Theoretical basis of the algorithms and early phase results of the GCOM-C (Shikisai) SGLI cloud products

Abstract

Abstract This paper discusses the cloud/clear discrimination algorithm (CLAUDIA) and the cloud microphysical properties algorithm (CAPCOM), which are used by the Second-generation GLobal Imager (SGLI) aboard the GCOM-C satellite, launched in December 2017. Also described are the preliminary results of cloud products’ validation. CLAUDIA was validated by comparing cloud fractions derived from satellite data against data from whole-sky images captured by ground-based fisheye cameras. User’s accuracy and producer’s accuracy were mostly high at around 90%, and the resulting overall accuracy was also high, ranging from 83 to 100% (average of all sites was 90.5%). CLAUDIA has proven to be sufficiently accurate to apply a cloud mask to measurements and meets the requirements for releasing data for SGLI cloud flag products (the minimum for a successful GCOM-C mission). CAPCOM was evaluated by comparing cloud properties obtained by SGLI products against data from MODIS collection 6 products (MOD06). This was done for both ocean and land in the low to middle latitudes (60° N–60° S) from August 22, 2018 to September 14, 2018. The comparison showed good correlation coefficients for cloud optical thickness, effective particle radius, and cloud-top temperature for water clouds: 0.88 (0.83), 0.92 (0.88), and 0.94 (0.92) for ocean (land), respectively. CAPCOM data for ice cloud optical thickness correlated well with data from MODIS products: 0.86 (ocean) and 0.82 (land).

Keywords

• GCOM-C
• Shikisai
• SGLI
• Climate change
• Cloud properties
• Remote sensing