variable or variables with the standard_name of quality_flag is achieved using the ancillary_variables attribute.', 'standard_name': 'quality_flag'}, 'dim': ['scan'], 'dtype': 'flag'}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'temperature': {'attributes': {'long_name': 'temperature of instrument', 'standard_name': 'temperature', 'units': 'degrees C'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'L0B_IRR': {'acceleration_x_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_x_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['series'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'dark_signal': {'attributes': {'long_name': 'Digital number, raw data, dark signal', 'standard_name': 'digital number for dark signal', 'unc_comps': ['u_rel_random_dark_signal'], 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.uint32'>}, 'digital_number': {'attributes': {'long_name': 'Digital number, raw data', 'standard_name': 'digital_number', 'unc_comps': ['u_rel_random_digital_number'], 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.int32'>}, 'integration_time': {'attributes': {'long_name': 'Integration time during measurement', 'standard_name': 'integration_time', 'units': 's'}, 'dim': ['series'], 'dtype': <class 'numpy.uint32'>}, 'n_total_scans': {'attributes': {'long_name': '', 'standard_name': 'number of total scans acquired', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans': {'attributes': {'long_name': '', 'standard_name': 'number of valid scans used in average of digital number', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'paa_abs': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_abs', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_abs as in metadata', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ask': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ask', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ask as in metadata', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ref': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ref', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ref in metadata', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence'], 'long_name': 'A variable with the standard name of quality_flag contains an indication of assessed quality information of another data variable. The linkage between the data variable and the variable or variables with the standard_name of quality_flag is achieved using the ancillary_variables attribute.', 'standard_name': 'quality_flag'}, 'dim': ['series'], 'dtype': 'flag'}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'std_dark_signal': {'attributes': {'long_name': 'standard deviation on digital number of dark signal', 'standard_name': 'standard deviation dark_signal', 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'std_digital_number': {'attributes': {'long_name': 'standard deviation on digital number', 'standard_name': 'standard deviation digital_number', 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'temperature': {'attributes': {'long_name': 'temperature of instrument', 'standard_name': 'temperature', 'units': 'degrees C'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>}, 'u_rel_random_dark_signal': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'series', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on digital number for dark signal', 'standard_name': 'random relative uncertainty on digital number for dark signal', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_random_digital_number': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'series', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on digital number', 'standard_name': 'random relative uncertainty on digital number', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'L0B_RAD': {'acceleration_x_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_x_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['series'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'dark_signal': {'attributes': {'long_name': 'Digital number, raw data, dark signal', 'standard_name': 'digital number for dark signal', 'unc_comps': ['u_rel_random_dark_signal'], 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.uint32'>}, 'digital_number': {'attributes': {'long_name': 'Digital number, raw data', 'standard_name': 'digital_number', 'unc_comps': ['u_rel_random_digital_number'], 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.int32'>}, 'integration_time': {'attributes': {'long_name': 'Integration time during measurement', 'standard_name': 'integration_time', 'units': 's'}, 'dim': ['series'], 'dtype': <class 'numpy.uint32'>}, 'n_total_scans': {'attributes': {'long_name': '', 'standard_name': 'number of total scans acquired', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans': {'attributes': {'long_name': '', 'standard_name': 'number of valid scans used in average of digital number', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'paa_abs': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_abs', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_abs as in metadata', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ask': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ask', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ask as in metadata', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ref': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ref', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ref in metadata', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence'], 'long_name': 'A variable with the standard name of quality_flag contains an indication of assessed quality information of another data variable. The linkage between the data variable and the variable or variables with the standard_name of quality_flag is achieved using the ancillary_variables attribute.', 'standard_name': 'quality_flag'}, 'dim': ['series'], 'dtype': 'flag'}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'std_dark_signal': {'attributes': {'long_name': 'standard deviation on digital number of dark signal', 'standard_name': 'standard deviation dark_signal', 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'std_digital_number': {'attributes': {'long_name': 'standard deviation on digital number', 'standard_name': 'standard deviation digital_number', 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'temperature': {'attributes': {'long_name': 'temperature of instrument', 'standard_name': 'temperature', 'units': 'degrees C'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>}, 'u_rel_random_dark_signal': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'series', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on digital number for dark signal', 'standard_name': 'random relative uncertainty on digital number for dark signal', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_random_digital_number': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'series', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on digital number', 'standard_name': 'random relative uncertainty on digital number', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'L_L1A_IRR': {'acceleration_x_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_x_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'err_corr_systematic_corr_rad_irr_irradiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on irradiance that is correlated with radiance', 'standard_name': 'correlation matrix of systematic error on irradiance, correlated with radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_indep_irradiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on irradiance that is not correlated with radiance', 'standard_name': 'independent correlation matrix of systematic error on irradiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'irradiance': {'attributes': {'long_name': 'downwelling irradiance', 'standard_name': 'irradiance', 'unc_comps': ['u_rel_random_irradiance', 'u_rel_systematic_indep_irradiance', 'u_rel_systematic_corr_rad_irr_irradiance'], 'units': 'mW m^-2 nm^-1'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>}, 'paa_abs': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_abs', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_abs as in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ask': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ask', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ask as in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ref': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ref', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ref in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence'], 'long_name': 'A variable with the standard name of quality_flag contains an indication of assessed quality information of another data variable. The linkage between the data variable and the variable or variables with the standard_name of quality_flag is achieved using the ancillary_variables attribute.', 'standard_name': 'quality_flag'}, 'dim': ['scan'], 'dtype': 'flag'}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'u_rel_random_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on downwelling irradiance', 'standard_name': 'random relative uncertainty on irradiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_corr_rad_irr_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_corr_rad_irr_irradiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on downwelling irradiance that is correlated with radiance', 'standard_name': 'systematic relative uncertainty on irradiance, correlated with radiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_irradiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on downwelling irradiance that is not correlated with radiance', 'standard_name': 'independent systematic relative uncertainty on irradiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'L_L1A_RAD': {'acceleration_x_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_x_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'err_corr_systematic_corr_rad_irr_radiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on radiance that is correlated with irradiance', 'standard_name': 'correlation matrix of systematic error on radiance, correlated with irradiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_indep_radiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on radiance that is not correlated with irradiance', 'standard_name': 'independent correlation matrix of systematic error on radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'paa_abs': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_abs', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_abs as in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ask': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ask', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ask as in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ref': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ref', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ref in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence'], 'long_name': 'A variable with the standard name of quality_flag contains an indication of assessed quality information of another data variable. The linkage between the data variable and the variable or variables with the standard_name of quality_flag is achieved using the ancillary_variables attribute.', 'standard_name': 'quality_flag'}, 'dim': ['scan'], 'dtype': 'flag'}, 'radiance': {'attributes': {'long_name': 'upwelling radiance', 'standard_name': 'radiance', 'unc_comps': ['u_rel_random_radiance', 'u_rel_systematic_indep_radiance', 'u_rel_systematic_corr_rad_irr_radiance'], 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'u_rel_random_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on upwelling radiance', 'standard_name': 'random relative uncertainty on radiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_corr_rad_irr_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_corr_rad_irr_radiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on radiance that is correlated with irradiance', 'standard_name': 'systematic relative uncertainty on radiance, correlated with irradiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_radiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on radiance that is not correlated with irradiance', 'standard_name': 'independent systematic relative uncertainty on radiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'L_L1B_IRR': {'acceleration_x_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_x_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['series'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'err_corr_systematic_corr_rad_irr_irradiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on irradiance that is correlated with radiance', 'standard_name': 'correlation matrix of systematic error on irradiance, correlated with radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_indep_irradiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on irradiance that is not correlated with radiance', 'standard_name': 'independent correlation matrix of systematic error on irradiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'irradiance': {'attributes': {'long_name': 'downwelling irradiance', 'standard_name': 'irradiance', 'unc_comps': ['u_rel_random_irradiance', 'u_rel_systematic_indep_irradiance', 'u_rel_systematic_corr_rad_irr_irradiance'], 'units': 'mW m^-2 nm^-1'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'n_total_scans': {'attributes': {'long_name': '', 'standard_name': 'number of total scans acquired for VNIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_total_scans_SWIR': {'attributes': {'long_name': '', 'standard_name': 'number of total scans acquired for SWIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans': {'attributes': {'long_name': '', 'standard_name': 'number of valid scans used in average of VNIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans_SWIR': {'attributes': {'long_name': '', 'standard_name': 'number of valid scans used in average of SWIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence', 'discontinuity_VNIR_SWIR'], 'long_name': 'The quality flag field consists of 32 bits. Every bit is related to the absence or presence of a a flag where each bit encodes a flag given in the flag_meanings attribute', 'standard_name': 'quality_flag'}, 'dim': ['series'], 'dtype': 'flag'}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'std_irradiance': {'attributes': {'long_name': 'standard deviation on downwelling irradiance', 'standard_name': 'standard deviation irradiance', 'units': 'mW m^-2 nm^-1'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'u_rel_random_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'series', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on downwelling irradiance', 'standard_name': 'random relative uncertainty on irradiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_corr_rad_irr_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_corr_rad_irr_irradiance'], 'units': []}, {'dim': 'series', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on downwelling irradiance that is correlated with radiance', 'standard_name': 'systematic relative uncertainty on irradiance, correlated with radiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_irradiance'], 'units': []}, {'dim': 'series', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on downwelling irradiance that is not correlated with radiance', 'standard_name': 'independent systematic relative uncertainty on irradiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'L_L1B_RAD': {'acceleration_x_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_x_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['series'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'err_corr_systematic_corr_rad_irr_radiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on radianc that is correlated with irradiancee', 'standard_name': 'correlation matrix of systematic error on radiance, correlated with irradiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_indep_radiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on radiance that is not correlated with irradiance', 'standard_name': 'independent correlation matrix of systematic error on radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'n_total_scans': {'attributes': {'long_name': '', 'standard_name': 'number of total scans acquired for VNIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_total_scans_SWIR': {'attributes': {'long_name': '', 'standard_name': 'number of total scans acquired for SWIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans': {'attributes': {'long_name': '', 'standard_name': 'number of valid scans used in average of VNIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans_SWIR': {'attributes': {'long_name': '', 'standard_name': 'number of valid scans used in average of SWIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence', 'discontinuity_VNIR_SWIR'], 'long_name': 'The quality flag field consists of 32 bits. Every bit is related to the absence or presence of a a flag where each bit encodes a flag given in the flag_meanings attribute', 'standard_name': 'quality_flag'}, 'dim': ['series'], 'dtype': 'flag'}, 'radiance': {'attributes': {'long_name': 'upwelling radiance', 'standard_name': 'radiance', 'unc_comps': ['u_rel_random_radiance', 'u_rel_systematic_indep_radiance', 'u_rel_systematic_corr_rad_irr_radiance'], 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'std_radiance': {'attributes': {'long_name': 'standard deviation on upwelling radiance', 'standard_name': 'standard deviation radiance', 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'u_rel_random_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'series', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on upwelling radiance', 'standard_name': 'random relative uncertainty on radiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_corr_rad_irr_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_corr_rad_irr_radiance'], 'units': []}, {'dim': 'series', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on upwelling radiance that is correlated with irradiance', 'standard_name': 'systematic relative uncertainty on radiance, correlated with irradiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_radiance'], 'units': []}, {'dim': 'series', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on upwelling radiance that is not correlated with irradiance', 'standard_name': 'independent systematic relative uncertainty on radiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'L_L1C': {'acceleration_x_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_x_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['series'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'err_corr_systematic_corr_rad_irr_irradiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on irradiance that is correlated with radiance', 'standard_name': 'correlation matrix of systematic error on irradiance, correlated with radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_corr_rad_irr_radiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on radianc that is correlated with irradiancee', 'standard_name': 'correlation matrix of systematic error on radiance, correlated with irradiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_indep_irradiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on irradiance that is not correlated with radiance', 'standard_name': 'independent correlation matrix of systematic error on irradiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_indep_radiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on radiance that is not correlated with irradiance', 'standard_name': 'independent correlation matrix of systematic error on radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'irradiance': {'attributes': {'long_name': 'downwelling irradiance', 'standard_name': 'irradiance', 'unc_comps': ['u_rel_random_irradiance', 'u_rel_systematic_indep_irradiance', 'u_rel_systematic_corr_rad_irr_irradiance'], 'units': 'mW m^-2 nm^-1'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'n_total_scans': {'attributes': {'long_name': '', 'standard_name': 'number of total scans acquired for VNIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_total_scans_SWIR': {'attributes': {'long_name': '', 'standard_name': 'number of total scans acquired for SWIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans': {'attributes': {'long_name': '', 'standard_name': 'number of valid scans used in average of VNIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans_SWIR': {'attributes': {'long_name': '', 'standard_name': 'number of valid scans used in average of SWIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence', 'discontinuity_VNIR_SWIR'], 'long_name': 'The quality flag field consists of 32 bits. Every bit is related to the absence or presence of a a flag where each bit encodes a flag given in the flag_meanings attribute', 'standard_name': 'quality_flag'}, 'dim': ['series'], 'dtype': 'flag'}, 'radiance': {'attributes': {'long_name': 'upwelling radiance', 'standard_name': 'radiance', 'unc_comps': ['u_rel_random_radiance', 'u_rel_systematic_indep_radiance', 'u_rel_systematic_corr_rad_irr_radiance'], 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'std_irradiance': {'attributes': {'long_name': 'standard deviation on downwelling irradiance', 'standard_name': 'standard deviation irradiance', 'units': 'mW m^-2 nm^-1'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'std_radiance': {'attributes': {'long_name': 'standard deviation on upwelling radiance', 'standard_name': 'standard deviation radiance', 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'u_rel_random_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'series', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on downwelling irradiance', 'standard_name': 'random relative uncertainty on irradiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_random_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'series', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on upwelling radiance', 'standard_name': 'random relative uncertainty on radiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_corr_rad_irr_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_corr_rad_irr_irradiance'], 'units': []}, {'dim': 'series', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on downwelling irradiance that is correlated with radiance', 'standard_name': 'systematic relative uncertainty on irradiance, correlated with radiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_corr_rad_irr_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_corr_rad_irr_radiance'], 'units': []}, {'dim': 'series', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on upwelling radiance that is correlated with irradiance', 'standard_name': 'systematic relative uncertainty on radiance, correlated with irradiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_irradiance'], 'units': []}, {'dim': 'series', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on downwelling irradiance that is not correlated with radiance', 'standard_name': 'independent systematic relative uncertainty on irradiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_radiance'], 'units': []}, {'dim': 'series', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on upwelling radiance that is not correlated with irradiance', 'standard_name': 'independent systematic relative uncertainty on radiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'L_L2A': {'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['series'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'err_corr_systematic_reflectance': {'attributes': {'long_name': 'Error-correlation matrix of systematic uncertainty on hemispherical-conical reflectance factorreflectance uncertainty', 'standard_name': 'err_corr_systematic_reflectance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'n_total_scans': {'attributes': {'long_name': '', 'standard_name': 'number of total radiance scans acquired for VNIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_total_scans_SWIR': {'attributes': {'long_name': '', 'standard_name': 'number of total radiance scans acquired for SWIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans': {'attributes': {'long_name': '', 'standard_name': 'number of valid radiance scans used in average of VNIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans_SWIR': {'attributes': {'long_name': '', 'standard_name': 'number of valid radiance scans used in average of SWIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence', 'discontinuity_VNIR_SWIR'], 'long_name': 'The quality flag field consists of 32 bits. Every bit is related to the absence or presence of a a flag where each bit encodes a flag given in the flag_meanings attribute', 'standard_name': 'quality_flag'}, 'dim': ['series'], 'dtype': 'flag'}, 'reflectance': {'attributes': {'long_name': 'hemispherical-conical reflectance factor', 'standard_name': 'reflectance', 'unc_comps': ['u_rel_random_reflectance', 'u_rel_systematic_reflectance'], 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'std_reflectance': {'attributes': {'long_name': 'standard deviation on hemispherical-conical reflectance factor that is due to the variability in radiance (i.e. not accounting for variability in darks or in irradiance)', 'standard_name': 'standard deviation hemispherical-conical reflectance factor', 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'u_rel_random_reflectance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'series', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'Random relative uncertainty on hemispherical-conical reflectance factor', 'standard_name': 'u_rel_random_reflectance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_reflectance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_reflectance'], 'units': []}, {'dim': 'series', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'Systematic relative uncertainty on hemispherical-conical reflectance factor', 'standard_name': 'u_rel_systematic_reflectance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'L_L2B': {'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['series'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'err_corr_systematic_reflectance': {'attributes': {'long_name': 'Error-correlation matrix of systematic uncertainty on hemispherical-conical reflectance factorreflectance uncertainty', 'standard_name': 'err_corr_systematic_reflectance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'n_total_scans': {'attributes': {'long_name': '', 'standard_name': 'number of total radiance scans acquired for VNIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_total_scans_SWIR': {'attributes': {'long_name': '', 'standard_name': 'number of total radiance scans acquired for SWIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans': {'attributes': {'long_name': '', 'standard_name': 'number of valid radiance scans used in average of VNIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans_SWIR': {'attributes': {'long_name': '', 'standard_name': 'number of valid radiance scans used in average of SWIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence', 'discontinuity_VNIR_SWIR'], 'long_name': 'The quality flag field consists of 32 bits. Every bit is related to the absence or presence of a a flag where each bit encodes a flag given in the flag_meanings attribute', 'standard_name': 'quality_flag'}, 'dim': ['series'], 'dtype': 'flag'}, 'reflectance': {'attributes': {'long_name': 'hemispherical-conical reflectance factor', 'standard_name': 'reflectance', 'unc_comps': ['u_rel_random_reflectance', 'u_rel_systematic_reflectance'], 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'std_reflectance': {'attributes': {'long_name': 'standard deviation on hemispherical-conical reflectance factor that is due to the variability in radiance (i.e. not accounting for variability in darks or in irradiance)', 'standard_name': 'standard deviation hemispherical-conical reflectance factor', 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'u_rel_random_reflectance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'series', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'Random relative uncertainty on hemispherical-conical reflectance factor', 'standard_name': 'u_rel_random_reflectance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_reflectance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_reflectance'], 'units': []}, {'dim': 'series', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'Systematic relative uncertainty on hemispherical-conical reflectance factor', 'standard_name': 'u_rel_systematic_reflectance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'W_L1A_IRR': {'acceleration_x_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_x_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'err_corr_systematic_corr_rad_irr_irradiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on irradiance that is correlated with radiance', 'standard_name': 'correlation matrix of systematic error on irradiance, correlated with radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_indep_irradiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on irradiance that is not correlated with radiance', 'standard_name': 'independent correlation matrix of systematic error on irradiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'irradiance': {'attributes': {'long_name': 'downwelling irradiance', 'standard_name': 'irradiance', 'unc_comps': ['u_rel_random_irradiance', 'u_rel_systematic_indep_irradiance', 'u_rel_systematic_corr_rad_irr_irradiance'], 'units': 'mW m^-2 nm^-1'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>}, 'paa_abs': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_abs', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_abs as in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ask': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ask', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ask as in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ref': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ref', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ref in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence'], 'long_name': 'A variable with the standard name of quality_flag contains an indication of assessed quality information of another data variable. The linkage between the data variable and the variable or variables with the standard_name of quality_flag is achieved using the ancillary_variables attribute.', 'standard_name': 'quality_flag'}, 'dim': ['scan'], 'dtype': 'flag'}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'u_rel_random_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on downwelling irradiance', 'standard_name': 'random relative uncertainty on irradiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_corr_rad_irr_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_corr_rad_irr_irradiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on downwelling irradiance that is correlated with radiance', 'standard_name': 'systematic relative uncertainty on irradiance, correlated with radiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_irradiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on downwelling irradiance that is not correlated with radiance', 'standard_name': 'independent systematic relative uncertainty on irradiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'W_L1A_RAD': {'acceleration_x_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_x_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_mean', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_std', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'err_corr_systematic_corr_rad_irr_radiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on radiance that is correlated with irradiance', 'standard_name': 'correlation matrix of systematic error on radiance, correlated with irradiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_indep_radiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on radiance that is not correlated with irradiance', 'standard_name': 'independent correlation matrix of systematic error on radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'paa_abs': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_abs', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_abs as in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ask': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ask', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ask as in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'paa_ref': {'attributes': {'long_name': '', 'preferred_symbol': 'paa_ref', 'reference': 'no idea', 'standard_name': 'pan (pointing azimuth angle) from pt_ref in metadata', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence'], 'long_name': 'A variable with the standard name of quality_flag contains an indication of assessed quality information of another data variable. The linkage between the data variable and the variable or variables with the standard_name of quality_flag is achieved using the ancillary_variables attribute.', 'standard_name': 'quality_flag'}, 'dim': ['scan'], 'dtype': 'flag'}, 'radiance': {'attributes': {'long_name': 'upwelling radiance', 'standard_name': 'radiance', 'unc_comps': ['u_rel_random_radiance', 'u_rel_systematic_indep_radiance', 'u_rel_systematic_corr_rad_irr_radiance'], 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'u_rel_random_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on upwelling radiance', 'standard_name': 'random relative uncertainty on radiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_corr_rad_irr_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_corr_rad_irr_radiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on radiance that is correlated with irradiance', 'standard_name': 'systematic relative uncertainty on radiance, correlated with irradiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_radiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on radiance that is not correlated with irradiance', 'standard_name': 'independent systematic relative uncertainty on radiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'W_L1B_IRR': {'acceleration_x_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_x_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['series'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'err_corr_systematic_corr_rad_irr_irradiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on irradiance that is correlated with radiance', 'standard_name': 'correlation matrix of systematic error on irradiance, correlated with radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_indep_irradiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on irradiance that is not correlated with radiance', 'standard_name': 'independent correlation matrix of systematic error on irradiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'irradiance': {'attributes': {'long_name': 'downwelling irradiance', 'standard_name': 'irradiance', 'unc_comps': ['u_rel_random_irradiance', 'u_rel_systematic_indep_irradiance', 'u_rel_systematic_corr_rad_irr_irradiance'], 'units': 'mW m^-2 nm^-1'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'n_total_scans': {'attributes': {'long_name': '', 'standard_name': 'number of total scans acquired for VNIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_total_scans_SWIR': {'attributes': {'long_name': '', 'standard_name': 'number of total scans acquired for SWIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans': {'attributes': {'long_name': '', 'standard_name': 'number of valid scans used in average of VNIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans_SWIR': {'attributes': {'long_name': '', 'standard_name': 'number of valid scans used in average of SWIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence', 'single_skyradiance_used', 'lu_eq_missing', 'rhof_angle_missing', 'rhof_default', 'temp_variability_irr', 'temp_variability_rad', 'min_nbred', 'min_nbrlu', 'min_nbrlsky', 'def_wind_flag', 'simil_fail'], 'long_name': 'A variable with the standard name of quality_flag contains an indication of assessed quality information of another data variable. The linkage between the data variable and the variable or variables with the standard_name of quality_flag is achieved using the ancillary_variables attribute.', 'standard_name': 'quality_flag'}, 'dim': ['series'], 'dtype': 'flag'}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'std_irradiance': {'attributes': {'long_name': 'standard deviation on downwelling irradiance', 'standard_name': 'standard deviation irradiance', 'units': 'mW m^-2 nm^-1'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'u_rel_random_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'series', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on downwelling irradiance', 'standard_name': 'random relative uncertainty on irradiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_corr_rad_irr_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_corr_rad_irr_irradiance'], 'units': []}, {'dim': 'series', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on downwelling irradiance that is correlated with radiance', 'standard_name': 'systematic relative uncertainty on irradiance, correlated with radiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_irradiance'], 'units': []}, {'dim': 'series', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on downwelling irradiance that is not correlated with radiance', 'standard_name': 'independent systematic relative uncertainty on irradiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'W_L1B_RAD': {'acceleration_x_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_x_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_x_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_y_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_y_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_mean': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_mean', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acceleration_z_std': {'attributes': {'long_name': '', 'standard_name': 'acceleration_z_std', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['series'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'err_corr_systematic_corr_rad_irr_radiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on radianc that is correlated with irradiancee', 'standard_name': 'correlation matrix of systematic error on radiance, correlated with irradiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_indep_radiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on radiance that is not correlated with irradiance', 'standard_name': 'independent correlation matrix of systematic error on radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'n_total_scans': {'attributes': {'long_name': '', 'standard_name': 'number of total scans acquired for VNIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_total_scans_SWIR': {'attributes': {'long_name': '', 'standard_name': 'number of total scans acquired for SWIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans': {'attributes': {'long_name': '', 'standard_name': 'number of valid scans used in average of VNIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans_SWIR': {'attributes': {'long_name': '', 'standard_name': 'number of valid scans used in average of SWIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence', 'single_skyradiance_used', 'lu_eq_missing', 'rhof_angle_missing', 'rhof_default', 'temp_variability_irr', 'temp_variability_rad', 'min_nbred', 'min_nbrlu', 'min_nbrlsky', 'def_wind_flag', 'simil_fail'], 'long_name': 'A variable with the standard name of quality_flag contains an indication of assessed quality information of another data variable. The linkage between the data variable and the variable or variables with the standard_name of quality_flag is achieved using the ancillary_variables attribute.', 'standard_name': 'quality_flag'}, 'dim': ['series'], 'dtype': 'flag'}, 'radiance': {'attributes': {'long_name': 'upwelling radiance', 'standard_name': 'radiance', 'unc_comps': ['u_rel_random_radiance', 'u_rel_systematic_indep_radiance', 'u_rel_systematic_corr_rad_irr_radiance'], 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'std_radiance': {'attributes': {'long_name': 'standard deviation on upwelling radiance', 'standard_name': 'standard deviation radiance', 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'u_rel_random_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'series', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on upwelling radiance', 'standard_name': 'random relative uncertainty on radiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_corr_rad_irr_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_corr_rad_irr_radiance'], 'units': []}, {'dim': 'series', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on upwelling radiance that is correlated with irradiance', 'standard_name': 'systematic relative uncertainty on radiance, correlated with irradiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_radiance'], 'units': []}, {'dim': 'series', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on upwelling radiance that is not correlated with irradiance', 'standard_name': 'independent systematic relative uncertainty on radiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'W_L1C': {'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'downwelling_radiance': {'attributes': {'long_name': 'downwelling radiance', 'standard_name': 'radiance', 'unc_comps': ['u_rel_random_downwelling_radiance', 'u_rel_systematic_indep_downwelling_radiance', 'u_rel_systematic_corr_rad_irr_downwelling_radiance'], 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>}, 'epsilon': {'attributes': {'long_name': 'Similarity spectrum ratio at two wavelengths see Ruddick et al. (2016)', 'reference': '', 'standard_name': 'epsilon', 'unc_comps': ['u_rel_random_epsilon', 'u_rel_systematic_epsilon'], 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>}, 'err_corr_systematic_corr_rad_irr_downwelling_radiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on radiance that is correlated with irradiance', 'standard_name': 'correlation matrix of systematic error on radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_corr_rad_irr_irradiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on irradiance that is correlated with radiance', 'standard_name': 'correlation matrix of systematic error on irradiance, correlated with radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_corr_rad_irr_upwelling_radiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on radiance that is correlated with irradiance', 'standard_name': 'correlation matrix of systematic error on radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_corr_rad_irr_water_leaving_radiance': {'attributes': {'long_name': 'Error-correlation matrix of water leaving radiance Systematic uncertainty component that is correlated with uncertainties on irradianceleaving radiance uncertainty', 'standard_name': 'err_corr_systematic_corr_rad_irr_water_leaving_radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_indep_downwelling_radiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on radiance that is not correlated with irradiance', 'standard_name': 'independent correlation matrix of systematic error on radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_indep_irradiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on irradiance that is not correlated with radiance', 'standard_name': 'independent correlation matrix of systematic error on irradiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_indep_upwelling_radiance': {'attributes': {'long_name': 'Error-correlation matrix between wavelengths for the systematic error component on radiance that is not correlated with irradiance', 'standard_name': 'independent correlation matrix of systematic error on radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_indep_water_leaving_radiance': {'attributes': {'long_name': 'Error-correlation matrix of water leaving radiance Systematic uncertainty component that is independent with uncertainties on irradianceleaving radiance uncertainty', 'standard_name': 'err_corr_systematic_indep_water_leaving_radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_reflectance': {'attributes': {'long_name': 'Error-correlation matrix of systematic water leaving reflectance uncertainty', 'standard_name': 'err_corr_systematic_reflectance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_reflectance_nosc': {'attributes': {'long_name': 'Error-correlation matrix of systematic water leaving reflectance not corrected for NIR similarity spectrum uncertainty', 'standard_name': 'err_corr_systematic_reflectance_nosc', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'irradiance': {'attributes': {'long_name': 'downwelling irradiance', 'standard_name': 'irradiance', 'unc_comps': ['u_rel_random_irradiance', 'u_rel_systematic_indep_irradiance', 'u_rel_systematic_corr_rad_irr_irradiance'], 'units': 'mW m^-2 nm^-1'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence', 'single_skyradiance_used', 'lu_eq_missing', 'rhof_angle_missing', 'rhof_default', 'temp_variability_irr', 'temp_variability_rad', 'min_nbred', 'min_nbrlu', 'min_nbrlsky', 'def_wind_flag', 'simil_fail'], 'long_name': 'A variable with the standard name of quality_flag contains an indication of assessed quality information of another data variable. The linkage between the data variable and the variable or variables with the standard_name of quality_flag is achieved using the ancillary_variables attribute.', 'standard_name': 'quality_flag'}, 'dim': ['scan'], 'dtype': 'flag'}, 'reflectance': {'attributes': {'long_name': "The surface called 'surface' means the lower boundary of the atmosphere. Upwelling radiation is radiation from below. It does not mean 'net upward''. The sign convention is that 'upwelling' is positive upwards and 'downwelling' is positive downwards. Radiance is the radiative flux in a particular direction, per unit of solid angle. The direction towards which it is going must be specified, for instance with a coordinate of zenith_angle. ", 'preferred_symbol': 'rhow', 'reference': 'SYSTEM_HEIGHT_DEPLOYEMENT', 'standard_name': 'surface_upwelling_radiance_per_unit_wavelength_in_air_reflected_by_water', 'unc_comps': ['u_rel_random_reflectance', 'u_rel_systematic_reflectance'], 'units': '-'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>}, 'reflectance_nosc': {'attributes': {'long_name': 'Reflectance of the water column at the surface without correction for the NIR similarity spectrum (see Ruddick et al., 2006)', 'preferred_symbol': 'rhow_nosc', 'standard_name': 'reflectance_nosc', 'unc_comps': ['u_rel_random_reflectance_nosc', 'u_rel_systematic_reflectance_nosc'], 'units': '-'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>}, 'rhof': {'attributes': {'long_name': 'Fraction of downwelling sky radiance reflected at the air-water interface', 'preferred_symbol': 'rhof', 'reference': 'SYSTEM_HEIGHT_DEPLOYEMENT', 'standard_name': 'air_water_int_radiance_ratio', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>}, 'rhof_raa': {'attributes': {'long_name': 'Relative azimuth angle from sun to sensor (0° when sun and sensor are aligned 180° when the sensor is looking into the sunglint) used for the retrieval of the fraction of downwelling sky radiance reflected at the air-water interface', 'reference': 'SYSTEM_HEIGHT_DEPLOYEMENT', 'standard_name': 'rhof_relative_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'rhof_sza': {'attributes': {'long_name': 'Solar zenith angle used for the retrieval of the fraction of downwelling sky radiance reflected at the air-water interface', 'reference': 'SYSTEM_HEIGHT_DEPLOYEMENT', 'standard_name': 'rhof_solar_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>}, 'rhof_vza': {'attributes': {'long_name': 'Sensor zenith angle used for the retrieval of the fraction of downwelling sky radiance reflected at the air-water interface', 'reference': '', 'standard_name': 'rhof_sensor_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'rhof_wind': {'attributes': {'long_name': 'Surface wind speed used for the retrieval of the fraction of downwelling sky radiance reflected at the air-water interface', 'reference': 'SYSTEM_HEIGHT_DEPLOYEMENT', 'standard_name': 'rhof_wind', 'units': 'ms^-1'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['scan'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'std_downwelling_radiance': {'attributes': {'long_name': 'standard deviation for downwelling radiance', 'standard_name': 'std radiance', 'unc_comps': ['u_rel_random_downwelling_radiance', 'u_rel_systematic_indep_downwelling_radiance', 'u_rel_systematic_corr_rad_irr_downwelling_radiance'], 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>}, 'std_irradiance': {'attributes': {'long_name': 'standard deviation for downwelling irradiance', 'standard_name': 'std irradiance', 'unc_comps': ['u_rel_random_irradiance', 'u_rel_systematic_indep_irradiance', 'u_rel_systematic_corr_rad_irr_irradiance'], 'units': 'mW m^-2 nm^-1'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>}, 'u_rel_random_downwelling_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on upwelling radiance', 'standard_name': 'random relative uncertainty on radiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_random_epsilon': {'attributes': {'err_corr': [{'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'Random epsilon relative uncertainty', 'standard_name': 'u_rel_random_epsilon', 'units': '%'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_random_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on downwelling irradiance', 'standard_name': 'random relative uncertainty on irradiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_random_reflectance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'Random water leaving reflectance relative uncertainty', 'standard_name': 'u_rel_random_reflectance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_random_reflectance_nosc': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'Random water leaving reflectance not corrected for NIR similarity spectrum relative uncertainty', 'standard_name': 'u_rel_random_reflectance_nosc', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_random_upwelling_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'random relative uncertainty on upwelling radiance', 'standard_name': 'random relative uncertainty on radiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_random_water_leaving_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'Random normalized water leaving radiance relative uncertainty', 'standard_name': 'u_rel_random_normalized_water_leaving_radiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_corr_rad_irr_downwelling_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_corr_rad_irr_downwelling_radiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on upwelling radiance that is correlated with irradiance', 'standard_name': 'systematic relative uncertainty on radiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_corr_rad_irr_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_corr_rad_irr_irradiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on downwelling irradiance that is correlated with radiance', 'standard_name': 'systematic relative uncertainty on irradiance, correlated with radiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_corr_rad_irr_upwelling_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_corr_rad_irr_upwelling_radiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on upwelling radiance that is correlated with irradiance', 'standard_name': 'systematic relative uncertainty on radiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_corr_rad_irr_water_leaving_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_water_leaving_radiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'water leaving radiance Systematic uncertainty component that is correlated with uncertainties on irradiance relative uncertainty', 'standard_name': 'u_rel_systematic_corr_rad_irr_water_leaving_radiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_epsilon': {'attributes': {'err_corr': [{'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'Systematic epsilon relative uncertainty', 'standard_name': 'u_rel_systematic_epsilon', 'units': '%'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_downwelling_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_downwelling_radiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on upwelling radiance that is not correlated with irradiance', 'standard_name': 'independent systematic relative uncertainty on radiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_irradiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_irradiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on downwelling irradiance that is not correlated with radiance', 'standard_name': 'independent systematic relative uncertainty on irradiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_upwelling_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_upwelling_radiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'the systematic relative uncertainty component on upwelling radiance that is not correlated with irradiance', 'standard_name': 'independent systematic relative uncertainty on radiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_water_leaving_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_water_leaving_radiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'water leaving radiance Systematic uncertainty component that is independent with uncertainties on irradiance relative uncertainty', 'standard_name': 'u_rel_systematic_indep_water_leaving_radiance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_reflectance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_reflectance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'Systematic water leaving reflectance relative uncertainty', 'standard_name': 'u_rel_systematic_reflectance', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_reflectance_nosc': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_reflectance_nosc'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'Systematic water leaving reflectance not corrected for NIR similarity spectrum relative uncertainty', 'standard_name': 'u_rel_systematic_water_leaving_reflectance_nosc', 'units': '%'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'upwelling_radiance': {'attributes': {'long_name': 'upwelling radiance', 'standard_name': 'upwelling_radiance', 'unc_comps': ['u_rel_random_upwelling_radiance', 'u_rel_systematic_indep_upwelling_radiance', 'u_rel_systematic_corr_rad_irr_upwelling_radiance'], 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['scan'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'water_leaving_radiance': {'attributes': {'long_name': 'water-leaving radiance of electromagnetic radiation (unspecified single wavelength) from the water body by cosine-collector radiometer', 'preferred_symbol': 'lw', 'reference': '', 'standard_name': 'water_leaving_radiance', 'unc_comps': ['u_rel_random_water_leaving_radiance', 'u_rel_systematic_indep_water_leaving_radiance', 'u_rel_systematic_corr_rad_irr_water_leaving_radiance'], 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['wavelength', 'scan'], 'dtype': <class 'numpy.float32'>}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'W_L2A': {'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['series'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'epsilon': {'attributes': {'long_name': 'Similarity spectrum ratio at two wavelengths see Ruddick et al. (2016)', 'reference': '', 'standard_name': 'epsilon', 'unc_comps': ['u_rel_random_epsilon', 'u_rel_systematic_epsilon'], 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>}, 'err_corr_systematic_corr_rad_irr_water_leaving_radiance': {'attributes': {'long_name': 'Error-correlation matrix of water leaving radiance Systematic uncertainty component that is correlated with uncertainties on irradianceleaving radiance uncertainty', 'standard_name': 'err_corr_systematic_corr_rad_irr_water_leaving_radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_indep_water_leaving_radiance': {'attributes': {'long_name': 'Error-correlation matrix of water leaving radiance Systematic uncertainty component that is independent with uncertainties on irradianceleaving radiance uncertainty', 'standard_name': 'err_corr_systematic_indep_water_leaving_radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_reflectance': {'attributes': {'long_name': 'Error-correlation matrix of systematic water leaving reflectance uncertainty', 'standard_name': 'err_corr_systematic_reflectance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_reflectance_nosc': {'attributes': {'long_name': 'Error-correlation matrix of systematic water leaving reflectance not corrected for NIR similarity spectrum uncertainty', 'standard_name': 'err_corr_systematic_reflectance_nosc', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'n_total_scans': {'attributes': {'long_name': '', 'standard_name': 'number of total radiance scans acquired', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans': {'attributes': {'long_name': '', 'standard_name': 'number of valid radiance scans used in average of VNIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence', 'single_skyradiance_used', 'lu_eq_missing', 'rhof_angle_missing', 'rhof_default', 'temp_variability_irr', 'temp_variability_rad', 'min_nbred', 'min_nbrlu', 'min_nbrlsky', 'def_wind_flag', 'simil_fail'], 'long_name': 'A variable with the standard name of quality_flag contains an indication of assessed quality information of another data variable. The linkage between the data variable and the variable or variables with the standard_name of quality_flag is achieved using the ancillary_variables attribute.', 'standard_name': 'quality_flag'}, 'dim': ['series'], 'dtype': 'flag'}, 'reflectance': {'attributes': {'long_name': "The surface called 'surface' means the lower boundary of the atmosphere. Upwelling radiation is radiation from below. It does not mean 'net upward''. The sign convention is that 'upwelling' is positive upwards and 'downwelling' is positive downwards. Radiance is the radiative flux in a particular direction, per unit of solid angle. The direction towards which it is going must be specified, for instance with a coordinate of zenith_angle. ", 'preferred_symbol': 'rhow', 'reference': 'SYSTEM_HEIGHT_DEPLOYEMENT', 'standard_name': 'surface_upwelling_radiance_per_unit_wavelength_in_air_reflected_by_water', 'unc_comps': ['u_rel_random_reflectance', 'u_rel_systematic_reflectance'], 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'reflectance_nosc': {'attributes': {'long_name': 'Reflectance of the water column at the surface without correction for the NIR similarity spectrum (see Ruddick et al., 2006)', 'preferred_symbol': 'rhow_nosc', 'standard_name': 'reflectance_nosc', 'unc_comps': ['u_rel_random_reflectance_nosc', 'u_rel_systematic_reflectance_nosc'], 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'rhof': {'attributes': {'long_name': 'Fraction of downwelling sky radiance reflected at the air-water interface', 'preferred_symbol': 'rhof', 'reference': 'SYSTEM_HEIGHT_DEPLOYEMENT', 'standard_name': 'air_water_int_radiance_ratio', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'std_epsilon': {'attributes': {'long_name': 'standard deviation on Similarity spectrum ratio at two wavelengths see Ruddick et al. (2016)  that is due to the variability in radiance (i.e. not accounting for variability in darks or in irradiance)', 'standard_name': 'standard deviation epsilon', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>}, 'std_reflectance': {'attributes': {'long_name': 'standard deviation on reflectance that is due to the variability in radiance (i.e. not accounting for variability in darks or in irradiance)', 'standard_name': 'standard deviation reflectance', 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'std_reflectance_nosc': {'attributes': {'long_name': 'standard deviation on Reflectance of the water column at the surface without correction for the NIR that is due to the variability in radiance (i.e. not accounting for variability in darks or in irradiance) ', 'standard_name': 'standard deviation reflectance_nosc', 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'std_water_leaving_radiance': {'attributes': {'long_name': 'standard deviation on water leaving radiance that is due to the variability in radiance (i.e. not accounting for variability in darks or in irradiance)', 'standard_name': 'standard deviation water_leaving_radiance', 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'u_rel_random_epsilon': {'attributes': {'err_corr': [{'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'Random epsilon relative uncertainty', 'standard_name': 'u_rel_random_epsilon', 'units': '%'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_random_reflectance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'Random water leaving reflectance relative uncertainty', 'standard_name': 'u_rel_random_reflectance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_random_reflectance_nosc': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'Random water leaving reflectance not corrected for NIR similarity spectrum relative uncertainty', 'standard_name': 'u_rel_random_reflectance_nosc', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_random_water_leaving_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'Random normalized water leaving radiance relative uncertainty', 'standard_name': 'u_rel_random_normalized_water_leaving_radiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_corr_rad_irr_water_leaving_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_water_leaving_radiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'water leaving radiance Systematic uncertainty component that is correlated with uncertainties on irradiance relative uncertainty', 'standard_name': 'u_rel_systematic_corr_rad_irr_water_leaving_radiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_epsilon': {'attributes': {'err_corr': [{'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'Systematic epsilon relative uncertainty', 'standard_name': 'u_rel_systematic_epsilon', 'units': '%'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_water_leaving_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_water_leaving_radiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'water leaving radiance Systematic uncertainty component that is independent with uncertainties on irradiance relative uncertainty', 'standard_name': 'u_rel_systematic_indep_water_leaving_radiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_reflectance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_reflectance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'Systematic water leaving reflectance relative uncertainty', 'standard_name': 'u_rel_systematic_reflectance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_reflectance_nosc': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_reflectance_nosc'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'Systematic water leaving reflectance not corrected for NIR similarity spectrum relative uncertainty', 'standard_name': 'u_rel_systematic_water_leaving_reflectance_nosc', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'water_leaving_radiance': {'attributes': {'long_name': 'water-leaving radiance of electromagnetic radiation (unspecified single wavelength) from the water body by cosine-collector radiometer', 'preferred_symbol': 'lw', 'reference': '', 'standard_name': 'water_leaving_radiance', 'unc_comps': ['u_rel_random_water_leaving_radiance', 'u_rel_systematic_indep_water_leaving_radiance', 'u_rel_systematic_corr_rad_irr_water_leaving_radiance'], 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}, 'W_L2B': {'acquisition_time': {'attributes': {'long_name': 'Acquisition time in seconds since 1970-01-01 00:00:00', 'standard_name': 'time', 'units': 's'}, 'dim': ['series'], 'dtype': <class 'numpy.uint32'>}, 'bandwidth': {'attributes': {'long_name': 'bandwidth FWHM assuming a Gaussian SRF', 'preferred_symbol': 'FWHM', 'standard_name': 'bandwidth', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.1}}, 'epsilon': {'attributes': {'long_name': 'Similarity spectrum ratio at two wavelengths see Ruddick et al. (2016)', 'reference': '', 'standard_name': 'epsilon', 'unc_comps': ['u_rel_random_epsilon', 'u_rel_systematic_epsilon'], 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>}, 'err_corr_systematic_corr_rad_irr_water_leaving_radiance': {'attributes': {'long_name': 'Error-correlation matrix of water leaving radiance Systematic uncertainty component that is correlated with uncertainties on irradianceleaving radiance uncertainty', 'standard_name': 'err_corr_systematic_corr_rad_irr_water_leaving_radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_indep_water_leaving_radiance': {'attributes': {'long_name': 'Error-correlation matrix of water leaving radiance Systematic uncertainty component that is independent with uncertainties on irradianceleaving radiance uncertainty', 'standard_name': 'err_corr_systematic_indep_water_leaving_radiance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_reflectance': {'attributes': {'long_name': 'Error-correlation matrix of systematic water leaving reflectance uncertainty', 'standard_name': 'err_corr_systematic_reflectance', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'err_corr_systematic_reflectance_nosc': {'attributes': {'long_name': 'Error-correlation matrix of systematic water leaving reflectance not corrected for NIR similarity spectrum uncertainty', 'standard_name': 'err_corr_systematic_reflectance_nosc', 'units': '-'}, 'dim': ['wavelength', 'wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.int8'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'n_total_scans': {'attributes': {'long_name': '', 'standard_name': 'number of total radiance scans acquired', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'n_valid_scans': {'attributes': {'long_name': '', 'standard_name': 'number of valid radiance scans used in average of VNIR', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint8'>}, 'pointing_azimuth_angle': {'attributes': {'long_name': 'pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'pointing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'quality_flag': {'attributes': {'flag_meanings': ['lon_default', 'lat_default', 'pt_ref_invalid', 'bad_pointing', 'outliers', 'L0_threshold', 'L0_discontinuity', 'dark_masked', 'half_of_scans_masked', 'not_enough_dark_scans', 'not_enough_rad_scans', 'not_enough_irr_scans', 'series_missing', 'vza_irradiance', 'no_clear_sky_irradiance', 'variable_irradiance', 'half_of_uncertainties_too_big', 'single_irradiance_used', 'no_clear_sky_sequence', 'discontinuity_VNIR_SWIR'], 'long_name': 'The quality flag field consists of 32 bits. Every bit is related to the absence or presence of a a flag where each bit encodes a flag given in the flag_meanings attribute', 'standard_name': 'quality_flag'}, 'dim': ['series'], 'dtype': 'flag'}, 'reflectance': {'attributes': {'long_name': "The surface called 'surface' means the lower boundary of the atmosphere. Upwelling radiation is radiation from below. It does not mean 'net upward''. The sign convention is that 'upwelling' is positive upwards and 'downwelling' is positive downwards. Radiance is the radiative flux in a particular direction, per unit of solid angle. The direction towards which it is going must be specified, for instance with a coordinate of zenith_angle. ", 'preferred_symbol': 'rhow', 'reference': 'SYSTEM_HEIGHT_DEPLOYEMENT', 'standard_name': 'surface_upwelling_radiance_per_unit_wavelength_in_air_reflected_by_water', 'unc_comps': ['u_rel_random_reflectance', 'u_rel_systematic_reflectance'], 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'reflectance_nosc': {'attributes': {'long_name': 'Reflectance of the water column at the surface without correction for the NIR similarity spectrum (see Ruddick et al., 2006)', 'preferred_symbol': 'rhow_nosc', 'standard_name': 'reflectance_nosc', 'unc_comps': ['u_rel_random_reflectance_nosc', 'u_rel_systematic_reflectance_nosc'], 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'rhof': {'attributes': {'long_name': 'Fraction of downwelling sky radiance reflected at the air-water interface', 'preferred_symbol': 'rhof', 'reference': 'SYSTEM_HEIGHT_DEPLOYEMENT', 'standard_name': 'air_water_int_radiance_ratio', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>}, 'series_id': {'attributes': {'long_name': 'Series id number', 'standard_name': 'series_id', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.uint16'>}, 'solar_azimuth_angle': {'attributes': {'long_name': 'solar_azimuth_angle is the horizontal angle between the line of sight to the sun and True North. The angle is measured clockwise.', 'preferred_symbol': 'saa', 'reference': 'True North', 'standard_name': 'solar_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'solar_zenith_angle': {'attributes': {'long_name': 'solar_zenith_angle is the the angle between the line of sight to the sun and the local zenith. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'sza', 'standard_name': 'solar_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'std_epsilon': {'attributes': {'long_name': 'standard deviation on Similarity spectrum ratio at two wavelengths see Ruddick et al. (2016)  that is due to the variability in radiance (i.e. not accounting for variability in darks or in irradiance)', 'standard_name': 'standard deviation epsilon', 'units': '-'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>}, 'std_reflectance': {'attributes': {'long_name': 'standard deviation on reflectance that is due to the variability in radiance (i.e. not accounting for variability in darks or in irradiance)', 'standard_name': 'standard deviation reflectance', 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'std_reflectance_nosc': {'attributes': {'long_name': 'standard deviation on Reflectance of the water column at the surface without correction for the NIR that is due to the variability in radiance (i.e. not accounting for variability in darks or in irradiance) ', 'standard_name': 'standard deviation reflectance_nosc', 'units': '-'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'std_water_leaving_radiance': {'attributes': {'long_name': 'standard deviation on water leaving radiance that is due to the variability in radiance (i.e. not accounting for variability in darks or in irradiance)', 'standard_name': 'standard deviation water_leaving_radiance', 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'u_rel_random_epsilon': {'attributes': {'err_corr': [{'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'Random epsilon relative uncertainty', 'standard_name': 'u_rel_random_epsilon', 'units': '%'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_random_reflectance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'Random water leaving reflectance relative uncertainty', 'standard_name': 'u_rel_random_reflectance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_random_reflectance_nosc': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'Random water leaving reflectance not corrected for NIR similarity spectrum relative uncertainty', 'standard_name': 'u_rel_random_reflectance_nosc', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_random_water_leaving_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'random', 'params': [], 'units': []}, {'dim': 'scan', 'form': 'random', 'params': [], 'units': []}], 'long_name': 'Random normalized water leaving radiance relative uncertainty', 'standard_name': 'u_rel_random_normalized_water_leaving_radiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_corr_rad_irr_water_leaving_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_water_leaving_radiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'water leaving radiance Systematic uncertainty component that is correlated with uncertainties on irradiance relative uncertainty', 'standard_name': 'u_rel_systematic_corr_rad_irr_water_leaving_radiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_epsilon': {'attributes': {'err_corr': [{'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'Systematic epsilon relative uncertainty', 'standard_name': 'u_rel_systematic_epsilon', 'units': '%'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_indep_water_leaving_radiance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_indep_water_leaving_radiance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'water leaving radiance Systematic uncertainty component that is independent with uncertainties on irradiance relative uncertainty', 'standard_name': 'u_rel_systematic_indep_water_leaving_radiance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_reflectance': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_reflectance'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'Systematic water leaving reflectance relative uncertainty', 'standard_name': 'u_rel_systematic_reflectance', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'u_rel_systematic_reflectance_nosc': {'attributes': {'err_corr': [{'dim': 'wavelength', 'form': 'err_corr_matrix', 'params': ['err_corr_systematic_reflectance_nosc'], 'units': []}, {'dim': 'scan', 'form': 'systematic', 'params': [], 'units': []}], 'long_name': 'Systematic water leaving reflectance not corrected for NIR similarity spectrum relative uncertainty', 'standard_name': 'u_rel_systematic_water_leaving_reflectance_nosc', 'units': '%'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.01}}, 'viewing_azimuth_angle': {'attributes': {'long_name': 'viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.', 'preferred_symbol': 'vaa', 'reference': 'True North', 'standard_name': 'viewing_azimuth_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0055}}, 'viewing_zenith_angle': {'attributes': {'long_name': 'viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith).', 'preferred_symbol': 'vza', 'standard_name': 'viewing_zenith_angle', 'units': 'degrees'}, 'dim': ['series'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 0.0, 'scale_factor': 0.0028}}, 'water_leaving_radiance': {'attributes': {'long_name': 'water-leaving radiance of electromagnetic radiation (unspecified single wavelength) from the water body by cosine-collector radiometer', 'preferred_symbol': 'lw', 'reference': '', 'standard_name': 'water_leaving_radiance', 'unc_comps': ['u_rel_random_water_leaving_radiance', 'u_rel_systematic_indep_water_leaving_radiance', 'u_rel_systematic_corr_rad_irr_water_leaving_radiance'], 'units': 'mW m^-2 nm^-1 sr^-1'}, 'dim': ['wavelength', 'series'], 'dtype': <class 'numpy.float32'>}, 'wavelength': {'attributes': {'long_name': 'wavelength as determined from lab calibration of HYPSTAR instrument', 'preferred_symbol': 'wv', 'standard_name': 'wavelength', 'units': 'nm'}, 'dim': ['wavelength'], 'dtype': <class 'numpy.float32'>, 'encoding': {'dtype': <class 'numpy.uint16'>, 'offset': 320, 'scale_factor': 0.025}}}}, metadata_defs={'CAL': {'Calibration_device_LED_ID': 'TBD', 'Calibration_device_LED_manufacturer': 'TBD', 'Calibration_device_LED_model': 'TBD', 'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'calibration_device_date_manufacture': 'TBD', 'calibration_device_description': 'TBD', 'calibration_device_documentation_directory': 'TBD', 'calibration_device_id': 'TBD', 'calibration_device_manufacturer': 'Tartu University', 'calibration_device_model': 'TBD', 'calibration_device_version': 'TBD', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'data_created': 'NaN', 'format_version': 'v01.0', 'instrument_date_manufacture': 'TBD', 'instrument_deployment_date': 'TBD', 'instrument_documentation_references': 'TBD', 'instrument_firmware': 'TBD', 'instrument_firmware_version': 'TBD', 'instrument_history': 'TBD', 'instrument_id': 'NaN', 'instrument_manufacturer': 'Tartu University', 'instrument_model': 'HYPSTARv1', 'instrument_version': 'TBD', 'irr_vis_head_ documentation_reference': 'TBD', 'irr_vis_head_description': 'TBD', 'irr_vis_head_firmware_version': 'TBD', 'irr_vis_head_id': 'TBD', 'irr_vis_head_manufacture': 'TBD', 'irr_vis_head_manufacturer': 'TBD', 'irr_vis_head_model': 'TBD', 'irr_vis_head_version': 'TBD', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following [reference to doc]', 'netcdf_version': '1.6', 'operational_status': 'operational', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'rad_vis_head_ documentation_reference': 'TBD', 'rad_vis_head_calibration_documentation_reference': 'TBD', 'rad_vis_head_cosine_documentation_reference': 'TBD', 'rad_vis_head_description': 'custom 25 μm slit width for the VNIR spectral region', 'rad_vis_head_firmware_version': 'v001', 'rad_vis_head_id': 'TBD', 'rad_vis_head_linearity_documentation_reference': 'TBD', 'rad_vis_head_manufacture': '20190120', 'rad_vis_head_manufacturer': 'Ibsen', 'rad_vis_head_model': 'Freedom FSA-101', 'rad_vis_head_radiometric_resolution': '16', 'rad_vis_head_spectral_accuracy': '0.3', 'rad_vis_head_spectral_fov': '7', 'rad_vis_head_spectral_range': '190-1100', 'rad_vis_head_spectral_resolution': '3', 'rad_vis_head_spectral_sampling': '1.5', 'rad_vis_head_version': '101', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'source': 'surface observation', 'system_comment': 'system below bird nest, bad luck', 'system_date_manufacture': 'TBD', 'system_deployment_date': 'TBD', 'system_deployment_height': 'TBD', 'system_documentation_references': 'TBD', 'system_firmaware_version': 'TBD', 'system_manufacturer': "Laboratoire d'Océanographie de Villefranche UMR 7093 - CNRS / Sorbonne Univ", 'system_model': 'TBD', 'system_version': 'TBD', 'type': 'dataset'}, 'L0A_BLA': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'sequence_id': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'type': 'dataset'}, 'L0A_IRR': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'sequence_id': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'type': 'dataset'}, 'L0A_RAD': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'sequence_id': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'type': 'dataset'}, 'L0B_IRR': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'sequence_id': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'type': 'dataset'}, 'L0B_RAD': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'sequence_id': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'type': 'dataset'}, 'L_L1A_IRR': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'creator_email': 'pieter.de.vis@npl.cp.uk', 'creator_name': 'Pieter De Vis', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_calibration_file_irr': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'keyword': 'Environmental monitoring Facilities (INSPIRE Spatial Data Theme), reflectance (http://aims.fao.org/aos/agrovoc/c_28538), optical properties(http://aims.fao.org/aos/agrovoc/c_5371), vegetation (http://www.eionet.europa.eu/gemet/concept/8922)', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'locator': 'https://github.com/HYPERNETS/hypernets_processor/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'responsible_party': 'National Physical Laboratory (NPL), UK', 'sequence_id': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'title': 'HYPSTAR dataset of irradiance', 'topic_category': 'land, environment, geoscientific information', 'type': 'dataset'}, 'L_L1A_RAD': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'creator_email': 'pieter.de.vis@npl.cp.uk', 'creator_name': 'Pieter De Vis', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_calibration_file_rad': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'keyword': 'Environmental monitoring Facilities (INSPIRE Spatial Data Theme), reflectance (http://aims.fao.org/aos/agrovoc/c_28538), optical properties(http://aims.fao.org/aos/agrovoc/c_5371), vegetation (http://www.eionet.europa.eu/gemet/concept/8922)', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'locator': 'https://github.com/HYPERNETS/hypernets_processor/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'responsible_party': 'National Physical Laboratory (NPL), UK', 'sequence_id': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'title': 'HYPSTAR dataset of radiance', 'topic_category': 'land, environment, geoscientific information', 'type': 'dataset'}, 'L_L1B_IRR': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'creator_email': 'pieter.de.vis@npl.cp.uk', 'creator_name': 'Pieter De Vis', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_calibration_file_irr': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'keyword': 'Environmental monitoring Facilities (INSPIRE Spatial Data Theme), reflectance (http://aims.fao.org/aos/agrovoc/c_28538), optical properties(http://aims.fao.org/aos/agrovoc/c_5371), vegetation (http://www.eionet.europa.eu/gemet/concept/8922)', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'locator': 'https://github.com/HYPERNETS/hypernets_processor/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'responsible_party': 'National Physical Laboratory (NPL), UK', 'sequence_id': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'title': 'HYPSTAR dataset of irradiance averaged over scans', 'topic_category': 'land, environment, geoscientific information', 'type': 'dataset'}, 'L_L1B_RAD': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'creator_email': 'pieter.de.vis@npl.cp.uk', 'creator_name': 'Pieter De Vis', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_calibration_file_rad': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'keyword': 'Environmental monitoring Facilities (INSPIRE Spatial Data Theme), reflectance (http://aims.fao.org/aos/agrovoc/c_28538), optical properties(http://aims.fao.org/aos/agrovoc/c_5371), vegetation (http://www.eionet.europa.eu/gemet/concept/8922)', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'locator': 'https://github.com/HYPERNETS/hypernets_processor/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'responsible_party': 'National Physical Laboratory (NPL), UK', 'sequence_id': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'title': 'HYPSTAR dataset of radiance averaged over scans', 'topic_category': 'land, environment, geoscientific information', 'type': 'dataset'}, 'L_L1C': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'creator_email': 'pieter.de.vis@npl.cp.uk', 'creator_name': 'Pieter De Vis', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_calibration_file_irr': 'NaN', 'instrument_calibration_file_rad': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'keyword': 'Environmental monitoring Facilities (INSPIRE Spatial Data Theme), reflectance (http://aims.fao.org/aos/agrovoc/c_28538), optical properties(http://aims.fao.org/aos/agrovoc/c_5371), vegetation (http://www.eionet.europa.eu/gemet/concept/8922)', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'locator': 'https://github.com/HYPERNETS/hypernets_processor/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'responsible_party': 'National Physical Laboratory (NPL), UK', 'sequence_id': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'title': 'HYPSTAR Land network dataset of radiance and irradiance', 'topic_category': 'land, environment, geoscientific information', 'type': 'dataset'}, 'L_L2A': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'creator_email': 'pieter.de.vis@npl.cp.uk', 'creator_name': 'Pieter De Vis', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_calibration_file_irr': 'NaN', 'instrument_calibration_file_rad': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'keyword': 'Environmental monitoring Facilities (INSPIRE Spatial Data Theme), reflectance (http://aims.fao.org/aos/agrovoc/c_28538), optical properties(http://aims.fao.org/aos/agrovoc/c_5371), vegetation (http://www.eionet.europa.eu/gemet/concept/8922)', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'locator': 'https://github.com/HYPERNETS/hypernets_processor/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'responsible_party': 'National Physical Laboratory (NPL), UK', 'sequence_id': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'title': 'HYPSTAR Land network dataset of spectral surface reflectance', 'topic_category': 'land, environment, geoscientific information', 'type': 'dataset'}, 'L_L2B': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'creator_email': 'pieter.de.vis@npl.cp.uk', 'creator_name': 'Pieter De Vis', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_calibration_file_irr': 'NaN', 'instrument_calibration_file_rad': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'keyword': 'Environmental monitoring Facilities (INSPIRE Spatial Data Theme), reflectance (http://aims.fao.org/aos/agrovoc/c_28538), optical properties(http://aims.fao.org/aos/agrovoc/c_5371), vegetation (http://www.eionet.europa.eu/gemet/concept/8922)', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'locator': 'https://github.com/HYPERNETS/hypernets_processor/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'responsible_party': 'National Physical Laboratory (NPL), UK', 'sequence_id': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'title': 'HYPSTAR Land network dataset of spectral surface reflectance', 'topic_category': 'land, environment, geoscientific information', 'type': 'dataset'}, 'W_L1A_IRR': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'creator_email': 'remsem@naturalsciences.be', 'creator_name': 'Clemence Goyens', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_calibration_file_irr': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'keyword': 'Environmental monitoring Facilities (INSPIRE Spatial Data Theme), reflectance (http://aims.fao.org/aos/agrovoc/c_28538), optical properties(http://aims.fao.org/aos/agrovoc/c_5371),inland waters (http://www.eionet.europa.eu/gemet/concept/4333), sea (http://www.eionet.europa.eu/gemet/concept/7495)', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'locator': 'https://github.com/HYPERNETS/hypernets_processor/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'responsible_party': 'Royal Belgian Institute for Natural Sciences, Directorate Natural Environment, REMSEM', 'sequence_id': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'title': 'HYPSTAR dataset of irradiance', 'topic_category': 'oceans, environment, inland waters, geoscientific information', 'type': 'dataset'}, 'W_L1A_RAD': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'creator_email': 'remsem@naturalsciences.be', 'creator_name': 'Clemence Goyens', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_calibration_file_rad': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'keyword': 'Environmental monitoring Facilities (INSPIRE Spatial Data Theme), reflectance (http://aims.fao.org/aos/agrovoc/c_28538), optical properties(http://aims.fao.org/aos/agrovoc/c_5371),inland waters (http://www.eionet.europa.eu/gemet/concept/4333), sea (http://www.eionet.europa.eu/gemet/concept/7495)', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'locator': 'https://github.com/HYPERNETS/hypernets_processor/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'responsible_party': 'Royal Belgian Institute for Natural Sciences, Directorate Natural Environment, REMSEM', 'sequence_id': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'title': 'HYPSTAR dataset of radiance', 'topic_category': 'oceans, environment, inland waters, geoscientific information', 'type': 'dataset'}, 'W_L1B_IRR': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'creator_email': 'remsem@naturalsciences.be', 'creator_name': 'Clemence Goyens', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_calibration_file_irr': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'keyword': 'Environmental monitoring Facilities (INSPIRE Spatial Data Theme), reflectance (http://aims.fao.org/aos/agrovoc/c_28538), optical properties(http://aims.fao.org/aos/agrovoc/c_5371),inland waters (http://www.eionet.europa.eu/gemet/concept/4333), sea (http://www.eionet.europa.eu/gemet/concept/7495)', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'locator': 'https://github.com/HYPERNETS/hypernets_processor/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'responsible_party': 'Royal Belgian Institute for Natural Sciences, Directorate Natural Environment, REMSEM', 'sequence_id': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'title': 'HYPSTAR dataset of irradiance averaged over scans', 'topic_category': 'oceans, environment, inland waters, geoscientific information', 'type': 'dataset'}, 'W_L1B_RAD': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'creator_email': 'remsem@naturalsciences.be', 'creator_name': 'Clemence Goyens', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_calibration_file_rad': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'keyword': 'Environmental monitoring Facilities (INSPIRE Spatial Data Theme), reflectance (http://aims.fao.org/aos/agrovoc/c_28538), optical properties(http://aims.fao.org/aos/agrovoc/c_5371),inland waters (http://www.eionet.europa.eu/gemet/concept/4333), sea (http://www.eionet.europa.eu/gemet/concept/7495)', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'locator': 'https://github.com/HYPERNETS/hypernets_processor/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'responsible_party': 'Royal Belgian Institute for Natural Sciences, Directorate Natural Environment, REMSEM', 'sequence_id': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'title': 'HYPSTAR dataset of radiance averaged over scans', 'topic_category': 'oceans, environment, inland waters, geoscientific information', 'type': 'dataset'}, 'W_L1C': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'creator_email': 'remsem@naturalsciences.be', 'creator_name': 'Clemence Goyens', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_calibration_file_irr': 'NaN', 'instrument_calibration_file_rad': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'keyword': 'Environmental monitoring Facilities (INSPIRE Spatial Data Theme), reflectance (http://aims.fao.org/aos/agrovoc/c_28538), optical properties(http://aims.fao.org/aos/agrovoc/c_5371),inland waters (http://www.eionet.europa.eu/gemet/concept/4333), sea (http://www.eionet.europa.eu/gemet/concept/7495)', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'locator': 'https://github.com/HYPERNETS/hypernets_processor/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'responsible_party': 'Royal Belgian Institute for Natural Sciences, Directorate Natural Environment, REMSEM', 'rhof_option': 'NaN', 'rhof_wind_source': 'NaN', 'sequence_id': 'NaN', 'similarity_alpha': 'NaN', 'similarity_wavelen1': 'NaN', 'similarity_wavelen2': 'NaN', 'similarity_waveref': 'NaN', 'similarity_wavethres': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'title': 'HYPSTAR Water network dataset of downwelling irradiance, upwelling and downwelling radiance and water leaving radiance', 'topic_category': 'oceans, environment, inland waters, geoscientific information', 'type': 'dataset'}, 'W_L2A': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'creator_email': 'remsem@naturalsciences.be', 'creator_name': 'Clemence Goyens', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_calibration_file_irr': 'NaN', 'instrument_calibration_file_rad': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'keyword': 'Environmental monitoring Facilities (INSPIRE Spatial Data Theme), reflectance (http://aims.fao.org/aos/agrovoc/c_28538), optical properties(http://aims.fao.org/aos/agrovoc/c_5371),inland waters (http://www.eionet.europa.eu/gemet/concept/4333), sea (http://www.eionet.europa.eu/gemet/concept/7495)', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'locator': 'https://github.com/HYPERNETS/hypernets_processor/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'responsible_party': 'Royal Belgian Institute for Natural Sciences, Directorate Natural Environment, REMSEM', 'rhof_option': 'NaN', 'sequence_id': 'NaN', 'similarity_alpha': 'NaN', 'similarity_wavelen1': 'NaN', 'similarity_wavelen2': 'NaN', 'similarity_waveref': 'NaN', 'similarity_wavethres': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'title': 'HYPSTAR Water network dataset of spectral surface reflectance', 'topic_category': 'oceans, environment, inland waters, geoscientific information', 'type': 'dataset'}, 'W_L2B': {'abstract': 'The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).', 'acknowledgement': 'HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.', 'comment': 'Any free-format text is appropriate.', 'conformity': 'notEvaluated', 'conventions': 'CFv72, NVS2.0', 'creator_email': 'remsem@naturalsciences.be', 'creator_name': 'Clemence Goyens', 'data_created': 'NaN', 'easting': 'longitude', 'format_version': 'v2.0', 'history': 'TBD', 'illuminance': 'NaN', 'instrument_calibration_file_irr': 'NaN', 'instrument_calibration_file_rad': 'NaN', 'instrument_configuration_file': 'TBD', 'instrument_id': 'TBD', 'keyword': 'Environmental monitoring Facilities (INSPIRE Spatial Data Theme), reflectance (http://aims.fao.org/aos/agrovoc/c_28538), optical properties(http://aims.fao.org/aos/agrovoc/c_5371),inland waters (http://www.eionet.europa.eu/gemet/concept/4333), sea (http://www.eionet.europa.eu/gemet/concept/7495)', 'language': 'English', 'licence': 'Attribution-NonCommercial-NoDerivs CC BY-NC-ND', 'limitations': 'no limitations to public access', 'lineage': 'Quality assured following www.hypernets.eu/docs/QC/', 'locator': 'https://github.com/HYPERNETS/hypernets_processor/', 'netcdf_version': '1.6', 'northing': 'latitude', 'operational_status': 'operational', 'processor_atbd': 'https://hypernets-processor.readthedocs.io/en/latest/', 'processor_configuration_file': 'NaN', 'processor_name': 'hypernets_processor', 'processor_version': 'NaN', 'product_name': 'NaN', 'project_name': 'H2020 HYPERNETS GN 775993', 'references': 'https://hypernets-processor.readthedocs.io/en/latest/', 'responsible_party': 'Royal Belgian Institute for Natural Sciences, Directorate Natural Environment, REMSEM', 'rhof_option': 'NaN', 'sequence_id': 'NaN', 'similarity_alpha': 'NaN', 'similarity_wavelen1': 'NaN', 'similarity_wavelen2': 'NaN', 'similarity_waveref': 'NaN', 'similarity_wavethres': 'NaN', 'site_id': 'NaN', 'site_latitude': 'NaN', 'site_longitude': 'NaN', 'source': 'surface observation', 'source_file': 'NaN', 'system_id': 'NaN', 'system_pressure': 'NaN', 'system_relative_humidity': 'NaN', 'system_temperature': 'NaN', 'title': 'HYPSTAR Water network dataset of spectral surface reflectance', 'topic_category': 'oceans, environment, inland waters, geoscientific information', 'type': 'dataset'}})

Bases: object

Class to generate xarray Datasets in the Hypernets file format specification, handling the library of defined file formats, metadata database and interfacing with the TemplateUtil tool.

Parameters:

• context (hypernets_processor.context.Context) – processor state defining context object

• variables_dict_defs (dict) – dictionary of variables_dict for each product format (default is Hypernets formats)

• metadata_defs (dict) – dictionary of metadata for each product format (default is Hypernets formats)

create_ds_template(dim_sizes_dict: object, ds_format: object, propagate_ds: object | None = None, swir: object = False, angles: object = False, ds=None) → object

Returns empty Hypernets dataset

Parameters:

• dim_sizes_dict (dict) – entry per dataset dimension with value of size as int

• ds_format (str) – product format string

• propagate_ds (xarray.Dataset) – (optional) template dataset is populated with data from propagate_ds for their variables

with common names and dimensions. Useful for transferring common data between datasets at different processing levels (e.g. times, etc.).

N.B. propagates data only, not variables as a whole with attributes etc.

Returns:

Empty dataset

Return type:

xarray.Dataset

create_empty_dim_sizes_dict(ds_format)

Returns empty dim_size_dict for specified ds format

Parameters:

ds_format (str) – product format string

Returns:

empty dim_size_dict

Return type:

dict

static find_metadata(metadata, db, query)

Populate metadata dictionary with values from database query

Parameters:

• metadata (dict) – dictionary of dataset metadata

• db (dataset.Database) – metadata database

• query (dict/list) – database query, defined as {“table_name”: query_dict} where query_dict defines. Can be a list of

such database queries

return_ds_format_dim_names(ds_format)

Returns dims required for specified ds format

Parameters:

ds_format (str) – product format string

Returns:

ds format dims

Return type:

list

return_ds_format_variable_dict(ds_format, variable_name)

Returns variable definition info for specified ds format

Parameters:

• ds_format (str) – product format string

• variable_name (str) – variable name

Returns:

variable definition info

Return type:

dict

return_ds_format_variable_names(ds_format)

Returns variables for specified ds format

Parameters:

ds_format (str) – product format string

Returns:

ds format variables

Return type:

list

return_ds_formats()

Returns available ds format names

Returns:

ds formats

Return type:

list