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This study exploits the Meteosat Second Generation (MSG)–Spinning EnhancedVisible and Infrared Imager (SEVIRI) observations to evaluate the rain classat high spatial and temporal resolutions and, to this aim, proposes the RainClass Evaluation from Infrared and Visible observation (RainCEIV) technique.RainCEIV is composed of two modules: a cloud classification algorithm whichindividuates and characterizes the cloudy pixels, and a supervised classifierthat delineates the rainy areas according to the three rainfall intensityclasses, the non-rainy (rain rate value < 0.5 mm h-1)class, the light-to-moderate rainy class(0.5 mm h−1 ≤ rain rate value < 4 mm h-1), and theheavy–to-very-heavy-rainy class (rain ratevalue ≥ 4 mm h-1). The second module considers as input thespectral and textural features of the infrared and visible SEVIRIobservations for the cloudy pixels detected by the first module. It alsotakes the temporal differences of the brightness temperatures linked to theSEVIRI water vapour channels as indicative of the atmospheric instabilitystrongly related to the occurrence of rainfall events. The rainfall rates used in the training phase are obtained through thePrecipitation Estimation at Microwave frequencies, PEMW (an algorithm forrain rate retrievals based on Atmospheric Microwave Sounder Unit (AMSU)-Bobservations). RainCEIV's principal aim is that of supplying preliminaryqualitative information on the rainy areas within the Mediterranean Basinwhere there is no radar network coverage. The results of RainCEIV have beenvalidated against radar-derived rainfall measurements from the ItalianOperational Weather Radar Network for some case studies limited to theMediterranean area. The dichotomous assessment related to daytime (nighttime)validation shows that RainCEIV is able to detect rainy/non-rainy areas withan accuracy of about 97% (96%), and when all the rainy classes areconsidered, it shows a Heidke skill score of 67% (62%), a biasscore of 1.36 (1.58), and a probability of detection of rainy areas of81% (81%)

A statistical approach for rain intensity differentiation using Meteosat Second Generation–Spinning Enhanced Visible and InfraRed Imager observations