...

Easy Heading Macro
navigationExpandOption collapse-all-but-headings-1-2

History of modifications

Expand
title Click here to expand the history of modifications
Version Date Description of modification Chapters / Sections V1.0 10/03/2021 Initial (Covers TCDR and additionally ICDR (2017 and 2018)) All

List of datasets covered by this document

Expand
title Click here to expand the list od datasets covered by this document
Deliverable ID Product title Product type (CDR, ICDR) Version number Delivery date D3.3.17-v3.0 ECV Cloud Properties brokered from ESA's Cloud_cci ATSR2-AATSRv3 dataset CDR V3.0 30/03/2020 D3.3.18-v3.x ECV Cloud Properties derived from SLSTR ICDR V3.1 30/11/2020 - onward

Anchor
relateddocuments relateddocuments

Related documents

Expand
title Click here to expand the list of related documents (D1-D3)
Reference ID Document D1 Product Validation and Intercomparison Report (PVIR), v6.0. ESA Cloud_cci. https://climate.esa.int/media/documents/Cloud_Product-Validation-and-Intercomparison-Report-PVIR_v6.0.pdf Last accessed on 13/10/2022. D2 Algorithm Theoretical Basis Document, v.6.2. ESA Cloud_cci. https://climate.esa.int/media/documents/Cloud_Algorithm-Theoretical-Baseline-Document-ATBD_v6.2.pdf Last accessed on 13/10/2022. D3 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

Acronyms

Expand
title Click here to expand the list of acronyms
Acronym Definition AATSR Advanced Along-Track Scanning Radiometer AMSR-E Advanced Microwave Scanning Radiometer – EOS ATBD Algorithm Theoretical Basis Document ATSR Along-Track Scanning Radiometer bc-RMSE Bias Corrected Root Mean Squared Error BRDF Bidirectional Reflectance Distribution Function C3S Copernicus Climate Change Service CALIOP Cloud-Aerosol Lidar with Orthogonal Polarization CCI Climate Change Initiative CDR Climate Data Record CER Cloud Effective Radius CFC Cloud Fractional Cover COT Cloud Optical Thickness CTH Cloud Top Height CTP Cloud Top Pressure CTT Cloud Top Temperature CWP Cloud Water Path DARDAR raDAR/liDAR ECV Essential Climate Variable ENVISAT Environmental Satellite ERS European Remote Sensing ESA European Space Agency GCOS Global Climate Observing System ICDR Interim CDR IWP Ice Water Path LWP Liquid Water Path MODIS Moderate Resolution Imaging Spectroradiometer RAL Rutherford Appleton Laboratory SLSTR Sea and Land Surface Temperature Radiometer STFC Science and Technology Facilities Council SYNOP Surface synoptic observations TCDR Thematic Climate Data Record

...

The TCDR is a brokered version of ESA’s Cloud_cci ATSR2-AATSR version 3.0 (Level-3C) dataset, produced by RAL from the second Along-Track Scanning Radiometer (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) spanned 1995-2003 and the Advanced ATSR (AATSR) on board ENVISAT spanned 2002-2012. In addition, the Sea and Land Surface Temperature Radiometer (SLSTR) on board of Sentinel-3 spans from 2017 to present and forms the ICDR. The validation for the ICDR is over the period from January 2017 to December 2018. The retrieval algorithm is presented in [D2] and the validation methodology refers to the Cloud_cci Product Validation and Intercomparison Report [D1]. The same methodology is applied to SLSTR dataset.

Poulsen et al. (2019) [D3] is the paper describing the dataset that includes cloud properties as well as Surface Radiation Budget and Earth Radiation Budget products. This document will mainly refer to the Cloud_cci Product Validation and Intercomparison Report [D1].

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 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.

...

Anchor
table1 table1

Table 1: provides a summary of the estimated accuracies of the TCDR together with the GCOS requirements. The validation results are provided in [D1] section 7-2, with a recommendation for use.

Info
icon false
1 Value obtained from the difference between CDR LWP trend (0.99g/m2/decade) and MODIS LWP trend (8.06g/m2/decade) divided by mean MODIS C6.1 LWP (123g/m²). 2 Value obtained from the difference between CDR IWP trend (-2.27g/m2/decade) and MODIS IWP trend (7.82g/m2/decade) divided by mean MODIS C6.1 IWP (208g/m²).

1. Product validation methodology

The validation methodology is described in section 2.4 of [D1]. In summary, we use the bias (mean difference) between CDR and 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, which is also known as the standard deviation about the mean. Stability is calculated as the variation of the bias over a multi-annual time period.

2. Validation results

2.1 Validation results for the TCDR

The validation results for TCDR products are provided in [D1], section 3 and 4. The evaluation is divided in validation against high quality and satellite-based reference observations (CALIOP, AMSR-E and DARDAR) and an intercomparison to well-established, satellite-based cloud datasets of similar kind (e.g. MODIS Collection 6.1). Table 1 in the Executive summary provides a summary of the resulting TCDR accuracies.

2.1.1 Cloud Fractional Cover (CFC)

Cloud Fractional Cover (CFC) is validated against CALIOP in [D1], section 3.1.1 and compared with MODIS in [D1], section 4.1.1.

A slight underestimation of cloud occurrences is found in the Cloud_cci data compared to CALIOP, which is primarily due to a lack of sensitivity of passive imager data with respect to optically very thin clouds.

2.1.2 Cloud Top Height (CTH)

Cloud Top Height is validated against CALIOP in [D1] section 3.1.3.

For liquid clouds only very small biases (0.12km) and bc-RMSE (0.97km) are found.

For ice clouds, the strong underestimation of cloud top height is evident and a common feature for all three Cloud_cci datasets, and mainly caused by high-level, optically thin clouds. Biases are around -3.5 km and bc-RMSE around 2.3 km. Removing the optically very thin cloud layers at the top of the CALIOP profiles, improves the agreement between Cloud_cci and CALIOP significantly.

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

These parameters are compared against MODIS in the following sections of [D1]: Cloud Top Pressure (CTP) is compared in section 4.1.2. Cloud Effective Radius (CER) is compared in section 4.1.5 and 4.1.6. Cloud Optical Thickness (COT) is compared in section 4.1.3 and 4.1.4 for liquid and ice cloud.

2.1.4 Liquid Water Path (LWP) and Ice Water Path (IWP)

Liquid Water Path (LWP) is validated in [D1] section 3.1.4 against AMSR-E products and compared with MODIS in [D1] section 4.1.7.

Ice Water Path (IWP) is validated against DARDAR IWP product in [D1] section 3.1.5 and compared with MODIS in section 4.1.8.

Validating liquid water path over ocean against AMSR-E gives very convincing results for CDR dataset, with bc-RMSE values of 25 g/m², only small biases (-1.44 g/m²) and high correlations (0.76). Validating ice water path against the combined CALIPSO-CloudSat product DARDAR shows good agreement with correlations of 0.45. There is a general underestimation of IWP by Cloud_cci which in terms of relative bias partly exceeds 50%.

2.2 ICDR comparison with MODIS

The first 24 months of SLSTR products have been compared against MODIS (Collection 6.1 Terra) following the same methodology described in [D1] section 4.1. We estimate the bias, i.e. mean differences, and the monthly mean global average of C3S and the MODIS data. To compute the monthly mean global average of both datasets we considered only the valid data between -60 and +60 latitude.

Table 2 shows the bias results from this comparison for both TCDR data (from D1) and for ICDR.

Of the cloud properties considered only the IWP has higher bias against MODIS for the ICDR.

Figure 1 and 2 show an example of ICDR monthly products for March 2017 and the equivalent monthly product from MODIS.

Anchor
table2 table2

Table 2: Bias of TCDR and ICDR cloud properties estimate in comparison with MODIS.

Anchor
figure1 figure1

Figure 1: CFC, CTP, LWP and IWP from SLSTR (ICDR dataset) for March 2017.

Anchor
figure2 figure2

Figure 2. CFC, CTP, LWP and IWP from MODIS dataset for March 2017

3. Application(s) specific assessment

N/A

4. Compliance with user requirements

The main validation results are summarized in [D1], Section 7, including a comparison with GCOS requirements in table 7.2. We report here a summary.

Table 1 provides an overview of the GCOS requirements for the Cloud Properties and the values achieved by 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 frequency requirement (3h) due to the nature of the satellite observations, but exceeds the spatial resolution (50 km GCOS target). ICDR accuracies (estimate with the first 24 months only) are consistent with TCDR accuracies apart from IWP where we find slightly higher bias in comparison with MODIS (section 2.2).

...

Recommendation on the usage and know limitations (from [D1] table 7.1):

Cloud Fractional Cover (CFC)

...

Info
icon false
This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation agreement signed on 11/11/2014). All information in this document is provided "as is" and no guarantee or 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.

Related articles

Content by Label
showLabels false max 5 spaces CKB showSpace false sort modified reverse true type page cql label in ("ecv","cloud_properties","cci") and type = "page" and space = "CKB" labels era-interim