Last modified on May 30, 2025 12:14

Contributors: T. Usedly and M. Pondrom (Deutscher Wetterdienst)

Issued by: Deutscher Wetterdienst / Tim Usedly & Marc Pondrom

Date: 13 Jan 2025 

Ref: C3S2_D312a_Lot1.1.1.4_202407_PQAD_ECV_CLD_SLSTR_v1.2

Official reference number service contract: 2021/C3S2_312a_Lot1_DWD/SC1

History of modifications

List of datasets covered by this document

Related documents

Acronyms

General definitions

List of figures

List of tables

Scope of the document

This document provides a description of the product validation methodology for the Sea and Land Surface Temperature Radiometer (SLSTR) v4.0 based Interim Climate Data Record (ICDR) of the Essential Climate Variable (ECV) Cloud Properties.

The dataset produced by RAL Space and Brockmann Consult (BC) under the Copernicus Climate Change Service (C3S) program ranges from January 2017 to December 2023 and provides an Interim Climate Data Record (ICDR) to the brokered Thematic Climate Data Record (TCDR) from the European Space Agency Cloud Climate Change Initiative (ESA’s Cloud_cci).

The TCDR is a brokered product based on processing of the (Advanced) Along-Track Scanning Radiometer ((A)TSR) onboard ERS-2 and Envisat that was produced by RAL Space for the ESA Cloud_cci program and ranges from June 1995 to April 2012. Detailed validation methodology and results are presented in the Cloud_cci Product Validation and Intercomparison Report [D1].

The ICDR is derived with a five-year gap from SLSTR onboard the Sentinel-3A and -3B satellites covering 01/2017 – 12/2023. Detailed results are presented in the corresponding Product Quality Assessment Report (PQAR) [D2].

Executive Summary

The Sea and Land Surface Temperature Radiometer onboard Sentinel-3A provides data from 01/2017 on. With the launch of Sentinel-3B in 10/2018 not just individual data but also a merged version of Sentinel-3A/3B is provided (see chapter 1). The merged version (until 12/2023) is validated against the three following satellite-based datasets MODIS, CALIPSO-CALIOP and CLARA-A3, as well as ECMWF’s Reanalysis product ERA5 (see chapter 2). In addition to the merged SLSTR version, a second version on a different grid (equal area in addition to equal angle) is provided from 07/2022 to 12/2023 and also validated against the same reference datasets as the equal angle version of SLSTR.

All datasets are (if necessary) remapped to a 1°x1° grid and the following uncertainty metrics between datasets and reference calculated: Bias, Mean Bias and Mean Absolute Bias. The Mean Absolute Bias is then evaluated against requirements defined by the Global Climate Observing System (GCOS) (see chapter 3).

Overall, the SLSTR data does fulfill the requirements for accuracy by GCOS for Cloud Fractional Cover (CFC) and Ice Water Path (IWP)/ Liquid Water Path (LWP). Significant biases occur for comparison with Cloud Top Temperature (CTT), Cloud Top Height (CTH) and Cloud Top Pressure (CTP) and do not meet the requirements. Differences between the two provided grid versions from SLSTR are negligible and meet the goal requirement by GCOS (see chapter 4).

1. Validated products

The SLSTR-based dataset provides monthly means on a regular global latitude-longitude grid with 0.5°x0.5° spatial resolution for several variables of the Essential Climate Variable Cloud Properties (CLD): Cloud Fraction (CFC), Cloud Optical Thickness (COT), Cloud Effective Radius (CER), Cloud Top Pressure/Height/Temperature (CTP/CTH/CTT), Cloud Liquid Water Path/Ice Water Path (LWP/IWP).

The record is generated by RAL Space (data from January 2017 to June 2022) and Brockmann Consult (07/2022 – 12/2023) exclusively for the Climate Data Store (CDS) from the Copernicus Climate Change Service (C3S). The Data are provided for each individual satellite: for Sentinel-3A (S3A) from January 2017 to June 2022 and for Sentinel-3B (S3B) from October 2018 to June 2022. In addition, a merged version has been provided since 10/2018 when S3B was launched. Since 07/2022, Brockmann Consult has been providing a continuation of the merged version until 12/2023. BC provides data for the merged product for two different grids: (1) regular equal angle global latitude-longitude grid (continuation of previous data) and (2) regular equal area global latitude-longitude grid. The equal area projection uses a sinusoidal raster as aggregation raster for the binning process. A final transformation step maps the monthly aggregates into the plate-carree projection. During this projection, data of the sinusoidal raster close to the poles is repeatedly mapped to several plate-carree cells until the angle extension matches the ground extension in kilometers; the measurement data are not altered in this case.

An overview of the various data producers, satellites, grids and time coverages for the ICDRs is shown in Figure 1-1.

Figure 1-1: Overview of data producers, satellites, time coverages and grids for the ICDRs. Products above/below the black line are produced by RAL Space/Brockmann Consult (BC); the data generated since 07/2022 are provided in two different grids

2. Description of validating datasets

The SLSTR ICDR is validated against a wide range of datasets (Table 2-1). Sub-sections 2.1 – 2.4 provide a brief introduction to the respective datasets. All datasets provide a global coverage with a regular latitude-longitude grid with resolutions of 1°x1° or higher. Datasets with higher spatial resolution are remapped to 1° resolution (see Chapter 3).

Table 2-1: Datasets and information used for validation of SLSTR data

2.1 CLARA-A3

CM SAF CLoud, Albedo and surface RAdiation dataset from AVHRR data - Edition 3 (CLARA-A3), produced by EUMETSAT’s Satellite Application Facility on Climate Monitoring (CM SAF), provides data from 01/1979 on for Cloud Fractional Cover (CFC), Joint Cloud property Histogram (JCH), Cloud Top Level (CTO), Cloud Phase (CPH), Liquid Water Path (LWP), Ice Water Path (IWP) on a regular latitude-longitude grid with 0.25°x0.25° resolution and monthly means.

CLARA-A3 products are derived from Advanced Very High-Resolution Radiometers (AVHRR) onboard polar orbiting NOAA and MetOp satellites to derive spatio-temporal averaged datasets. The detailed description of the algorithm used to generate the Cloud Properties CDR is given in the CM SAF Algorithm Theoretical Basis Document [D3].

Data are downloaded via the CM SAF Web User Interface (WUI): https://wui.cmsaf.eu/safira/action/viewHome

2.2 MODIS

MODIS (MODerate resolution Imaging Spectroradiometer) is an advanced imaging instrument flying onboard the sun-synchronous polar-orbiting satellites Terra and Aqua. Terra passes from North to the South across the equator in the morning (local solar time 10:30) while Aqua passes from South to North over the equator in the afternoon (local solar time 13:30). Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands or groups of wavelengths.

Because of the proven stability of both instruments over time, the global Level-3 cloud products MOD08_M3 (Terra) and MYD08_M3 (Aqua) from MODIS Collection 6.1 (Platnick et al., 2017) are used here as a reference. They provide the monthly 1°x1° gridded average values of cloud optical and physical properties listed in table 2-1.

Data are downloaded via the MODIS Ordering Tool: https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/

2.3 CALIPSO-CALIOP

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite was launched in April 2006 and carries onboard the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) that provides detailed profile information about cloud and aerosol particles as well as corresponding physical parameters (Vaughan et al., 2009).

For the evaluation of the SLSTR ICDR, a special Level-3 product based on CALIOP data prepared for the Global Energy and Water cycle Experiment (GEWEX) cloud assessment study has been used. This dataset has a horizontal resolution of 1° and includes monthly averaged cloud parameters in different flavours. The top layer flavor is based on the top layer cloud only in each profile. Passive flavor chooses the top layer cloud which would be detected by a passive sensor, typically choosing the layer with the optical depth greater than or equal 0.3.

Data are downloaded via the CALIOP Ordering Tool: https://asdc.larc.nasa.gov/project/CALIPSO/CAL_LID_L3_GEWEX_Cloud-Standard-V1-00_V1-00

2.4 ERA5

ERA5 is the fifth generation of atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (Hersbach et al.,2020). Data is available with a spatial resolution of 0.25°x0.25° and monthly means from 1940 to present.

Reanalysis is a combination (data assimilation) of model data with observations and provides a globally complete and consistent dataset for several decades. In the case of ECMWF, a model trajectory of the previous forecast is fitted to the available observations in a 12-hour window to achieve the best estimate of the true atmospheric development.

Data are downloaded via the “ERA5 monthly averaged data on single levels from 1940 to present” landing page of the Climate Data Store. The variables “Total cloud cover”, “Total column cloud liquid water” and “Total column cloud ice water” are used for the validation of CFC, LWP and IWP respectively.

3. Description of product validation methodology

The validation methodology is separated into three parts: Data preparation (section 3.1) to make the SLSTR dataset as well as the four reference datasets comparable, Validation (section 3.2) against several global reference datasets and Evaluation (section 3.3) against the requirements defined by Global Climate Observing System (GCOS).

3.1 Data preparation

Datasets are prepared before the validation and modified towards a 1°x1° target grid with -90° to 90° latitude and -180° to 180° longitude. The validation is also based on monthly means. Except for CALIPSO-CALIOP that is available until 06/2023, the reference datasets are available from 10/2018 to 12/2023, while the SLSTR data start at different times:

Individual SLSTR onboard Sentinel-3A:          01/2017 – 06/2022 (not validated)

Individual SLSTR onboard Sentinel-3B:         10/2018 – 06/2022 (not validated)

Merged SLSTR product:                                 10/2018 – 12/2023

Merged SLSTR product on equal area grid:    07/2022 – 12/2023

3.1.1 Remapping

All datasets need to be on the same grid to make them comparable (e.g. difference of two maps). Therefore, a spatial resolution of 1°x1° is defined as the target grid for all datasets. MODIS and CALIOP are already available with a 1° resolution, while CLARA-A3, ERA5 and SLSTR need a remapping to meet the 1° grid. The remapping is done by a bilinear interpolation with the climate data operator (cdo¹) function named remapbil.

3.2 Validation

The following uncertainty metrics are calculated: Bias, Mean Bias and Mean Absolute Bias.

Bias is the difference between the (validated) dataset and the reference dataset for each month and grid box:

With B the Bias, F the validated/reference datasets, and i, j the indices. Prior to the bias calculation, the datasets are collocated and only grid points considered, where all datasets have valid values (not nan). This means, if grid points with nan values appear in at least one dataset, the corresponding grid points for all the datasets are set to nan and filtered out. This method can be applied on two or more datasets depending on what is compared. The bias is used for calculation of further uncertainty metrics.

The Mean Bias (MB) describes the overall bias with respect to a reference dataset. It is defined as the bias of two gridded data records and a subsequently calculation of the global spatial average. This results in one value per month which can be averaged over the whole time period.

with MB the Mean Bias, i and j (m and n, respectively) the indices, w the cosine weighting factor and B the Bias.

The Mean Absolute Bias (MAB) is a bias corrected uncertainty metric calculated by subtracting the previously calculated MB from every grid box bias. Subsequently, the same steps as for the calculation of the mean bias are applied.

The PQAR contains collocated, globally averaged climatologies of all datasets as well as deseasonalized climatologies. Difference plots (climatology of mean bias) are provided together with climatologies of the mean absolute bias. In addition, bias maps for each year are produced.

alidated trough CTP comparison with MODIS.

3.3 Evaluation

The Mean Absolute Bias is used as evaluation against the requirements defined by the Global Climate Observing System (GCOS) in the 2022 GCOS ECVs Requirements (GCOS 245) [D5].

GCOS defines three requirements depending on user needs:

• Goal (G): The strictest requirement, indicating no further improvements necessary
• Breakthrough (B): Intermediate level between threshold and goal. Breakthrough indicates that it is recommended for certain climate monitoring activities
• Threshold (T): Minimum requirement

It is worth mentioning, that these requirements are rather intended towards potentials and resolutions of climate models. Thus, GCOS requirements are not identical to the users needs outside the climate modelling community. Also, they are often not attainable using existing or historical observing systems.

Table 3-1: Summary of requirements for CFC, LWP, IWP, CTT and CTH based on GCOS [D5]

4. Summary of validation results

A brief summary of the validation results of SLSTR derived cloud products against the different reference datasets and an evaluation against GCOS requirement are provided in the following sections. Detailed results can be found in the corresponding Product Quality Assessment Report (PQAR) [D2].

Tables 4-1 to 4-3 summarize the evaluation of the accuracy metric against the GCOS threshold requirement. CFC from SLSTR meets the requirement compared to all datasets, while CTH, CTP and CTT show consistent biases with higher (lower) SLSTR values for CTT, CTP (and CTH). Ice Water Path and Liquid Water Path show also higher values for SLSTR which still fulfil the GCOS requirements.

Bias between the two provided grid versions for the time from 07/2022 to 12/2023 are small and meet the highest requirement defined by GCOS.

Table 4-1: Results of evaluation against GCOS requirements for SLSTR CFC

Table 4-2: Results of evaluation against GCOS requirements for SLSTR CTH/CTT

Table 4-3: Results of evaluation against GCOS requirements for SLSTR IWP/LWP

References

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., ... & Thépaut, J. N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049.

Platnick, S., Meyer, K. G., D., K. M., Wind, G., Amarasinghe, N., Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Holz, R. E., Yang, P., Ridgway, W. L., and Riedi, J., 2017: The MODIS Cloud Optical and Microphysical Products: Collection 6 Updates and Examples From Terra and Aqua, IEEE T. Geosci. Remote, 55, 502–525, doi: 10.1109/TGRS.2016.2610522.

Vaughan, M., Powell, K., Kuehn, R.l., Young, S., Winker, D., Hostetler, C., Hunt, W., Liu, Z., McGill, M., and Getzewich, B., 2009: Fully Automated Detection of Cloud and Aerosol Layers in the CALIPSO Lidar Measurements, J. Atmos. Oceanic Technol., 26, 2034–2050, doi: 10.1175/2009JTECHA1228.1.

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