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Continuous rainfall measurements from ground-based radars are crucial formonitoring and forecasting heavy rainfall-related events such as floods andlandslides. However, complete coverage by ground-based radars is oftenhampered by terrain blockage and beam-related errors. In this study, wepresented a method to fill the radar gap using surrounding radar-estimatedprecipitation and observations from a geostationary satellite. The methodfirst estimated the precipitation over radar gap areas using data from theCommunication, Ocean, and Meteorological Satellite (COMS); the firstgeostationary satellite of Korea. The initial precipitation estimation fromCOMS was based on the rain rate-brightness temperature relationships ofa priori databases. The databases were built with temporally and spatiallycollocated brightness temperatures at four channels (3.7, 6.7, 10.8, and 12 μm)and Jindo (126.3° E, 34.5° N) radar rain rateobservations. The databases were updated with collocated data sets in atimespan of approximately one hour prior to the designated retrieval. Then,bias correction based on an ensemble bias factor field (Tesfagiorgis et al.,2011b) from radar precipitation was applied to the estimated precipitationfield. Over the radar gap areas, this method finally merged the bias-corrected satellite precipitation with the radar precipitation obtained byinterpolating the adjacent radar observation data. The merging was based onoptimal weights determined from the root-mean-square error(RMSE) with the reference sensor observation or equal weights in the absenceof reference data. This method was tested for major precipitation eventsduring the summer of 2011 with assumed radar gap areas. The resultssuggested that successful merging appears to be closely related to thequality of the satellite precipitation estimates

Precipitation estimation over radar gap areas based on satellite and adjacent radar observations