Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications

Remote Sensing

Rainer Hollmann¹  VijuO. John²  Jörg Schulz²  JędrzejS. Bojanowski³  Quentin Bourgeois³  Anke Duguay-Tetzlaff³  Reto Stöckli³ 

[1] Deutscher Wetterdienst, Frankfurterstr. 135, 63067 Offenbach, Germany;EUMETSAT, Eumetsat-Allee 1, 64295 Darmstadt, Germany;Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, 8058 Zurich-Airport, Switzerland;

关键词: geostationary satellite;    cloud fractional cover;    climate data record;    decadal stability;    diurnal cycle;    Bayesian classifier;    historical satellites;   

DOI  :  10.3390/rs11091052

来源: DOAJ

【 摘 要 】

Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset from METeosat First and Second Generation (COMET) of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was created for the 25-year period 1991−2015. Modern multi-spectral cloud detection algorithms cannot be used for historical Geostationary (GEO) sensors due to their limited spectral resolution. We document the innovation needed to create a retrieval algorithm from scratch to provide the required accuracy and stability over several decades. It builds on inter-calibrated radiances now available for historical GEO sensors. It uses spatio-temporal information and a robust clear-sky retrieval. The real strength of GEO observations—the diurnal cycle of reflectance and brightness temperature—is fully exploited instead of just accounting for single “imagery”. The commonly-used naive Bayesian classifier is extended with covariance information of cloud state and variability. The resulting cloud fractional cover CDR has a bias of 1% Mean Bias Error (MBE), a precision of 7% bias-corrected Root-Mean-Squared-Error (bcRMSE) for monthly means, and a decadal stability of 1%. Our experience can serve as motivation for CDR developers to explore novel concepts to exploit historical sensor data.

【 授权许可】

Unknown