Theoretical basis of the algorithms and early phase results of the GCOM-C (Shikisai) SGLI cloud products
Autor: | Husi Letu, Riko Higuchi, Naoya Tamaru, Takashi Y. Nakajima, Naritoshi Imoto, Haruma Ishida, Takashi M. Nagao, Akihiro Yamazaki, Masahiro Hori |
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Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
010504 meteorology & atmospheric sciences
Climate change Cloud computing 010502 geochemistry & geophysics 01 natural sciences Shikisai Cloud properties 0105 earth and related environmental sciences Ice cloud SGLI Basis (linear algebra) business.industry lcsh:QE1-996.5 lcsh:Geography. Anthropology. Recreation Ranging Remote sensing GCOM-C lcsh:Geology lcsh:G Middle latitudes General Earth and Planetary Sciences Environmental science Satellite business Early phase Algorithm |
Zdroj: | Progress in Earth and Planetary Science, Vol 6, Iss 1, Pp 1-25 (2019) |
ISSN: | 2197-4284 |
DOI: | 10.1186/s40645-019-0295-9 |
Popis: | 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). |
Databáze: | OpenAIRE |
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