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Dataset

 
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: 50.6K Files | 9TB
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: 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.

Related Documents

 Tools for TROPOMI data
 TROPOMI webpage
 Sentinel-5p User Guide
 Sentinel 5P CLOUD ATBD
 TROPOMI Cloud

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
Coverage
Temporal Range
Start time:
2018-05-06T00:00:00
End time:
Ongoing
Geographic Extent

 
90.0000°
  
-180.0000°
  
180.0000°
 
-90.0000°
  
Related parties
Authors (2)
Publishers (1)
Funders (2)
Principal Investigators (1)