Contributors: E. Carboni (UKRI-STFC RAL Space), G. Thomas (UKRI-STFC RAL Space)

History of modifications

List of datasets covered by this document

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Acronyms

Scope of the document

This document provides a description of the product validation results for the Essential Climate Variable (ECV) Cloud Properties. These products are brokered to (in case of (A)ATSR) or produced for the Climate Data Store (in the case of SLSTR) by the Copernicus Climate Change Service (C3S).

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.

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. 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 TCDR and ICDR provided comprise daily (0.1° x 0.1° resolution), and monthly (0.5° x 0.5° resolution), means of cloud properties on a regular global latitude-longitude grid and includes the following products: Cloud Fractional Cover (CFC), Cloud Optical Thickness (COT), Cloud Effective Radius (CER), Liquid/Ice Water Path (LWP/IWP), and Cloud Top Pressure (CTP), Height (CTH) and Temperature (CTT). Note, that the brokered service within Copernicus provides only a subset of the original CCI cloud properties dataset, thus the mentioned products do not cover the entire range on cloud products contained in the original dataset provided by Cloud_cci.

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.

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.

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

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

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).

For nearly all validations for which a trusted reference data source is available, the compliance to GCOS requirements could be shown, e.g. cloud fractional cover stability, cloud top height accuracy and stability, liquid water path accuracy and stability and ice water path stability. Cloud fractional cover accuracy is close to the GCOS target requirements. For effective radius and optical thickness no reliable reference data is available for accuracy compliance analysis.

A general problem is the assessment of the stability. Stability assessments are based on comparisons to MODIS, which in turn however, is not an entirely reliable source itself as it is sometimes characterized by significant trends which may or may not be true.

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

Cloud Fractional Cover (CFC)

• Discrimination of heavy aerosol and cloudy is not optimal, thus aerosol is sometimes flagged as clouds in such conditions. It is advised to be careful in the interpretation cloudiness in periods with dust / volcanic ash outbreaks. Cloudiness is overestimated in these conditions.
• Cloud detection during polar night over snow and ice is generally difficult.
• Cloud detection in twilight conditions is of rather poor quality due to the small number of channels used.
• The ATSR2-AATSR cloud detection consistency between ATSR2 and AATSR is not optimal.
• Due to a limitation of passive imagers, the cloud fraction is usually biased towards lower values, compared to example CALIOP.

Cloud Top Pressure (CTP)

• In semi-transparent (ice) cloud conditions, the cloud top will be assigned too low.
• Multi-layer clouds are not modelled hence the CTH for cases of an upper layer of thin cirrus will effectively retrieve a radiative height (approx. 1 optical depth into the cloud).
• COT is a daytime product only.
• In cases of wrong phase (liquid/ice) assigned, the optical thickness is likely to have significant errors.
• In the case of incorrectly assigned surface BRDF the optical depth is likely to be biased. With high BRDF the COT will be biased low. With low BRDF the COT will be biased high.
• In case of sub-pixel clouds or cloud borders the COT is likely to have significant errors.
• In case of optically thin clouds above (especially poorly) defined highly reflecting surface, the COT retrieval might be problematic.
• For very optically thick clouds, the measurements go into saturation and thus the sensitivity of the measurement to the COT is small. Those values should be accompanied by large uncertainty values.

Cloud effective radius (CER)

• CER is a daytime product only
• In cases of wrong phase (liquid/ice) assigned, the effective radius is likely to have significant errors.
• In case of sub-pixel clouds or cloud boarders, the effective radius is likely to have significant errors.

Liquid water content (LWP)

• Monthly mean LWP is computed using only daytime measurements.
• Since LWP is computed from retrieved COT and CER, same limitations as for COT and CER apply for LWP.
• The method used assumes vertically homogeneous clouds, which might deviate from truth. In case of vertically inhomogeneous cloud layers, e.g. multi-layered clouds, the LWP retrieval is likely to show large errors, since the CER is retrieved from the uppermost cloud layers and may not be representative for the entire vertical column.
• In cases of wrongly assigned cloud phase, i.e. ice cloud is treated as liquid cloud, the retrieved LWP will show large errors.

Ice water content (IWP)

• Monthly mean IWP is computed using only daytime measurements.
• Similar limitations as mentioned for Cloud liquid water path

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