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

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).

An Intensive Campaign-Based Intercomparison of Cloud Optical Depth from Ground and Satellite Instruments under Overcast Conditions

“…AHI, aboard H-8, is a 16-channel multispectral imager able to capture visible light and infrared images with full-disk observations every 10 min. SGLI (Tanaka et al 2009;Letu et al 2016;Nakajima et al 2019), aboard GCOM-C, is a 19-channel radiometer. Its spatial resolution is 0.5−2 km for AHI data and 0.25−1 km for SGLI.…”

Temperature‐Independent Cloud Phase Retrieval From Shortwave‐Infrared Measurement of GCOM‐C/SGLI With Comparison to CALIPSO

“…and CER e E r can be determined uniquely from two-channel TOA reflectances in most cases, despite the fact that cloud droplet vertical profiles and size distributions in real clouds are complex and diverse. Figure 1a illustrates the relationships between the TOA reflectance at SW1 and SW4 used in the operational algorithm of the SGLI cloud property product (named SGLI-CLPR; Nakajima et al, 2019), which are separately shown for different cloud phases (liquid in blue and ice in red). Figure 1a (1)…”

“…The current operational product (or the standard product) of SGLI provides the cloud phase classification derived by a methodology that uses two TIR channels, T1 and T2, centered at 10.8 and 12.0 μm, respectively (Nakajima et al, 2019). The algorithm classifies liquid and ice phases according to the T1 brightness temperature and detects multi-layer clouds based on the T1 brightness temperature and the brightness temperature NAGAO AND SUZUKI 10.1029/2021EA001912 3 of 19 difference between T1 and T2.…”

Near-Ultraviolet to Near-Infrared Band Thresholds Cloud Detection Algorithm for TANSAT-CAPI

“…In the non-absorption visible and near-infrared bands, the clouds have higher reflectance value than that of clear sky surfaces [30]. Based on this fact, 0.67 µm reflectance is used for vegetated land and coastal regions, and 0.87 µm reflectance is used over water scenes in many cloud detection algorithms [31,32]. However, in the 0.67 and 0.87 µm bands, the reflectivity of different land surface types varies greatly in different seasons, so the reflectivity thresholds of these bands also change [33].…”

“…The thresholds of the tests are taken directly from the NNDT algorithm, considering the similar corresponding spectral bands between CAPI and SGLI. SGLI official cloud recognition product (SGLI-CLFG) is similar to MYD35, using the cloudy/clear discrimination algorithm (CLAUDIA) [31], which includes abundant cloud tests from visible to thermal infrared band. CCL is represented by a 3-bit binary number and is divided into 8 levels.…”