Contributors: E. Carboni (UKRI-STFC RAL Space), G.E. Thomas (UKRI-STFC RAL SpaceT. Usedly and M. Pondrom (Deutscher Wetterdienst)

Issued by: STFC RAL Space (UKRI-STFC) / Elisa CarboniDeutscher Wetterdienst / Tim Usedly & Marc Pondrom

Date: 13 Jan 2025 Date: 09/02/2023

Ref: C3S2_D312a_Lot1.1.1.2-v4.0_2023024_202407_PQAD_ECV_CCICloudPropertiesCLD_SLSTR_v1.12

Official reference number service contract: 2021/C3S2_312a_Lot1_DWD/SC1

...

Anchor

...

history_of_modifications history_of_modifications

History of modifications

List of datasets covered by this document

Anchor
list_of_datasets list_of_datasets

List of datasets covered by this document

Anchor

...

related_documents related_documents

...

Related documents

Anchor
acronyms acronyms

Acronyms

...

Anchor
general_definitions general_definitions

General definitions

List of figures

General definitions

The “CCI product family” Climate Data Record (CDR) consists of two parts. The ATSR2-AATSR Cloud Properties CDR is formed by a TCDR brokered from the ESA Cloud_cci project and an ICDR derived from the SLSTR on board of Sentinel-3. ICDR uses the same processing and infrastructure as the TCDR. Both TCDR and ICDR data have been produced by STFC RAL space.

...

b=\frac{\sum_{i=1}^N (p_i - r_i)}{N} \ \ (Eq. 1)

...

...

bc- RMSE=\sqrt{\frac{\sum_{i=1}^N ((p-b)-r)^2}{N}} \ \ (Eq. 2)

...

Stability: The variation of the bias over a multi-annual time period

Table 1: Summary of variables and definitions

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)

...

Table 3: Definition of various technical terms used in the document

Scope of the document

This document provides a description of the product validation methodology for the global Cloud Properties Climate Data Record (CDR). 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.

In addition, the Interim Climate Data Record (ICDR) is the product derived from the SLSTR on board of Sentinel-3 and spans the period  from 2017 to present. Validation of this SLSTR derived product for the period from January 2017 to December 2021 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 in the validation of the TCDR product. The same methodology is applied to the ICDR dataset.

Executive Summary

The ESA Climate Change Initiative (CCI) Cloud Properties Climate Data Record (CDR) is a brokered product from the ESA Cloud_cci project, while the extension Interim CDR (ICDR) produced from the Sea and Land Surface Temperature Radiometers (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 Cloud_cci dataset comprises 17 years (1995-2012) of satellite-based measurements derived from the Along Track Scanning Radiometers (ATSR-2 and AATSR) on board the ESA second European Research Satellite (ERS-2) and ENVISAT. This TCDR is partnered with the ICDR produced from the Sentinel-3A SLSTR, beginning in 2017, and Sentinel-3B SLSTR beginning in October 2018. In addition to individual products from each Sentinel-3 platform, a combined product that averages data from both SLSTR instruments into single daily and monthly means will also be provided.

The TCDR and ICDR provide level-3 data, monthly means on a regular global latitude-longitude grid (with a resolution of 0.5°´ 0.5°) and daily data (with a resolution of 0.1°´0.1°) and includes these products: Cloud Fractional Cover (CFC), Cloud Phase (water/ice), Cloud Optical Thickness (COT), Cloud particle Effective Radius (CER), Liquid/Ice Water Path (LWP/IWP), and Cloud Top Pressure (CTP), Height (CTH) and Temperature (CTT).

This document is divided into different sections:

• the first section presents a brief description of the Cloud Properties CDR products together with references for further information;
• the second section presents the datasets used to estimate the accuracy of the CDR Cloud Properties dataset;
• the third section presents the methodology used for the validation and is divided in different subsections that describe the different parameters: CFC, CTH, CTP, COT, CER, LWP and IWP.

1. Validated products

The Cloud Properties CDR is formed by a TCDR brokered from the ESA Cloud_cci project and an ICDR derived from the SLSTR on board of Sentinel-3. Both TCDR and ICDR data have been produced by STFC RAL space.

The SLSTR ICDR, both from the individual instruments (version 3.0) and combining both in a single product (version 4.0), is supplied to the CDS via the same route and uses the same processing software and infrastructure as the TCDR. The retrieval algorithm is described in [D2]

These Cloud Properties datasets from polar orbiting satellites consist of: Cloud Fractional Cover (CFC), Cloud Top Pressure (CTP), Cloud Top Height (CTH), Cloud Top Temperature (CTT), Cloud Effective Radius (CER), Cloud Optical Thickness (COT), Liquid Water Path (LWP), Ice Water Path (IWP) and Cloud Water Path (CWP).

The datasets cover the period from June 1995 to April 2012 (TCDR) of satellite-based measurements derived from ATSR2 and AATSR onboard the polar orbiting ERS-2 and ENVISAT respectively, and the period from January 2017 onwards using the SLSTR measurements (ICDR), with Sentinel-3b and combined data becoming available from October 2018. These are level 3 products (daily and monthly means) on a regular global latitude-longitude grid (with 0.1° x 0.1° resolution for the daily mean files and 0.5° x 0.5° resolution for the monthly mean files). Table 1-1 report the bias values from [D4].

ESA’s Cloud_cci dataset on cloud properties, version 3, is the Climate Data Record used for generation of the brokered cloud properties dataset¹.

The Cloud_cci dataset can be downloaded here https://climate.esa.int/en/projects/cloud/data/. The SLSTR based ICDR extends the coverage, with a five year gap, from 2017 onwards and is only available through Copernicus Climate Data Store (CDS).

The TCDR dataset that includes cloud products as well as Surface Radiation Budget and Earth Radiation Budget products are described by Poulsen et al. (2019) [D3]. 

...

B_{i,j}=F_{Data,i,j}-F_{Ref,i,j}

MB=\frac{\sum_{i=1}^m\sum_{j=1}^n (w_j(B_{i,j}))}{m*n} \

MAB=\frac{\sum_{i=1}^m\sum_{j=1}^n w_j*|B_{i,j}-MB|}{m*n} \

Anchor
list_of_figures list_of_figures

List of figures

Anchor
list_of_tables list_of_tables

List of tables

Anchor
scope_of_the_document scope_of_the_document

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].

Anchor
executive_summary executive_summary

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).

Anchor
chapter1 chapter1

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.

Image Added

Anchor
chapter2 chapter2

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).

Anchor
section2.1 section2.1

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

Anchor
section2.2 section2.2

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

...

Parameters

...

TCDR (2003-2011) bias

...

ICDR (2017-2021) bias

SLSTR-A

...

ICDR (2019-2021) bias

SLSTR-B

...

CFC

...

- 8.1%

...

- 6

...

-6%

...

CTP

...

-25 hPa

...

-17 hPa

...

-17 hPa

...

LWP

...

-17.3 g/m²

...

-14.0 g/m²

...

-6 g/m²

...

IWP

...

-28.8 g/m²

...

-35 g/m²

...

-54 g/m²

2. Description of validating datasets

The Cloud Properties TCDR from ATSR2 and AATSR instruments are validated against CALIOP for cloud fractional cover, and cloud top height. We used the CALIOP level-2 1 km and 5 km cloud layer data record versions 3-01, 3-02 and 3-30¹ for validation of cloud fraction and cloud top height.

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite was launched in April 2006 together with CloudSat. The satellite carries the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and the first data became available in August 2006. CALIOP provides detailed profile information about cloud and aerosol particles and corresponding physical parameters.

The cloud top pressure, cloud optical thickness and cloud effective radius (for both liquid and ice cloud) are compared against MODIS Collection 6.1 Terra. The MODIS (or Moderate Resolution Imaging Spectroradiometer) 6.1 Terra monthly datasets are available here². For the validation we used the data from MODIS Collection 6.1 Terra (dx.doi.org/10.5067/MODIS/MOD08_M3.061).

Terra passes from north to south across the equator in the morning (local solar time 10:30). MODIS Terra and MODIS Aqua are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands or groups of wavelengths. The MODIS observation period started in 2000. We have used the level-3 MODIS gridded atmosphere monthly global products -

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. They contain monthly 1° × 1° grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapour, 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 , and atmospheric stability indices. Statistics are sorted into 1° × 1° cells on an equal-angle grid that spans a (calendar) monthly interval and are then summarized over the globe.

Liquid water path is validated against AMSR-E products and ice water path is validated against the DARDAR IWP product. The Advanced Microwave Scanning Radiometer – EOS (AMSR-E) LWP products can be found here³ The DARDAR data has been downloaded from the University of Lille site⁴.

Passive microwave imagers, such as the Advanced Microwave Scanning Radiometer – EOS (AMSR-E), can be used to retrieve column-integrated liquid water along with water vapour and surface wind speed. AMSR-E is a dual-polarization conical-scanning passive microwave radiometer with 12 channels ranging from 6.9 to 89 GHz. This instrument was designed to measure cloud properties, sea surface temperature and surface water, ice and snow. Because the microwave (MW) channels usually fully penetrate clouds, they provide a direct measurement of the total liquid (but not solid) cloud condensate amount.

DARDAR is a combined product based on measurement by CALIOP lidar and CPR onboard CloudSat. CPR is a nadir-looking cloud profiling radar sensing the atmosphere from above at 94 GHz. The DARDAR product has the vertical resolution of CALIOP (30/60 m) and a horizontal resolution given by the radar footprint (700m).

More details on the datasets used for the validation are described in [D1] section 2.4.2 and Annex A of [D1].

...

¹ https://www-calipso.larc.nasa.gov/

² https://modis-atmosphere.gsfc.nasa.gov/products/monthly

³ https://nsidc.org/data/ae_ocean/

listed in table 2-1.

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

Anchor
section2.3 section2.3

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

Anchor
section2.4 section2.4

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.

Anchor
chapter3 chapter3

...

3. Description of product validation methodology

The validation strategy is described in [D1] section 2.4. We use the bias, i.e. mean difference between Cloud_cci and the reference data, as the metric for accuracy. The bias corrected root mean squared error (bc-RMSE) is used to express the precision of CDR compared to a reference data record. The stability is the variation of the bias over a multi-annual time period.

...

Bias (accuracy):

...

Mean difference between Cloud_cci and reference data

...

bc-RMSE (precision):

...

Bias corrected root mean squared error to express the precision of Cloud_cci compared to a reference data record

...

Stability:

...

The variation of the bias over a multi-annual time period.

TCDR evaluation is divided into:

(i) validation against high quality and satellite-based reference observations (CALIOP, DARDAR and AMSR-E)

(ii) an intercomparison with well-established, satellite-based cloud datasets of similar kind (MODIS).

ICDR data are only validated against MODIS dataset following the same methodology described in section 4.1 of [D1].

3.1 Cloud Fractional Cover (CFC)

Cloud Fractional Cover (CFC) from TCDR is validated against SYNOP in [D1] section 3.2, validated against CALIOP in [D1] section 3.1.1 and compared with MODIS in [D1] section 4.1.1. The validation methodology can be found in the corresponding sections.

The ICDR CFC is compared against MODIS using the same methodology described in [D1] section 4.4.1.

3.2 Cloud Top Height (CTH)

Cloud Top Height is validated against the CALIOP data and the validation methodology is described in [D1] section 3.1.3.

The ICDR CTH is indirectly validated trough CTP comparison with MODIS.

3.3 Cloud Top Pressure (CTP), Cloud Optical Thickness (COT) and Cloud Effective Radius (CER)

For the TCDR these parameters are compared against MODIS Collection 6.1.

• The Cloud Top Pressure (CTP) is compared in [D1] section 4.1.2
• The Cloud Optical Thickness (COT), is compared in [D1] section 4.1.3 and 4.1.4 for the liquid and ice cloud phase.
• The Cloud Effective Radius (CER) is compared in [D1] section 4.1.5 and 4.1.6.

The comparison methodology is described in the corresponding section introduction, 4.1 [D1]. The ICDR uses the same methodology as the TCDR.

3.4 Cloud Liquid Water Path (LWP) and cloud Ice Water Path (IWP)

For the TCDR, the validation of the Cloud Liquid Water Path (LWP) against AMSR-E products is presented in [D1] section 3.1.4. The  comparison with MODIS Collection 6.1 is presented in [D1] section 4.1.7. The validation and comparison methodology are described in the mentioned sections.

For the TCDR, the validation of the Cloud Ice Water Path (IWP) against the DARDAR IWP product is presented in [D1] section 3.1.5. The comparison with MODIS Collection 6.1 is presented in section 4.1.8. The validation and comparison methodology can be found in the corresponding sections.

For the ICDR, data are compared with MODIS Collection 6.1 only, according to the methodology described in [D1] section 4.1.

4. Summary of validation results

The validation results for TCDR are provided in [D1] section 7, together with recommendation for use. Table 4-1 is an extract from table 7-2 in [D1]:

...

Cloud Parameter

...

 Comments from [D1]

...

Cloud fractional cover

...

Accuracy

...

Level-2 validation against CALIOP

...

Values taken from Table 4-2 and Table 4-13 (L3C comparisons to MODIS C6.1)

...

Cloud top height/ pressure

...

Accuracy

...

0.12km (liquid cloud)
-1.76km (ice cloud)

...

Level-2 validation against CALIOP

...

Stability (per decade)

...

Values taken from Table 4-4 and Table 4-15 (L3C comparisons to MODIS C6.1)

...

Liquid cloud optical depth

...

Accuracy

...

n/v

...

No validation possible due to a lack of reliable reference data. through LWP and IWP validation

...

Stability (per decade)

...

Values taken from Table 4-5 and Table 4-16 divided by mean MODIS C6.1 Terra COTliq (13) (L3C comparisons to MODIS C6.1)

...

No validation possible due to a lack of reliable reference data. through LWP and IWP validation

...

Values taken from Table 4-6 and Table 4-17 divided by mean MODIS C6.1 Terra COTice (10) (L3C comparisons to MODIS C6.1)

...

Level-2 validation against AMSR-E (Figure 3-1)

...

Values taken from Table 4-9 and Table 4-20 divided by mean MODIS C6 LWP (123g/m²) (L3C comparisons to MODIS C6)

...

Ice water path

...

Accuracy

...

-39.9%

...

Level-2 validation against DARDAR
(Figure 3-2)

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).

Anchor
section3.1 section3.1

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

Anchor
subsection3.1.1 subsection3.1.1

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.

Anchor
section3.2 section3.2

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:

B_{i,j}=F_{Data,i,j}-F_{Ref,i,j} \ (1)

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.

MB=\frac{\sum_{i=1}^m\sum_{j=1}^n (w_j(B_{i,j}))}{m*n} \ \ (2)

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.

MAB=\frac{\sum_{i=1}^m\sum_{j=1}^n w_j*|B_{i,j}-MB|}{m*n} \ \ (3)

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.

Anchor
section3.3 section3.3

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.

Anchor
chapter4 chapter4

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.

Anchor
References References

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.

...

Values taken from Table 4-10 and Table 4-21 divided by mean MODIS C6 IWP (208g/m²) (L3C comparisons to MODIS C6)

...

No validation possible due to a lack of reliable reference data. through LWP and IWP validation

...

Values taken from Table 4-7 and Table 4-18 (L3C comparisons to MODIS C6)

...

No validation possible due to a lack of reliable reference data. through LWP and IWP validation

...

Values taken from Table 4-8 and Table 4-19 (L3C comparisons to MODIS C6) 

Intercomparison (using the monthly mean data from January 2017 to December 2019) of ICDR products with MODIS present biases consistent with values found through the TCDR MODIS comparison (D1, section 4.1) for cloud fractional cover (- 5 %), cloud top pressure (-17 hPa) and liquid water path (-14.0 g/m² for SLSTR-A and -6 g/m² for SLSTR-B). 

The intercomparison present higher bias for ice water path, with global average bias of -35 g/m² for SLSTR-A and -54 g/m² for SLSTR-B (TCDR IWP bias with MODIS was -29 g/m³).

Figure 4-1 and Figure 4-2 show an example of ICDR monthly products for March 2017 and the equivalent monthly product from MODIS. More detailed description and analysis of the results is available in [D4].

Image Removed

...

Image Removed

...

Poulsen, C. A., McGarragh, G. R., Thomas, G. E., Stengel, M., Christensen, M. W., Povey, A. C., Proud, S. R., Carboni, E., Hollmann, R., and Grainger, R. G.: Cloud_cci ATSR-2 and AATSR data set version 3: a 17-year climatology of global cloud and radiation properties, Earth Syst. Sci. Data, 12, 2121–2135, 2020, https://doi.org/10.5194/essd-12-2121-2020.

Anchor
related_articles related_articles

Related articles