Name: Sentinel 5P: Cloud (CLOUD) level 2 data
Published: 2022-11-10T14:19:46
Keywords: Sentinel 5P, Cloud, CLOUD, ESA

Dataset

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Sentinel 5P: Cloud (CLOUD) level 2 data

Permitted Use: Any

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Latest Data Update: 2023-04-19

Status: Ongoing

Online Status: ONLINE

Publication State: Published

Publication Date: 2022-11-10

Download Stats: last 12 months

Dataset Size: 60.83K Files | 12TB

Abstract

This dataset contains data that can be used for cloud correction of satellite trace gas retrievals these include: cloud fraction, cloud optical thickness (albedo), and cloud-top pressure (height).

Sentinel 5 Precursor (S5P) was launched on the 13th of October 2017 carrying the TROPOspheric Monitoring Instrument (TROPOMI). Cloud parameters from TROPOMI are not only used for enhancing the accuracy of trace gas retrievals but also to extend the satellite data record of cloud information derived from oxygen A-band measurements initiated with the Global Ozone Monitoring Experiment (GOME).

The TROPOMI/S5P cloud properties retrieval is based on the Optical Cloud Recognition Algorithm (OCRA) and Retrieval of Cloud Information using Neural Networks (ROCINN) algorithms currently being used in the operational GOME and GOME-2 products. OCRA retrieves the cloud fraction using measurements in the UV/VIS spectral regions and ROCINN retrieves the cloud height (pressure) and optical thickness (albedo) using measurements in and around the oxygen A-band at 760 nm. For TROPOMI/S5P we use OCRA/ROCINN Version 3.0, which is based on a more realistic treatment of clouds as optically uniform layers of light-scattering particles. Additionally, the cloud parameters are also provided for a cloud model which assumes the cloud to be a Lambertian reflecting boundary.

Citable as: Copernicus; European Space Agency (2022): Sentinel 5P: Cloud (CLOUD) level 2 data. NERC EDS Centre for Environmental Data Analysis, date of citation. https://catalogue.ceda.ac.uk/uuid/ba5618b8ad6540c4b16df4877350464c/

Abbreviation: Not defined

Keywords: Sentinel 5P, Cloud, CLOUD, ESA

Details

Previous Info:	No news update for this record
Previously used record identifiers:	No related previous identifiers.
Access rules:	Public data: access to these data is available to both registered and non-registered users. Use of these data is covered by the following licence(s): https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data lineage:	Data collected and prepared by European Space Agency (ESA). Downloaded from the Collaborative Hub for use by CEDA users.
Data Quality:	See dataset associated documentation
File Format:	These data are netCDF format.

Process overview

This dataset was generated by a combination of instruments deployed on platforms and computations as detailed below.

Instrument/Platform pairings

TROPOspheric Monitoring Instrument (TROPOMI)	Deployed on: Sentinel 5 Precursor

Computation Element: 1

Title	Level 2 Cloud processing algorithm applied to Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) raw data
Abstract	This computation involves the Level 2 processing algorithm applied to raw TROPOspheric Monitoring Instrument (TROPOMI) data. Optical Cloud Recognition Algorithm (OCRA) is the S5P_CLOUD_OCRA heritage. In OCRA, optical sensor measurements are divided into two components: a cloud-free background and a remainder expressing the influence of clouds. OCRA was first developed for GOME in the late 1990s, when enough data from the three sub-pixel broad-band PMDs (Polarization Measurement Devices) had accumulated to allow for the construction of the global cloud-free composite which is the key element in the algorithm. Over the course of the 16-year GOME record, the algorithm was refined and the cloud-free composite adjusted as more data became available. OCRA has also been applied to SCIAMACHY and GOME-2. Initial cloud-free composites for these sensors were based on GOME data before dedicated measurements became available from SCIAMACHY and GOME-2. For S5P_CLOUD_OCRA, the initial cloud-free composite will be based on GOME-2 and OMI (see section 5.2). Retrieval of Cloud Information using Neural Networks (ROCINN) is the S5P_CLOUD_ROCINN heritage. ROCINN is based on the comparison of measured and simulated satellite sun-normalized radiances in and near the O2 A-band, and it uses a neural network algorithm to retrieve cloud-top height and cloud-top albedo. ROCINN uses the cloud fraction input from OCRA as one starting point. Early versions of ROCINN used a transmittance model to compute simulated radiances, but the latest versions are based on the use of the VLIDORT radiative transfer scattering model. For GOME and GOME-2, ROCINN Version 2.0 is the current operational algorithm in the GDP [GOME Data Processor]. This version is based on the assumption that clouds are simply Lambertian reflecting surfaces so the two main retrieval products are the cloud-top height and the cloud-top albedo itself. This is the “clouds-as-reflecting-boundaries” (CRB) model; see for example [van Roozendael et al., 2006] for GOME and [Loyola et al., 2011] for GOME 2. Although ROCINN 2.0 is the heritage algorithm, there is an important point of departure for S5P. For TROPOMI/S5P, ROCINN Version 3.0 was initially used, which is based on a more realistic treatment of clouds as optically uniform layers of light-scattering particles (water droplets). This is the “clouds-as-layers" (CAL) model – here, the two main retrieval products are the cloud-top height and the cloud optical thickness. Details of this algorithm prototype may be found in [Schuessler et al., 2014]. Although the CAL model will be the default for S5P, it has been requested that the CRB method should also be retained as an option. CAL is the preferred method for the relatively small TROPOMI/S5P ground pixels (5.5 x 3.5 km2). The CRB approach works best with large pixels such as those from GOME (footprint 320 x 40 km2). [Schuessler et al., 2014] has shown that for the smaller GOME-2 pixels, CAL retrieval produces more reliable cloud information than that from CRB, not only with regard to the accuracy of the cloud parameters themselves but also with regard to the effect of cloud parameter uncertainties on total ozone accuracy. In OCRA, the intensity is regarded as a linear function of the radiometric cloud cover, and in ROCINN, TOA radiances for partially cloudy scenarios are computed using a linearly weighted mean of the clear-sky and fully-cloudy calculations, the weighting factor being the cloud fraction. In the context of this IPA model, the two algorithms are consistent. With the notably smaller pixel size that comes with higher spatial resolution, 3-D cloud radiative effects will become an important consideration in error budgeting for the cloud algorithms. For more information on the processing chain please see the ATBD document.
Input Description	None
Output Description	None
Software Reference	None

Output Description	None

units: m s-1
standard_name: eastward_wind
var_id: eastward_wind
long_name: Eastward wind from ECMWF at 10 meter height level

var_id: instrument_configuration_identifier
long_name: IcID

var_id: instrument_configuration_version
long_name: IcVersion

units: m s-1
standard_name: northward_wind
var_id: northward_wind
long_name: Northward wind from ECMWF at 10 meter height level

units: 1
var_id: number_of_spectral_points_in_retrieval
long_name: Number of spectral points used in the CAL retrieval

units: 1
var_id: number_of_spectral_points_in_retrieval
long_name: Number of spectral points used in the retrieval

units: 1
var_id: processing_quality_flags_crb
long_name: Processing quality flags

units: 1
var_id: viirs_cloud_fraction
standard_name: viirs_cloud_fraction_uvis
long_name: Ratio of VIIRS pixels classified as CONFIDENTLY_CLOUDY in the UV/VIS

units: 1
var_id: viirs_cloud_fraction_nir
standard_name: viirs_cloud_fraction_nir
long_name: Ratio of VIIRS pixels classified as CONFIDENTLY_CLOUDY in the near infrared

units: 1
var_id: covariance_matrix_diagonal
long_name: The diagonal entries of the covariance matrix of the CAL ROCINN retrieval

units: 1
var_id: covariance_matrix_diagonal_crb
long_name: The diagonal entries of the covariance matrix of the CRB ROCINN retrieval

var_id: time_utc
long_name: Time of observation as ISO 8601 date-time string

units: 1
var_id: ground_pixel
long_name: across-track dimension index

units: 1
var_id: scanline
long_name: along-track dimension index

units: 1
var_id: calibration_subwindows_root_mean_square
standard_name: TBA
long_name: calibration rms per subwindow

units: nm
standard_name: TBA
var_id: calibration_subwindows_wavelength
long_name: calibration wavelength center in each subwindow

units: 1
standard_name: cloud_albedo
var_id: cloud_albedo_crb_nir
long_name: cloud albedo from the CRB model

units: 1
var_id: cloud_albedo_crb_precision_nir
standard_name: cloud_albedo_standard_error
long_name: cloud albedo precision from the CRB model

units: m
var_id: cloud_base_height
long_name: cloud base height assumed in ROCINN retrieval

units: m
var_id: cloud_base_height_precision
long_name: cloud base height precision assumed in ROCINN retrieval

units: Pa
var_id: cloud_base_pressure
standard_name: air_pressure_at_cloud_base
long_name: cloud base pressure assumed in ROCINN retrieval

units: Pa
var_id: cloud_base_pressure_precision
standard_name: air_pressure_at_cloud_base standard_error
long_name: cloud base pressure precision assumed in ROCINN retrieval

units: 1
var_id: cloud_coregistration_inhomogeneity_parameter
long_name: cloud coregistration inhomogeneity parameter

units: Pa
standard_name: air_pressure_at_cloud_top
var_id: cloud_top_pressure
long_name: cloud optical centroid top pressure

units: Pa
var_id: cloud_top_pressure_precision
standard_name: air_pressure_at_cloud_top standard_error
long_name: cloud optical centroid top pressure precision

units: 1
standard_name: atmosphere_optical_thickness_due_to_cloud
long_name: cloud optical thickness
var_id: cloud_optical_thickness_nir

units: 1
var_id: cloud_optical_thickness_precision
standard_name: atmosphere_optical_thickness_due_to_cloud standard_error
long_name: cloud optical thickness precision

units: 1
var_id: cloud_optical_thickness_precision
standard_name: atmosphere_optical_thickness_due_to_cloud standard_error
long_name: cloud optical thickness precision coregistered using the OCRA/ROCINN CAL model.

units: 1
var_id: cloud_phase
long_name: cloud phase

units: m
standard_name: TBD
var_id: cloud_height_crb_nir
long_name: cloud radiometric optical centroid height from the CRB model

units: m
standard_name: TBD
var_id: cloud_height_crb_precision_nir
long_name: cloud radiometric optical centroid height precision from the CRB model

units: Pa
standard_name: TBD
var_id: cloud_pressure_crb
long_name: cloud radiometric optical centroid pressure from the CRB model

units: Pa
standard_name: TBD
var_id: cloud_pressure_crb_precision
long_name: cloud radiometric optical centroid pressure precision from the CRB model

units: m
long_name: cloud top height
var_id: cloud_top_height_nir

units: m
var_id: cloud_top_height_precision_nir
long_name: cloud top height precision

units: K
standard_name: air_temperature_at_cloud_top
long_name: cloud top temperature
var_id: cloud_top_temperature

var_id: cloud_histogram

var_id: cloud_pdf

units: 1
standard_name: TBA
var_id: calibration_polynomial_coefficients
long_name: computed coefficients of the polynomial function

units: 1
var_id: coregistration_weight_sums_cal
long_name: coregistration weight sums cal

units: 1
var_id: coregistration_weight_sums_crb
long_name: coregistration weight sums crb

units: 1
var_id: coregistration_weight_sums_ge
long_name: coregistration weight sums ge

units: 1
var_id: coregistration_weight_sums_nir
long_name: coregistration weight sums nir

units: 1
var_id: qa_value_crb
long_name: data quality value

units: 1
var_id: degrees_of_polynomial_shift
long_name: degrees_of_polynomial_shift dimension index

units: 1
var_id: cloud_fraction_nir
long_name: effective radiometric cloud fraction

units: 1
var_id: cloud_fraction_apriori_nir
long_name: effective radiometric cloud fraction a priori

units: 1
standard_name: TBD
var_id: cloud_fraction_crb_nir
long_name: effective radiometric cloud fraction from the CRB model

units: 1
var_id: cloud_fraction_precision_nir
long_name: effective radiometric cloud fraction precision

units: 1
standard_name: TBD
var_id: cloud_fraction_crb_precision_nir
long_name: effective radiometric cloud fraction precision from the CRB model

units: 1
var_id: effective_scene_albedo_nir
standard_name: effective_scene_albedo
long_name: effective scene albedo from the CRB model

units: 1
var_id: effective_scene_albedo_precision_nir
standard_name: effective_scene_albedo_standard_error
long_name: effective scene albedo precision from the CRB model

units: m
standard_name: TBD
var_id: effective_scene_height_nir
long_name: effective scene height from the CRB model

units: m
standard_name: TBD
var_id: effective_scene_height_precision_nir
long_name: effective scene height precision from the CRB model

units: Pa
standard_name: TBD
var_id: effective_scene_pressure
long_name: effective scene optical centroid pressure from the CRB model

units: Pa
standard_name: TBD
var_id: effective_scene_pressure_precision
long_name: effective scene pressure precision from the CRB model

units: 1
var_id: condition_number
long_name: final condition number of the rocinn inversion using the CAL model

units: 1
var_id: condition_number_crb
long_name: final condition number of the rocinn inversion using the CRB model

units: 1
var_id: condition_number_ge_nir
long_name: final condition number of the rocinn inversion using the CRB model for the effective scene

units: 1
var_id: degrees_of_freedom_nir
long_name: final degrees of freedom of the rocinn inversion using the CAL model

units: 1
var_id: degrees_of_freedom_crb_nir
long_name: final degrees of freedom of the rocinn inversion using the CRB model

units: 1
var_id: degrees_of_freedom_ge_nir
long_name: final degrees of freedom of the rocinn inversion using the CRB model for the effective scene

units: 1
var_id: shannon_information_content
long_name: final shannon information content of the rocinn inversion using the CAL model

units: 1
var_id: shannon_information_content_crb
long_name: final shannon information content of the rocinn inversion using the CRB model

units: 1
var_id: shannon_information_content_ge_nir
long_name: final shannon information content of the rocinn inversion using the CRB model for the effective scene

var_id: fitted_state_vector
long_name: fitted parameters in CAL ROCINN retrieval
units: various

units: various
var_id: fitted_state_vector_crb
long_name: fitted parameters in CRB ROCINN retrieval

units: 1
var_id: convergence_flag
long_name: flag signaling the convergence of the cal algorithm

units: 1
var_id: convergence_flag_crb
long_name: flag signaling the convergence of the crb algorithm

units: 1
var_id: satellite_orbit_phase
long_name: fractional satellite orbit phase

units: 1
var_id: geolocation_flags_nir
long_name: geolocation flags

units: 1
var_id: geolocation_flags_nir
long_name: ground pixel quality flag

units: 1
var_id: histogram_axis

units: nm
standard_name: TBA
var_id: calibration_subwindows_shift
long_name: irradiance wavelengths shift fitted values per subwindow

units: 1
standard_name: TBA
var_id: calibration_subwindows_squeeze
long_name: irradiance wavelengths squeeze fitted values per subwindow

units: 1
var_id: surface_classification_nir
long_name: land-water mask

units: degrees_north
var_id: latitude_bounds

units: degrees_north
var_id: latitude_bounds_nir

units: degrees_east
var_id: longitude_bounds

units: degrees_east
var_id: longitude_bounds_nir

units: 1
var_id: number_of_iterations_nir
long_name: number of rocinn iterations reached per pixel for the CAL model

units: 1
var_id: number_of_iterations_crb_nir
long_name: number of rocinn iterations reached per pixel for the CRB model

units: 1
var_id: number_of_iterations_ge_nir
long_name: number of rocinn iterations reached per pixel for the CRB model for the effective scene

units: 1
var_id: number_fitting_parameter
long_name: number_fitting_parameter dimension index

units: 1
var_id: number_fitting_parameter_crb
long_name: number_fitting_parameter_crb dimension index

units: 1
var_id: number_fitting_parameter_ge
long_name: number_fitting_parameter_ge dimension index

units: 1
var_id: number_of_calibrations
long_name: number_of_calibrations dimension index

units: 1
var_id: number_of_subwindows
long_name: number_of_subwindows dimension index

units: milliseconds
var_id: delta_time
long_name: offset from reference start time of measurement

units: 1
var_id: pdf_axis

units: 1
var_id: corner
long_name: pixel corner index

units: 1
var_id: reflectances
long_name: reflectances dimension index

units: 1
var_id: regularization_parameter
long_name: regularization parameter of the rocinn inversion using the CAL model

units: 1
var_id: regularization_parameter_crb
long_name: regularization parameter of the rocinn inversion using the CRB model

units: 1
var_id: fitted_root_mean_square_nir
long_name: root mean square residual

units: 1
var_id: fitted_root_mean_square_crb_nir
long_name: root mean square residual from the CRB model

units: 1
var_id: fitted_root_mean_square_ge_nir
long_name: root mean square residual from the CRB model for the effective scene

units: m
var_id: satellite_altitude
long_name: satellite altitude

units: degrees_east
var_id: satellite_longitude
long_name: satellite_longitude

units: 1
var_id: scaled_small_pixel_variance
long_name: scaled small pixel variance

units: 1
var_id: sea_ice_cover
long_name: sea-ice-cover

units: 1
var_id: snow_cover
long_name: snow-cover

units: 1
var_id: snow_ice_flag_nir
long_name: snow-ice mask

units: degree
standard_name: solar_azimuth_angle
long_name: solar azimuth angle
var_id: solar_azimuth_angle_nir

units: degree
long_name: solar zenith angle
standard_name: solar_zenith_angle
var_id: solar_zenith_angle_nir

units: degrees_north
var_id: satellite_latitude
long_name: sub satellite latitude

units: 1
var_id: sun_glint_flag
long_name: sun glint binary mask

units: 1
standard_name: surface_albedo
var_id: surface_albedo_fitted_nir
long_name: surface albedo fitted

units: 1
standard_name: surface_albedo
var_id: surface_albedo_fitted_crb_nir
long_name: surface albedo fitted from the CRB model

units: 1
standard_name: surface_albedo_standard_error
var_id: surface_albedo_fitted_precision_nir
long_name: surface albedo fitted precision

units: 1
var_id: surface_albedo_fitted_crb_precision_nir
standard_name: surface_albedo_standard_error
long_name: surface albedo fitted precision from the CRB model

units: 1
var_id: surface_albedo_nir
standard_name: surface_albedo_nir
long_name: surface albedo nir

units: m
standard_name: surface_altitude
var_id: surface_altitude_nir
long_name: surface altitude

units: m
var_id: surface_altitude_precision
standard_name: surface_altitude standard_error
long_name: surface altitude precision

units: Pa
standard_name: surface_air_pressure
long_name: surface_air_pressure
var_id: surface_pressure_nir

units: K
standard_name: surface_air_temperature
var_id: surface_temperature_nir
long_name: surface_air_temperature

units: 1
standard_name: toa_bidirectional_reflectance
var_id: continuum_reflectance_oxygen_Aband
long_name: toa bidirectional o2a continuum reflectance

units: 1
standard_name: toa_bidirectional_reflectance
var_id: reflectances_ocra
long_name: toa bidiretional ocra rgb reflectances

units: 1
var_id: vertices
long_name: vertices dimension index

units: degree
var_id: viewing_azimuth_angle_nir
standard_name: viewing_azimuth_angle
long_name: viewing azimuth angle

units: degree
long_name: viewing zenith angle
var_id: viewing_zenith_angle_nir
standard_name: viewing_zenith_angle

units: nm
var_id: wavelength_shift_crb
long_name: wavelength shift

units: nm
var_id: wavelength_shift_precision
long_name: wavelength shift precision

Co-ordinate Variables

units: degrees_north
standard_name: latitude
var_id: latitude_nir
long_name: pixel center latitude

units: degrees_east
standard_name: longitude
var_id: longitude_nir
long_name: pixel center longitude

standard_name: time
var_id: time
units: seconds
long_name: reference time for the measurements

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Coverage

Temporal Range

Start time:

2018-05-06T00:00:00

End time:

Ongoing

Geographic Extent

	90.0000°
-180.0000°		180.0000°
	-90.0000°

Dataset

Sentinel 5P: Cloud (CLOUD) level 2 data

Abstract

Details

Related Documents

Process overview

Instrument/Platform pairings

Computation Element: 1

Co-ordinate Variables

Temporal Range

2018-05-06T00:00:00

Ongoing

Geographic Extent

90.0000°

-180.0000°

180.0000°

-90.0000°

Authors (2)

Publishers (1)

Funders (2)

Principal Investigators (1)