Page info infoType Modified date prefix Last modified on type Flat
Contributors: ET. Carboni (UKRI-STFC RAL Space), G.E. Thomas (UKRI-STFC RAL SpaceUsedly (DWD)
Issued by: STFC RAL Space (UKRI-STFC) / Elisa CarboniDeutscher Wetterdienst / Tim Usedly
Date: 2701/0408/20232024
Ref: C3S2_D312a_Lot1.2.3.5-v4.0_2023047_202408_PQAR_ECV_SRB_CCISurfaceRadiationBudgetSLSTR_v1.12
Official reference number service contract: 2021/C3S2_312a_Lot1_DWD/SC1
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List of datasets covered by this document
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List of datasets covered by this document
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Acronyms
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Acronym
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Definition
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AATSR
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Advanced Along-Track Scanning Radiometer
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ATBD
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Algorithm Theoretical Basis Document
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ATSR
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Along-Track Scanning Radiometer
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bc-RMSE
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Bias Corrected Root Mean Squared Error
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BOA
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Bottom of the Atmosphere
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BSRN
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Baseline Surface Radiation Network
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C3S
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Copernicus Climate Change Service
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CC4CL
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Community Cloud retrieval for Climate
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CCI
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Climate Change Initiative
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CDR
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Climate Data Record
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CERES
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Clouds and Earth Radiation Energy System
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EBAF
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Energy Balanced and Filled
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ECV
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Essential Climate Variable
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ENVISAT
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Environmental Satellite
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ERS
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European Research Satellite
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ESA
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European Space Agency
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GCOS
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Global Climate Observing System
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ICDR
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Interim Climate Data Record
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ORAC
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Optimal Retrieval of Aerosol and Cloud
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RAL
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Rutherford Appleton Laboratory
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SAL
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Surface Albedo
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SDL
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Surface Downwelling Longwave radiation
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SIS
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Surface Incoming Shortwave radiation
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SLSTR
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Sea and Land Surface Temperature Radiometers
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SNL
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Surface Net Longwave radiation
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SNS
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Surface Net Shortwave radiation
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SOL
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Surface Outgoing Longwave radiation
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SRB
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Surface Radiation Budget
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SRS
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Surface Reflected Shortwave radiation
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STFC
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Science and Technology Facilities Council
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TCDR
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Thematic Climate Data Record
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TOA
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Top of the Atmosphere
List of tables
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Table 1-1: Summary of methodologies used to estimate the accuracies, for TCDR and ICDR datasetsTable 2requirements for OLR and RSF based on GCOS [D3] Table 4-1: Summary of the accuracy of the Surface Radiation Budget datasetTable 3-1: Summary of KPI results with 2.5 and 97.5 percentiles and number of ICDR months within the rangeResults of evaluation against GCOS requirements for SLSTR SIS Table 4-2: Results of evaluation against GCOS requirements for SLSTR SRS Table 4- 13: Results of evaluation against GCOS targets for Earth Radiation Budget ECVs and CDR valuesrequirements for SLSTR SDL Table 4-4: Results of evaluation against GCOS requirements for SLSTR SOL |
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Figure 2-1: Results from [D1]; Top: Validation results for Cloud_cci surface incoming shortwave (SIS) flux using BSRN as a reference(a) Bias (black dots) and absolute bias (white triangle) for the comparison of SLSTR SIS and BSRN ground stations. Green shaded area marks the 10 W/m² threshold requirement by GCOS. (b) Number of available months per station Figure 2-2: Results from [D1]; Top: Validation results for Cloud_cci surface downwelling longwave (SDL) flux using BSRN as a referenceTemporal average of SIS from SLSTR and stations from the BSRN. Green dots indicate surface stations where the SLSTR SIS data meets the target accuracy, black triangles correspond to surface stations, where the SLSTR data does not meet the target accuracy Figure 2-3: SRS, SOL, SIS and SDL values from SLSTR (ICDR dataset) for March 2017Correlation between SLSTR SIS and BSRN ground stations. Blue line defines a perfect correlation (45°), the red line marks the slope Figure 2-4: SRS, SOL, SIS and SDL values from CERES dataset for March 2017 |
General definitions
The “CCI product family” Climate Data Record (CDR) consists of two parts. The ATSR2-AATSR Surface Radiation Budget CDR is formed by a TCDR brokered from the ESA Cloud_cci project and an ICDR derived from the Sea and Land Surface Temperature Radiometer (SLSTR) on board of Sentinel-3A and -B. ICDR uses the same processing and infrastructure as the TCDR. Both TCDR and ICDR data have been produced by STFC RAL Space.
These Surface Radiation Budget datasets from polar orbiting satellites consists of seven main variables: Surface Incoming Shortwave radiation (SIS), Surface Reflected Shortwave radiation (SRS), the Surface Net Shortwave radiation (SNS), the Surface Outgoing Longwave radiation (SOL), Surface Downwelling Longwave radiation (SDL), Surface Net Longwave radiation (SNL), and the Surface Radiation Budget (SRB).
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b=\frac{\sum_{i=1}^N (p_i - r_i)}{N} \ \ (Eq. 1) |
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bc- RMSE=\sqrt{\frac{\sum_{i=1}^N ((p-b)-r)^2}{N}} \ \ (Eq. 2) |
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Stability: The variation of the bias over a multi-annual time period
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Variables | Abbreviation | Definition |
Surface incoming solar radiation | SIS | The total incoming solar flux, measured at the Earth’s surface. |
Surface reflected solar radiation | SRS | The total upwelling shortwave flux, measured at the Earth’s surface. |
Surface net solar radiation | SNS | The net downwelling solar flux, measured at the surface (equal to SIS – SRS). |
Surface downwelling longwave radiation | SDL
| The total downwelling thermal infrared flux, measured at the Earth’s surface. |
Surface outgoing longwave radiation
| SOL
| The total upwelling thermal infrared flux, measured at the Earth’s surface. |
Surface net longwave radiation | SNL | The net downwelling thermal infrared flux, measured at the Earth’s surface (equal to SDL-SOL). |
Total surface radiation budget | SRB | The total net downwelling radiative flux, measured at the Earth’s surface (equal to (SIS+SDL) – (SRS+SOL)). |
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Processing level
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Definition
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Level-1b
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The full-resolution geolocated radiometric measurements (for each view and each channel), rebinned onto a regular spatial grid.
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Level-2 (L2)
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Retrieved cloud variables at full input data resolution, thus with the same resolution and location as the sensor measurements (Level-1b).
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Level-3C (L3C)
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Cloud properties of Level-2 orbits of one single sensor combined (averaged) on a global spatial grid. Both daily and monthly products provided through C3S are Level-3C.
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Jargon | Definition |
Brokered product | The C3S Climate Data Store (CDS) provides both data produced specifically for C3S and so-called brokered products. The latter are existing products produced under an independent programme or project which are made available through the CDS. |
Climate Data Store (CDS) | The front-end and delivery mechanism for data made available through C3S. |
Retrieval | A numerical data analysis scheme which uses some form of mathematical inversion to derive physical properties from some form of measurement. In this case, the derivation of cloud properties from satellite measured radiances. |
Forward model | A deterministic model which predicts the measurements made of a system, given its physical properties. The forward model is the function which is mathematically inverted by a retrieval scheme. In this case, the forward model predicts the radiances measured by a satellite instrument as a function of atmospheric and surface state, and cloud properties. |
TCDR | It is a consistently-processed time series of a geophysical variable of sufficient length and quality. |
ICDR | An Interim Climate Data Record (ICDR) denotes an extension of TCDR, processed with a processing system as consistent as possible to the generation of TCDR. |
CDR | A Climate Data Record (CDR) is defined as a time series of measurements with sufficient length, consistency, and continuity to determine climate variability and change. |
Scope of the document
This document provides a description of the product validation results for the Climate Data Record (CDR) of the Essential Climate Variable (ECV) Surface Radiation Budget. This CDR comprises inputs from two sources: (i) brokered products from the Cloud Climate Change Initiative (ESA’s Cloud_cci), namely those coming from processing of the Advanced Along-Track Scanning Radiometer (A)ATSR) data and (ii) those produced under this contract for the Climate Data Store, specifically those coming from processing of the Sea and Land Surface Temperature Radiometers (SLSTR).
The Thematic Climate Data Record (TCDR) is the product brokered from the European Space Agency Cloud Climate Change Initiative (ESA’s Cloud_cci) ATSR2-AATSR version 3.0 (Level-3C) dataset. This is produced by STFC RAL Space from the second Along-Track Scanning Radiometer (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) spanning the period 1995-2003 and the Advanced ATSR (AATSR) on board ENVISAT, spanning the period 2002-2012.
In addition, the Interim Climate Data Record (ICDR) is the product derived from the SLSTR onboard Sentinel-3A and –B and spans the period from January 2017 to present. Validation of this SLSTR derived product for the period from January 2017 to March 2022 is described in this document.
This document summarizes and refers to the methodology presented in the Cloud_cci Product Validation and Intercomparison Report [D1], used for the validation of the TCDR product. The same methodology is applied to the ICDR dataset.
Executive Summary
The ESA Climate Change Initiative (CCI) Surface Radiation Budget Climate Data Record (CDR) is a brokered product from the ESA Cloud_cci project (TCDR), while the extension Interim CDR (ICDR), produced from the Sea and Land Surface Temperature Radiometer (SLSTR), is produced specifically for C3S. The product is generated by STFC RAL Space, using the Community Cloud for Climate (CC4CL) processor, based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. The Surface Radiation Budget is a product of the Broadband Radiative Flux Retrieval (BRFR) module of CC4CL, which uses the cloud properties produced by ORAC to compute broadband radiative flux values. Please find further information in the Algorithm Theoretical Basis Document (ATBD) [D4].
The Cloud_cci dataset comprises 17 years (1995-2012) of satellite-based measurements derived from the Along Track Scanning Radiometers (ATSR-2 and AATSR) onboard the ESA second European Research Satellite (ERS-2) and ENVISAT satellites. This TCDR is partnered with the ICDR produced from the Sentinel-3A SLSTR, beginning in 2017, and Sentinel-3B SLSTR beginning in October 2018.
The dataset encompasses level-3 data (monthly means) on a regular global latitude-longitude grid (with a resolution of 0.5°´ 0.5°) and includes these products: the Surface Incoming and Reflected Shortwave radiation (SIS and SRS respectively), the Surface Downwelling and Outgoing Longwave radiation (SDL and SOL respectively), the Surface Net Shortwave and Longwave radiation (SNS and SNL), and the total Surface Radiation Budget (SRB). Table 2-1 provides a summary of the calculated accuracies of the Surface Radiation Budget dataset (see section 2).
This document is divided in different sections:
- the first section presents a brief description of validation methodology together with reference for further information;
- the second section presents the results of the validation and comparison of TCDR and ICDR data;
- the third section presents the compliance with user requirements and includes recommendation on the usage and know limitations.
1. Product validation methodology
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Product name
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Validation with BSRN
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Comparison with CERES
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Uncertainty propagation
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Surface Incoming Shortwave radiation (SIS)
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TCDR
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TCDR and ICDR
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Surface Reflected Shortwave radiation (SRS)
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TCDR and ICDR
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TCDR and ICDR
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Surface Net Shortwave radiation (SNS)
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TCDR and ICDR
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Surface Outgoing Longwave radiation (SOL)
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TCDR and ICDR
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Surface Downwelling Longwave radiation (SDL)
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TCDR
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TCDR and ICDR
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Surface Net Longwave radiation (SNL)
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TCDR and ICDR
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Surface Radiation Budget (SRB)
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TCDR and ICDR
The Product Validation and Intercomparison Report [D1] includes the validation and intercomparison of the TCDR Surface Radiation Budget versus the CERES satellite dataset. The same methodology is used for the ICDR.
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1 Please find more information on the reference data on [D1]: BSRN (Annex A.5) and CERES (A.6) |
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The validation results for the TCDR products are presented and described in detail in [D1], sections 3.3.2, 5.3 and 5.4. In this document, a summary highlighting the main results is presented. Only SIS and SDL in the TCDR product are validated with BSRN. All other properties (including SIS and SDL in the ICDR) are compared with CERES. The evaluation with CERES is considered to be a comparison because the CERES surface radiation dataset has a similar accuracy to the CDR dataset.
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Product name | TCDR Accuracy [W/m2] | ICDR SLSTR-A Accuracy [W/m2] | ICDR SLSTR-B Accuracy [W/m2] | ICDR A+B Accuracy [W/m2] |
Surface Incoming Shortwave radiation (SIS) | 8.2 | 1.8 | 0.23 | 0.51 |
Surface Reflected Shortwave radiation (SRS) | 4.6 | 1.6 | 2.1 | 2.2 |
Surface Net Shortwave radiation (SNS) | 13 | 3.4 | 2.3 | 2.7 |
Surface Outgoing Longwave radiation (SOL) | 11 | 1.6 | 4.1 | 3.8 |
Surface Downwelling Longwave radiation (SDL) | 12 | 9.7 | 11 | 11 |
Surface Net Longwave radiation (SNL) | 23 | 11 | 15 | 15 |
Surface Radiation Budget (SRB) | 36 | 14 | 17 | 18 |
The ICDR data of SIS, SRS, SOL and SDL are compared with CERES, using the methodology described in [D1] section 5.3 and 5.4, and results are presented here in section 2.2.
There is no direct validation (e.g. against more accurate measurements) for the net fluxes and these accuracies are estimated by error propagation. Sections 2.4, 2.5, 2.6 in this document present the estimate of the accuracy for SNS, SNL and SRB. Table 2-1 provides a summary of the CDR accuracies.
2.1 Validation with BSRN ground base radiative flux
BSRN stations measure direct, diffuse and global downwelling shortwave and longwave fluxes with a 1-minute temporal resolution. The 1-minute data was aggregated to monthly averages which were used for the validation. Using the TCDR and the reference datasets (for multiple locations around the world (see Figures 2-1 and 2-2)) we compute the bias and standard deviation.
The validation for Surface Incoming Shortwave radiation (SIS) and Surface Downwelling Longwave radiation (SDL) with BSRN ground measurements is described in section 3.3.2 of [D1].
Validation of BOA fluxes (i.e. SIS and SDL) in the TCDR against BSRN stations result in a standard deviation of 24 W/m² and a bias of 8.2 W/m² for shortwave radiation (SIS) and a standard deviation of 14 W/m² and a bias of 11.9 W/m² for longwave radiation (SDL).
Figures 2-1 and 2-2 show the results of the TCDR comparison with the BSRN incoming shortwave (SIS) and longwave (SDL) radiation with scatter plots and global maps showing the bias for each station.
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2.2 Comparison with CERES satellite data
The TCDR and reference datasets from CERES are compared by means of multi-annual mean and standard deviation, all for a common time period (2003-2011). Global maps of multiannual Surface Incoming Shortwave radiation (SIS) and Surface Downwelling Longwave radiation (SDL) are computed for the TCDR and reference dataset. The scores (bias and bc-RMSE) are calculated by including all valid data points (for a latitude band of 60°S - 60°N) pairwise in the CERES and the Cloud_cci dataset.
The validation for Surface Incoming Shortwave radiation (SIS) and Surface Downwelling Longwave radiation (SDL) with CERES is described in section 5.3 and 5.4 of [D1]. Intercomparison of Cloud_cci radiation products with CERES present bias of 1.53 W/m² for SIS and bias of 10.17 W/m2 for SDL.
The stability estimated for the TCDR dataset are 0.97 and 2.76 W/m²/decade for SIS and SDL respectively. The dataset is relatively stable during this period but shows some variations in the downwelling longwave radiation.
The same methodology is used to estimate the accuracy of SOL in comparison with CERES. Intercomparison of Cloud_cci radiation products (TCDR) with CERES present bias of 11 W/m² for SOL, which is the accuracy value reported in Table 2-1.
The evaluation method is also used to estimate the SIS, SRS, SOL and SDL accuracy of the ICDR.
Figures 2-3 and 2-4 show an example of ICDR products for March 2017 and the equivalent monthly mean product from CERES.
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Bias between TCDR and CERES are calculated including all valid data points (for a latitude band of 60° S-60° N). Intercomparison of ICDR radiation products with CERES has been performed using the data between January 2017 and March 2022. The resulting bias (reported in Table 2-1) are the following:
SLSTR-A: 1.8 W/m² for SIS, 1.6 W/m2 for SRS, 1.6 W/m2 for SOL and 9.7 W/m2 for SDL
SLSTR-B: 0.23 W/m² for SIS, 2.1 W/m² for SRS, 4.1 W/m² for SOL, 11 W/m² for SDL
SLSTR-A+B: 0.51 W/m² for SIS, 2.2 W/m² for SRS, 3.8 W/m² for SOL, 11 W/m² for SDL
General findings:
SIS (from [D1] section 5.3)
- The CDR dataset shows very similar patterns to the other Cloud_cci datasets of the global mean bottom of the atmosphere incoming solar radiation. Larger values are found for the subtropics; the maximum is located in the Atacama Desert. Lowest mean BOA incoming solar radiation is found over the polar regions.
- Spread among all the Cloud_cci datasets is highest for polar regions and high latitudes. Also the stratocumulus regions are clearly noticeable in the dataset.
- CDR dataset compared to CERES present similar patterns of temporal variability. The tropics show the lowest variability over time, while polar landmasses show the highest. CERES EBAF-SURFACE Ed4.0 contains both the highest and lowest values for temporal variability, but CDR and the other Cloud_cci datasets are alike.
- For the period from 2003 to 2011 no significant trends or anomalies are determinable. CDR dataset is relatively stable during this period and only show small variations in the BOA incoming solar radiation.
SDL (from [D1] section 5.4):
- Multi-annual global means of CDR and all Cloud_cci datasets compare very well with each other and hardly any larger differences are visible. The highest BOA downwelling thermal radiation is found over the tropics and subtropics, lowest values are found in Antarctica.
- With the exception of the inner tropics, there are slight differences between sea and land BOA downwelling thermal radiation in all datasets. Higher values are measured over the ocean than over land. Over land, mountains such as the Himalayas or the Andes are noticeable due to their clearly lower values.
- Similar to the global mean, the temporal variability of CDR datasets also shows a good agreement. In addition, the variability over land is significantly higher than over the ocean, with the exception of a narrow band in the inner tropics. The highest variability is found in East Asia.
- Strong seasonal cycles are visible with higher values in boreal summer and lower values in boreal winter. CDR time series, as well as all Cloud_cci datasets, also contain a small positive trend.
2.3 Surface Reflected Shortwave Radiation (SRS)
The TCDR accuracy of SRS is estimated from the accuracy of the Surface Incoming Shortwave radiation (SIS) and the Surface albedo (SAL).
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\Delta SRS= \frac{\delta SRS}{\delta SIS} \Delta SIS + \frac{\delta SRS}{\delta SAL} \Delta SAL = SAL \Delta SIS + SIS \Delta SAL, \quad \ \ (Eq. 3) |
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\Delta SIS |
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SAL = SRS / SIS, \quad \ \ (Eq. 4) |
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Accuracies of ICDR SRS are 1.6, 2.1 and 2.2 W/m2 for SLSTR-A, SLSTR-B and SLSTR A+B respectively and are estimated in comparison with CERES (section 2.2).
2.4 Surface Net Shortwave Radiation (SNS)
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SNS = SIS - SRS, \quad \ \ (Eq. 5) |
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\Delta SNS = \Delta SIS + \Delta SRS, \quad \ \ (Eq. 6) |
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2.5 Surface Net Longwave Radiation (SNL)
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SNL = SDL - SOL, \quad \ \ (Eq. 7) |
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\Delta SNL |
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\Delta SNL = \Delta SDL + \Delta SOL, \quad \ \ (Eq. 8) |
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(a) Bias (black dots) and absolute bias (white triangle) for the comparison of SLSTR SIS (equal area grid) and BSRN ground stations. Green shaded area marks the 10 W/m² threshold requirement by GCOS. (b) Number of available months per station Figure 2-5: Temporal average of SIS (equal area grid) from SLSTR and stations from the BSRN. Green dots indicate surface stations where the SLSTR SIS data meets the target accuracy, black triangles correspond to surface stations, where the SLSTR data does not meet the target accuracy Figure 2-6: Correlation between SLSTR SIS (equal area grid) and BSRN ground stations. Blue line defines a perfect correlation (45°), the red line marks the slope Figure 2-7: (a) Bias (black dots) and absolute bias (white triangle) for the comparison of SLSTR SRS and BSRN ground stations. Green shaded area marks the 10 W/m² threshold requirement by GCOS. (b) Number of available months per station Figure 2-8: Temporal average of SRS from SLSTR and stations from the BSRN. Green dots indicate surface stations where the SLSTR SRS data meets the target accuracy, black triangles correspond to surface stations, where the SLSTR data does not meet the target accuracy Figure 2-9: Correlation between SLSTR SRS and BSRN ground stations. Blue line defines a perfect correlation (45°), the red line marks the slope Figure 2-10: (a) Bias (black dots) and absolute bias (white triangle) for the comparison of SLSTR SRS (equal area grid) and BSRN ground stations. Green shaded area marks the 10 W/m² threshold requirement by GCOS. (b) Number of available months per station Figure 2-11: Temporal average of SRS (equal area grid) from SLSTR and stations from the BSRN. Green dots indicate surface stations where the SLSTR SRS data meets the target accuracy, black triangles correspond to surface stations, where the SLSTR data does not meet the target accuracy Figure 2-12: Correlation between SLSTR SRS (equal area grid) and BSRN ground stations. Blue line defines a perfect correlation (45°), the red line marks the slope Figure 2-13: (a) Bias (black dots) and absolute bias (white triangle) for the comparison of SLSTR SDL and BSRN ground stations. Green shaded area marks the 10 W/m² threshold requirement by GCOS. (b) Number of available months per station Figure 2-14: Temporal average of SDL from SLSTR and stations from the BSRN. Green dots indicate surface stations where the SLSTR SDL data meets the target accuracy, black triangles correspond to surface stations, where the SLSTR data does not meet the target accuracy Figure 2-15: Correlation between SLSTR SDL and BSRN ground stations. Blue line defines a perfect correlation (45°), the red line marks the slope Figure 2-16: (a) Bias (black dots) and absolute bias (white triangle) for the comparison of SLSTR SDL (equal area grid) and BSRN ground stations. Green shaded area marks the 10 W/m² threshold requirement by GCOS. (b) Number of available months per station Figure 2-17: Temporal average of SDL (equal area grid) from SLSTR and stations from the BSRN. Green dots indicate surface stations where the SLSTR SDL data meets the target accuracy, black triangles correspond to surface stations, where the SLSTR data does not meet the target accuracy Figure 2-18: Correlation between SLSTR SDL (equal area grid) and BSRN ground stations. Blue line defines a perfect correlation (45°), the red line marks the slope Figure 2-19: (a) Bias (black dots) and absolute bias (white triangle) for the comparison of SLSTR SOL and BSRN ground stations. Green shaded area marks the 10 W/m² threshold requirement by GCOS. (b) Number of available months per station Figure 2-20: Temporal average of SOL from SLSTR and stations from the BSRN. Green dots indicate surface stations where the SLSTR SOL data meets the target accuracy, black triangles correspond to surface stations, where the SLSTR data does not meet the target accuracy Figure 2-21: Correlation between SLSTR SOL and BSRN ground stations. Blue line defines a perfect correlation (45°), the red line marks the slope Figure 2-22: (a) Bias (black dots) and absolute bias (white triangle) for the comparison of SLSTR SOL (equal area grid) and BSRN ground stations. Green shaded area marks the 10 W/m² threshold requirement by GCOS. (b) Number of available months per station Figure 2-23: Temporal average of SOL (equal area grid) from SLSTR and stations from the BSRN. Green dots indicate surface stations where the SLSTR SOL data meets the target accuracy, black triangles correspond to surface stations, where the SLSTR data does not meet the target accuracy Figure 2-24: Correlation between SLSTR SOL (equal area grid) and BSRN ground stations. Blue line defines a perfect correlation (45°), the red line marks the slope |
General definitions
Table 1: Summary of variables and definitions
Variables | Abbreviation | Definition |
Surface Incoming Shortwave Radiation | SIS | Amount of shortwave radiation energy reaching the lower boundary of the atmosphere per unit of time and area from the above. |
Surface Reflected Shortwave Radiation | SRS | Amount of shortwave radiation energy reaching the lower boundary of the atmosphere per unit of time and area from below. |
Surface Outgoing Longwave Radiation | SOL | Amount of longwave radiation energy reaching the lower boundary of the atmosphere per unit of time and area from below. |
Surface Downwelling Longwave Radiation | SDL | Amount of longwave radiation energy reaching the lower boundary of the atmosphere per unit of time and area from the above. |
Surface Net Shortwave Radiation | SNS | Difference between the amount of shortwave radiation energy reaching the lower boundary of the atmosphere from below (upwelling) and the amount from above (downwelling). Values are provided per unit of time and area. |
Surface Net Longwave Radiation | SNL | Difference between the amount of longwave radiation energy reaching the lower boundary of the atmosphere from below (upwelling) and the amount from above (downwelling). Values are provided per unit of time and area. |
Surface Radiation Budget | SRB | Difference between the amount of radiation energy reaching the lower boundary of the atmosphere from below (upwelling) and the amount from above (downwelling). Values are provided per unit of time and area. |
Table 2: Definition of processing levels
Processing level | Definition |
Level-1b | The full-resolution geolocated radiometric measurements (for each view and each channel), rebinned onto a regular spatial grid. |
Level-2 (L2) | Retrieved cloud variables at full input data resolution, thus with the same resolution and location as the sensor measurements (Level-1b). |
Level-3C (L3C) | Cloud properties of Level-2 orbits of one single sensor combined (averaged) on a global spatial grid. Both daily and monthly products are provided through C3S are Level-3C. |
Table 3: Definition of various technical terms used in the document
Jargon | Definition |
TCDR | A Thematic Climate Data Record is a consistently processed time series of a geophysical variable. The time series should be of sufficient length and quality. |
ICDR | An Interim Climate Data Record (ICDR) denotes an extension of TCDR, processed with a processing system as consistent as possible to the generation of TCDR. |
Brokered product | The C3S Climate Data Store (CDS) provides both data produced specifically for C3S and so-called brokered products. The latter are existing products produced under an independent program or project which are made available through the CDS. |
Climate Data Store (CDS) | The front-end and delivery mechanism for data made available through C3S. It is a platform that provides access to a wide range of climate data, including satellite and in-situ observations, reanalysis and other relevant datasets. |
Retrieval | A numerical data analysis scheme which uses some form of mathematical inversion to derive physical properties from some form of measurement. In this case, the derivation of cloud properties from satellite measured radiances. |
Forward model | A deterministic model which predicts the measurements made of a system, given its physical properties. The forward model is the function which is mathematically inverted by a retrieval scheme. In this case, the forward model predicts the radiances measured by a satellite instrument as a function of atmospheric and surface state, and cloud properties. |
Remapping | Interpolation of horizontal fields to a new, predefined grid. All datasets are remapped to the same grid (1°x1°, latitude from -90° to 90°, longitude from -180° to 180°) to make them comparable. The remap is done with bilateral interpolation. |
Collocation | A collocation consists in filtering nan values of different datasets in the same grid to make them uniform. This is necessary to compare e.g. the global average of two datasets. |
Cosine weighted averaging | Consideration of different grid box areas. Grid boxes on usual equal angle grid boxes have a different area depending on the latitude (with larger areas towards the equator). Towards the poles the same number of boxes covers a smaller area; therefore, a correction factor is needed to achieve equal area grid boxes. This factor is the cosine of the latitude. The method is applied for calculation of global averages. |
Nearest neighbor | Technique used for a comparison of gridded, satellite-based data and ground station. Ground stations coordinates are used to extract the nearest grid point of the gridded dataset to calculate bias and further statistical measures. |
Plate Carree projection | Cylindrical projection of a map with meridians and parallels build equally spaced grids. |
Table 4: Definition of statistical measures used in the document
Statistical measures | Definition | ||
Bias | Mean difference between two datasets. In this case, a comparison between a gridded dataset and ground stations reference data, it is simply the arithmetic mean of the difference of all months for the nearest grid point in the datasets based on the location of the ground station. It is defined as:
with B the Bias, n as the number of months, y the dataset and o as the reference dataset. | ||
Mean Absolute Difference (MAD) | The Mean Absolute Difference is the arithmetic mean of the absolute biases of all months. It is defined as:
with MAD as Mean Absolute Difference, n as the number of months, y as the dataset and o as the reference dataset. | ||
Standard deviation | The standard deviation provides a quantification of the spread around the mean. It is defined as:
with SD as Standard Deviation, n as the number of months, y as the dataset and o as the reference dataset. | ||
Frac | Fraction of months with bias above the validation target values. It is defined as:
with n as the number of months, y as dataset and T as Target accuracy (10 W/m²) |
Scope of the document
This document provides a description of the product validation results for the Sea and Land Surface Temperature Radiometer (SLSTR) v4.0 based Interim Climate Data Record (ICDR) of the Essential Climate Variable (ECV) Surface Radiation Budget (SRB).
The dataset produced by RAL Space and Brockmann Consult (BC) under the Copernicus Climate Change Service (C3S) programme ranges from 01/2017 – 12/2023 and provides an Interim Climate Data Record (ICDR) to the brokered Thematic Climate Data Record (TCDR) from 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 by RAL Space for the ESA Cloud_cci programme and ranges from 06/1995 – 04/2012. 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 spanning from 01/2017 – 12/2023
Executive Summary
The Sea and Land Surface Temperature Radiometer onboard Sentinel-3A has provided data since January 2017. The launch of Sentinel-3B in October 2018 makes it possible to deliver not only individual data from both satellites but also a merged Sentinel-3A/3B product. The merged version (until 12/2023) is validated against measurements from ground stations from the Baseline Surface Radiation Network (BSRN). Depending on the temporal availability and specific variable up to 37 stations were used to provide the best possible global coverage. In addition to the merged SLSTR version, a second version on a different grid (equal area in addition to equal angle) is provided for the period from 07/2022 to 12/2023 and also validated against the same reference dataset as the equal angle version of SLSTR.
Validation to these SLSTR derived products is described in the following chapters of this document: Chapter 1 provides a summary of the product validation methodology while chapter 2 presents the validation results. A detailed validation methodology can be found in the Product Quality Assurance Document (PQAD) [D2]. Chapters 3 and 4 discuss possible application specific assessments and compliances with user requirements respectively.
Overall the SLSTR data meets the breakthrough/target GCOS requirement for the horizontal and temporal resolution. However, all variables do not meet the threshold GCOS requirements in terms of accuracy (Table 1-1); the values of the absolute bias are 13.05 W/m² (13.60 W/m²) for Surface Incoming Shortwave radiation (SIS) for equal angle grid (equal area grid), 13.36 W/m² (15.73 W/m²) for Surface Outgoing Longwave Radiation and do meet the threshold requirement (10 W/m²). Biases for Surface Reflected Shortwave Radiation (SRS) are 14.65 W/m² (14.00 W/m²) and Surface Downwelling Longwave Radiation (SDL) also do not meet the requirement due to a bias of 20.56 W/m² (18.40 W/m²). Comparison with the most continental and representative stations meet the threshold requirement by GCOS, outliers are mainly due the stations at high altitudes, high latitudes or Islands.
Anchor section1 section1
1. Product validation methodology
| section1 | |
| section1 |
Detailed information about the validation methodology can be found in the corresponding PQAD [D2], section 3. The validation process is separated into three parts: Data preparation (section 1.1), validation (section 1.2) and evaluation (1.3).
1.1 Data preparation
SLSTR data is provided at a regular latitude-longitude grid with 0.5°x0.5° spatial resolution and monthly means. The validation is based on the comparison with monthly means of available ground station measurements of the BSRN (Ohmura, 1998). A nearest neighbor technique is used for a comparison, using the ground stations coordinates to extract the nearest grid point of the gridded dataset to calculate the bias and further statistical measures.
1.2 Validation
Validation is done by calculating each station’s mean bias and absolute bias as well as a correlation of all available station-months. In addition, Standard deviation and Fraction of months outside the validation target values are calculated.
1.3 Evaluation
The previously calculated 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) [D3]. They are summarized in table 1-1.Comparison with CERES satellite data
Table 1-1: Summary of requirements for Surface Radiation Parameters: SIS, SDL and SOL based on GCOS [D3]Anchor table1_1 table1_1
Products | Requirement | Surface Incoming Shortwave Radiation | Surface Downwelling Longwave Radiation | Surface Outgoing Longwave Radiation |
Horizontal Resolution | G | 10 km | 10 km | 10 km |
B | 50 km | 50 km | 50 km | |
T | 100 km | 100 km | 100 km | |
Temporal Resolution | G | 1 h | 1 h | 1 h |
B | 24 h | 24 h | 24 h | |
T | 720 h | 720 h | 720 h | |
Accuracy | G | 1 W/m² | 1 W/m² | 1 W/m² |
B | 5 W/m² | 5 W/m² | 5 W/m² | |
T | 10 W/m² | 10 W/m² | 10 W/m² |
| Anchor | ||||
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Sections 2.1 – 2.4 show the validation results for the four variables: (i) Surface Incoming Shortwave Radiation (SIS), (ii) Surface Reflected Shortwave Radiation (SRS), (iii) Surface Downwelling Longwave Radiation (SDL) and (iv) Surface Outgoing Longwave Radiation (SOL).
2.1 Surface Incoming Shortwave Radiation
Figure 2-1: Plot (a, left) shows bias (black dots) and absolute bias (white triangle) for the comparison of SLSTR SIS and BSRN ground stations. Green shaded area marks the 10 W/m² threshold requirement by GCOS. Plot (b, right) shows the number of available months per stationAnchor figure2_1 figure2_1
Figure 2-1 shows bias and absolute bias for the SLSTR SIS compared to measurements from 37 stations. Most of the absolute biases are within the ± 10W/m² area with an overall small positive bias (1.82 W/m²). Four stations show a significant positive bias: Boulder (1689 m, east end of the Rocky Mountains), Darwin Met Office (North coast of Australia), Lanyu Island (Island close to Taiwan) and Reunion Island (Island east of Madagascar). A negative bias is seen in Izana (2373 m, central Spain). What all of these stations have in common is that they are either located at high altitude/topographic terrain or small islands/coastal areas and are questionable in terms of representativeness. In addition, Boulder and Darwin Met Office only rely on 19 and 17 months, respectively. Figure 2-2 indicates that almost all stations with absolute biases >10 W/m² are at high altitudes, islands/coasts or at high latitude (see Antarctica).
Figure 2-2: Temporal average of SIS from SLSTR and stations from the BSRN. Green dots indicate surface stations where the SLSTR SIS data meets the target accuracy (absolute bias less than 10W/m²), black triangles correspond to surface stations, where the SLSTR data does not meet the target accuracyAnchor figure2_2 figure2_2
The correlation of all available 1706 station-months shows good agreement with a correlation of 0.97 and a small positive bias (red line slightly below the blue line) (figure 2-3).
Figure 2-3: Correlation between SLSTR SIS and BSRN ground stations. Blue line defines a perfect correlation (45°), the red line marks the slopeAnchor figure2_3 figure2_3
2.1.1 Surface Incoming Shortwave Radiation (equal area grid)
Figure 2-4: Plot (a, left) shows the bias (black dots) and absolute bias (white triangle) for the comparison of SLSTR SIS (equal area grid) and BSRN ground stations. Green shaded area marks the 10 W/m² threshold requirement by GCOS. Plot (b, right) shows the number of available months per stationAnchor figure2_4 figure2_4
The reduced validation period (07/2022 – 12/2023) for the SLSTR SIS version on the equal area grid shows generally the same pattern (Figures 2-4 to 2-6). Stations in Izana and Reunion Island are the biggest outliers, while most of the stations´ biases are slightly positive but within the 10 W/m² area. The overall bias is higher compared to the SLSTR SIS on the equal angle grid (4.81 W/m²) but this might be related to a reduced number of months (312) and a different selection of stations. In addition to the previously mentioned outliers, Ny Alesund, another outlier, is located at Spitzbergen, Norway at a very high latitude of 78.93 °N.
Figure 2-5: Temporal average of SIS (equal area grid) from SLSTR and stations from the BSRN. Green dots indicate surface stations where the SLSTR SIS data meets the target accuracy (absolute bias > 10W/m²), black triangles correspond to surface stations, where the SLSTR data does not meet the target accuracyAnchor figure2_5 figure2_5
Figure 2-6: Correlation between SLSTR SIS (equal area grid) and BSRN ground stations. Blue line defines a perfect correlation (45°), the red line marks the slopeAnchor figure2_6 figure2_6
2.2 Surface Reflected Shortwave Radiation
Figure 2-7: (a) Bias (black dots) and absolute bias (white triangle) for the comparison of SLSTR SRS and BSRN ground stations. Green shaded area marks the 10 W/m² threshold requirement by GCOS. (b) Number of available months per stationAnchor figure2_7 figure2_7
Figures 2-7 to 2-9 show the bias and absolute bias for the SLSTR SRS data. For the outgoing radiations (SRS and SOL) a reduced number of stations is available for the period from 10/2018 on. The majority of stations show a negative bias (-5.22 W/m² on average). Outliers are again noticed for Izana (negative bias) and Boulder (positive bias). Stations south of 60°S latitude show generally a negative bias (Concordia Station, Georg von Neumayer Station and Syowa – all located on the Antarctica continent) and absolute biases are higher than 10 W/m². Other outliers are seen for the high-latitude stations Barrows and Ny-Alesund as well as the high altitude station of Izana. Stations with higher values tend to have a negative bias (see Figure 2-9), while satellite data with generally low values show good agreement with BSRN data.
Figure 2-8: Temporal average of SRS from SLSTR and stations from the BSRN. Green dots indicate surface stations where the SLSTR SRS data meets the target accuracy (absolute bias less than 10W/m²), black triangles correspond to surface stations, where the SLSTR data does not meet the target accuracyAnchor figure2_8 figure2_8
Figure 2-9: Correlation between SLSTR SRS and BSRN ground stations. Blue line defines a perfect correlation (45°), the red line marks the slopeAnchor figure2_9 figure2_9
2.2.1 Surface Reflected Shortwave Radiation (equal area grid)
Figure 2-10: (a) Bias (black dots) and absolute bias (white triangle) for the comparison of SLSTR SRS (equal area grid) and BSRN ground stations. Green shaded area marks the 10 W/m² threshold requirement by GCOS. (b) Number of available months per stationAnchor figure2_10 figure2_10
Comparison of SLSTR SRS data on the equal area grid shows the same pattern with an overall negative bias (-8.99 W/m²). Stations with absolute biases outside of 10 W/m² are generally the same as those mentioned previously while European continental stations (Budapest-Lorinc, Hungary and Payerne, Switzerland) meet the target accuracy requirements for SLSTR (see Figure 2-11). Correlation shows a slight negative bias with a correlation of 0.93.
Figure 2-11: Temporal average of SRS (equal area grid) from SLSTR and stations from the BSRN. Green dots indicate surface stations where the SLSTR SRS data meets the target accuracy (absolute bias less than 10W/m²), black triangles correspond to surface stations, where the SLSTR data does not meet the target accuracyAnchor figure2_11 figure2_11
Figure 2-12: Correlation between SLSTR SRS (equal area grid) and BSRN ground stations. Blue line defines a perfect correlation (45°), the red line marks the slopeAnchor figure2_12 figure2_12
2.3 Surface Downwelling Longwave Radiation
Figure 2-13: Plot (a, left) shows the bias (black dots) and absolute bias (white triangle) for the comparison of SLSTR SDL and BSRN ground stations. Green shaded area marks the 10 W/m² threshold requirement by GCOS. Plot (b, right) shows the number of available months per stationAnchor figure2_13 figure2_13
Compared with the previous variables, in the case of SDL most of the stations´ biases are positive (31/37) with more significant outliers for the high-altitude stations of Izana (2373 m), Sonnblick (3109 m) and Yushan (3858 m). Those three stations are clearly visible in figure 2-15 in a pronounced cloud of data points situated separately below the 1-1 line. The majority of the 1701 months shows a slight positive bias for values from 200 W/m² upwards, while generally lower values (<200 W/m²) are underestimated by SLSTR (majority of points belongs to Concordia Station, Antarctica. 26/37 stations have higher absolute bias than 10 W/m² (figure 2-14).
Figure 2-14: Temporal average of SDL from SLSTR and stations from the BSRN. Green dots indicate surface stations where the SLSTR SDL data meets the target accuracy (absolute bias > 10W/m²), black triangles correspond to surface stations, where the SLSTR data does not meet the target accuracyAnchor figure2_14 figure2_14
Figure 2-15: Correlation between SLSTR SDL and BSRN ground stations. Blue line defines a perfect correlation (45°), the red line marks the slope. The pronounced cloud of data below the 1-1 line are the outliers for the high-altitude stations of Izana (2373 m), Sonnblick (3109 m) and Yushan (3858 m).Anchor figure2_15 figure2_15
2.3.1 Surface Downwelling Longwave Radiation (equal area grid)
Figure 2-16: (a) Bias (black dots) and absolute bias (white triangle) for the comparison of SLSTR SDL (equal area grid) and BSRN ground stations. Green shaded area marks the 10 W/m² threshold requirement by GCOS. (b) Number of available months per stationAnchor figure2_16 figure2_16
Figures 2-16 to 2-18 show the same patterns for the SLSTR SDL equal area grid version with reduced months/stations availability. 312 station-months (compared to 1701) result in similar correlation coefficients (0.89 and 0.88 for equal area grid). However, it is worth mentioning that the selection of stations differs for the two comparisons and the absence of the Concordia Station removes parts of the negative bias.
Figure 2-17: Temporal average of SDL (equal area grid) from SLSTR and stations from the BSRN. Green dots indicate surface stations where the SLSTR SDL data meets the target accuracy (absolute bias > 10W/m²), black triangles correspond to surface stations, where the SLSTR data does not meet the target accuracyAnchor figure2_17 figure2_17
Figure 2-18: Correlation between SLSTR SDL (equal area grid) and BSRN ground stations. Blue line defines a perfect correlation (45°), the red line marks the slope. The pronounced cloud of data below the 1-1 line are the outliers for the high-altitude stations of Izana (2373 m), Sonnblick (3109 m)Anchor figure2_18 figure2_18
2.4 Surface Outgoing Longwave Radiation
Figure 2-19: (a) Bias (black dots) and absolute bias (white triangle) for the comparison of SLSTR SOL and BSRN ground stations. Green shaded area marks the 10 W/m² threshold requirement by GCOS. (b) Number of available months per stationAnchor figure2_19 figure2_19
The SLSTR SOL product exhibits the smallest bias (0.08 W/m²) compared to the other variables. This minimal bias arises because there is no overall trend, as observed with variables like SDL; instead, there is a balanced distribution (see figure 2-19). Correlation between SLSTR and BSRN is 0.97 while most of the months/stations tend to have a small negative bias which is compensated by measurements from Georg von Neumeyer and Izana Stations with positive biases (see figure 2-21).
Figure 2-20: Temporal average of SOL from SLSTR and stations from the BSRN. Green dots indicate surface stations where the SLSTR SOL data meets the target accuracy (absolute bias > 10W/m²), black triangles correspond to surface stations, where the SLSTR data does not meet the target accuracyAnchor figure2_20 figure2_20
Figure 2-21: Correlation between SLSTR SOL and BSRN ground stations. Blue line defines a perfect correlation (45°), the red line marks the slopeAnchor figure2_21 figure2_21
2.4.1 Surface Outgoing Longwave Radiation (equal area grid)
Figure 2-22: (a) Bias (black dots) and absolute bias (white triangle) for the comparison of SLSTR SOL (equal area grid) and BSRN ground stations. Green shaded area marks the 10 W/m² threshold requirement by GCOS. (b) Number of available months per stationAnchor figure2_22 figure2_22
Validation for the SLSTR SOL product on the equal area grid for the period from 07/2022 – 12/2023 is limited to eight available stations (and five for the whole time period) and should therefore be treated with caution. Overall bias is -2.63 W/m² and absolute bias 15.73 W/m² which is mainly due to the Station at Izana, which has a larger impact due to the small number of stations (Figures 2-22 to 2-24).
Figure 2-23: Temporal average of SOL (equal area grid) from SLSTR and stations from the BSRN. Green dots indicate surface stations where the SLSTR SOL data meets the target accuracy (absolute bias > 10W/m²), black triangles correspond to surface stations, where the SLSTR data does not meet the target accuracyAnchor figure2_23 figure2_23
Figure 2-24: Correlation between SLSTR SOL (equal area grid) and BSRN ground stations. Blue line defines a perfect correlation (45°), the red line marks the slopeAnchor figure2_24 figure2_24
Anchor section3 section3
3. Application(s) specific assessments
| section3 | |
| section3 |
This section is not applicable. There are no additional application specific assessments known since the dataset has just been published.
Anchor section4 section4
4.Compliance with user requirements
| section4 | |
| section4 |
The GCOS requirements [D3] for the ECV Surface Radiation Budget are used to evaluate the compliance for different users needs. Tables 4-1 and 4-2 show the requirements as well as the results.
GCOS defines three requirements depending on user’s 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
The SLSTR ICDR meets the breakthrough/target requirement (closely) for the horizontal/temporal resolution, respectively. However, all variables do not meet the requirements in terms of accuracy. Most of the continental stations are within the threshold requirement of 10 W/m², outliers stand out due to high altitudes, locations on islands or high latitude (e.g. Antarctica, Svalbard, Alaska)
It is worth mentioning that the GCOS requirements, defined by the World Meteorological Organisation (WMO), are not focused on satellite-based data records but also on climate models. Satellite-based data records, especially historical observing systems, are often not able to achieve the requirements.
Table 4-1: Results of evaluation against GCOS requirements for SLSTR SISAnchor table4_1 table4_1
Products | Requirement | Values | Surface Incoming Shortwave Radiation | |
Horizontal Resolution | G | 10 km | Roughly 55 km at the equator | |
B | 50 km | |||
T | 100 km | |||
Temporal Resolution | G | 1 h | Monthly mean (720h) | |
B | 24 h | |||
T | 720 h | |||
Accuracy | G | 1 W/m² | Merged SLSTR product vs. reference datasets (10/2018 – 12/2023): Bias: 1.82 W/m² Absolute Bias: 13.05 W/m² Standard Deviation: 15.97 W/m² Frac: 42.94% Available months: 1706 | Equal area SLSTR product vs. reference datasets (07/2022 – 12/2023): Bias: 4.81 W/m² Absolute Bias: 13.60 W/m² Standard Deviation: 14.91 W/m² Frac: 46.97 % Available months: 312 |
B | 5 W/m² | |||
T | 10 W/m² | |||
Table 4-2: Results of evaluation against GCOS requirements for SLSTR SRSAnchor table4_2 table4_2
Products | Requirement | Values | Surface Incoming Shortwave Radiation | |
Horizontal Resolution | G | 10 km | Roughly 55 km at the equator | |
B | 50 km | |||
T | 100 km | |||
Temporal Resolution | G | 1 h | Monthly mean (720h) | |
B | 24 h | |||
T | 720 h | |||
Accuracy | G | 1 W/m² | Merged SLSTR product vs. reference datasets (10/2018 – 12/2023): Bias: -5.22 W/m² Absolute Bias: 14.65 W/m² Standard Deviation: 16.20 W/m² Frac: 49.15 % Available months: 782 | Equal area SLSTR product vs. reference datasets (07/2022 – 12/2023): Bias: -8.99 W/m² Absolute Bias: 14.00 W/m² Standard Deviation: 11.45 W/m² Frac: 50.67 % Available months: 132 |
B | 5 W/m² | |||
T | 10 W/m² | |||
Table 4-3: Results of evaluation against GCOS requirements for SLSTR SDLAnchor table4_3 table4_3
Products | Requirement | Values | Surface Downwelling Longwave Radiation | |
Horizontal Resolution | G | 10 km | Roughly 55 km at the equator | |
B | 50 km | |||
T | 100 km | |||
Temporal Resolution | G | 1 h | Monthly mean (720h) | |
B | 24 h | |||
T | 720 h | |||
Accuracy | G | 1 W/m² | Merged SLSTR product vs. reference datasets (10/2018 – 12/2023): Bias: 16.90 W/m² Absolute Bias: 20.56 W/m² Standard Deviation: 9.49 W/m² Frac: 59.30 % Available months: 1701 | Equal area SLSTR product vs. reference datasets (07/2022 – 12/2023): Bias: 15.01 W/m² Absolute Bias: 18.40 W/m² Standard Deviation: 7.79 W/m² Frac: 54.04 % Available months: 312 |
B | 5 W/m² | |||
T | 10 W/m² | |||
Table 4-4: Results of evaluation against GCOS requirements for SLSTR SOLAnchor table4_4 table4_4
Products | Requirement | Values | Surface Outwelling Longwave Radiation | |
Horizontal Resolution | G | 10 km | Roughly 55 km at the equator | |
B | 50 km | |||
T | 100 km | |||
Temporal Resolution | G | 1 h | Monthly mean (720h) | |
B | 24 h | |||
T | 720 h | |||
Accuracy | G | 1 W/m² | Merged SLSTR product vs. reference datasets (10/2018 – 12/2023): Bias: 0.08 W/m² Absolute Bias: 13.36 W/m² Standard Deviation: 13.57 W/m² Frac: 49.23 % Available months: 781 | Equal area SLSTR product vs. reference datasets (07/2022 – 12/2023): Bias: -2.63 W/m² Absolute Bias: 15.73 W/m² Standard Deviation: 14.62 W/m² Frac: 52.48 % Available months: 132 |
B | 5 W/m² | |||
T | 10 W/m² | |||
| Anchor | ||||
|---|---|---|---|---|
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Ohmura, A., et al. (1998), Baseline Surface Radiation Network (BSRN/WCRP): New precision radiometry for climate research, Bulletin of the American Meteorological Society, 79(10), 2115-2136. DOI:https://doi.org/10.1175/1520-0477(1998)079<2115:BSRNBW>2.0.CO;2
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2.6 Surface Radiation Budget (SRB)
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| Mathinline |
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SRB = SNS + SNL, \quad \ \ (Eq. 9) |
...
| Mathinline |
|---|
\Delta SRB = \Delta SNS + \Delta SNL, \quad \ \ (Eq. 10) |
...
3. Application(s) specific assessments
In addition to the extensive product validation (see chapter 2 for results and chapter 2/3 in [D6] for validation methodology) a second assessment is introduced to evaluate the Interim Climate Data Record (ICDR) against the Thematic Climate Data Record (TCDR) in terms of consistency. Since frequent ICDR deliveries make detailed validation not feasible, a consistency check against the deeply validated TCDR is used as an indication of quality. This is done by a comparison of the following two evaluations:
- TCDR against a stable, long-term and independent reference dataset
- ICDR against the same stable, long-term and independent reference dataset
The evaluation method is generated to detect differences in the ICDR performance in a quantitative, binary way with so called Key Performance Indicators. The general method is outlined in [D5] chapter 3. The same difference between TCDR/ICDR and the reference dataset would lead to the conclusion that TCDR and ICDR have the same quality (key performance is "good"). Variations or trends in the differences (TCDR/ICDR against reference) would require a further investigation to analyze the reasons. The key performance would be marked as "bad". The binary decision whether the key performance is good or bad is made in a statistical way by a hypotheses test (binomial test). Based on the TCDR/reference comparison (global means, monthly or daily means) a range is defined with 95% of the differences are within. This range (2.5 and 97.5 percentile) is used for the ICDR/reference comparison to check whether the values are in or out of the range. The results could be the following:
- All or a sufficient high number of ICDR/reference differences lies within the range defined by the TCDR/reference comparison: Key performance of the ICDR is "good"
- A smaller number of ICDR/reference differences is within the pre-defined range: Key performance of the ICDR is "bad"
3.1 Results
The results of the KPI test are summarized in Table 3-1.
...
p2.5
p97.5
...
-1.29 W/m²
2.12 W/m²
...
-0.45 W/m²
0.36 W/m²
...
-16.4 W/m²
11.3 W/m²
...
-4.65 W/m²
4.48 W/m²
...
Sentinel-3B:
...
Percentiles were calculated based on the comparison of the TCDR using the Advanced Along Track Scanning Radiometer (AATSR) instrument against CERES as reference dataset for the variables Surface Incoming Shortwave Radiation (SIS), Surface Reflected Shortwave Radiation (SRS), Surface Outgoing Longwave Radiation (SOL) and Surface Downwelling Longwave Radiation (SDL). Percentiles were based on the time from 2002-2012 with monthly means and applied to the ICDR from 01/2017 (10/2018) to 06/2022 for Sentinel-3A (Sentinel-3B and merged product Sentinel-3A+B) based on measurements of the Sea and Land Surface Temperature Radiometer (SLSTR).
Most of the ICDR months are outside the TCDR-based KPI limits and leading to “bad” KPI tests. Therefore, the ICDR is not stable in relation to the TCDR. This is due to multiple reasons starting with the fact of a five year gap (2012-2016) between TCDR and ICDR. In addition, TCDR and ICDR are based on different instruments with SLSTR on Sentinel-3 and (A)ATSR/ATSR-2 on Envisat/ERS-2, respectively. Differences occur due to a lower bias between ICDR and reference dataset and a subtraction of the monthly means (based on the TCDR) to remove the annual cycle leads to values outside of the KPI range (see method in [D5], chapter 3.2.2). Please note that significant changes between 01/2017 - 12/2020 and 01/2017 - 12/2021 are due to bugfixes.
4. Compliance with user requirements
There are no direct user requirements for the Surface Radiation Budget defined in the Cloud_cci project. Looking at the GCOS ECV requirements for Surface Radiation Budget2 the values for SIS and SDL are 1 W/m2 uncertainty, while the TCDR dataset achieves an accuracy of 8 W/m2 for SIS and 12 W/m2 for SDL therefore they currently do not meet the GCOS requirements. Please find more detailed information about the target requirements in the corresponding (Target Requirement and Gap Analysis Document) TRGAD [D3].
ICDR accuracies (estimated with the first 5 years (2017 -2021)) appear to be better than TCDR accuracies. ICDR accuracies have been estimated in comparison with CERES surface fluxes. The better agreement of the ICDR dataset with CERES could come from different factors: (i) comparison of satellite vs satellite instead of satellite vs ground measurements; (ii) wider swath of SLSTR measurements; (iii) closest assumption (auxiliary data) used by both CERES and C3S estimate in the period considered (SRS and SOL strongly depend on surface reflectance and emissivity).
Table 4-1 provides an overview of the GCOS requirements for the surface radiative balance and the values achieved by the TCDR parameters. ICDR accuracies, estimated with 5 years of comparison with a similar satellite dataset, show results consistent with TCDR. It should be noted that GCOS requirements are targets and are often not attainable using existing or historical observing systems. The Cloud_cci doesn’t meet the requirement for resolving the diurnal cycle due to the nature of the satellite observations, but exceeds the spatial resolution.
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Product name
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GCOS targets
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Cloud_cci dataset
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SIS
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Frequency
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Monthly (resolving diurnal cycles)
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Cloud_cci products do not meet the requirement for resolving the diurnal cycle.
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Resolution
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100 km
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Cloud_cci products exceed the spatial resolution.
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Measurement uncertainty
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1 W/m² on global mean
Uncertainty: 8.2 W/m²
Standard Deviation: 24 W/m² on global mean
(Validation with BSRN ground base measurements)
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Stability
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0.2 W/m²/decade
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0.97 W/m²/decade (Comparison with CERES)
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SDL
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Frequency
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Monthly (resolving diurnal cycles)
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Cloud_cci products do not meet the requirement for resolving the diurnal cycle.
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Resolution
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100 km
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Cloud_cci products exceed the spatial resolution.
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Measurement uncertainty
...
1 W/m² on global mean
Uncertainty: 12 W/m2
Standard Deviation: 15 W/m2 on global mean
(Validation with BSRN ground base measurements)
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Stability
...
0.2 W/m²/decade
...
2.76 W/m2/decade
(Comparison with CERES)
Known limitations [From D1 table 7.1]:
- Higher uncertainties in twilight conditions, especially in the shortwave fluxes, due to limitation in retrieving cloud optical thickness and cloud particle effective radius (input to the radiation calculation) in these conditions
- Partly sparse temporal/spatial sampling (partly compensated by introduced diurnal cycles correction).
- Downwelling longwave fluxes seem biased high.
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This document has been produced with funding by the European Union in the context of the Copernicus Climate Change Service (C3S), operated by the European Centre for Medium-Range Weather Forecasts on behalf on the European Union (Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee of warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt, the European Commission and the European Centre for Medium-Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view. |
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