Precipitation estimation over radar gap areas based on satellite and adjacent radar observations
- 1Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea
- 2Radar Analysis Division, Weather Radar Center, KMA, Seoul, South Korea
Abstract. Continuous rainfall measurements from ground-based radars are crucial for monitoring and forecasting heavy rainfall-related events such as floods and landslides. However, complete coverage by ground-based radars is often hampered by terrain blockage and beam-related errors. In this study, we presented a method to fill the radar gap using surrounding radar-estimated precipitation and observations from a geostationary satellite. The method first estimated the precipitation over radar gap areas using data from the Communication, Ocean, and Meteorological Satellite (COMS); the first geostationary satellite of Korea. The initial precipitation estimation from COMS was based on the rain rate-brightness temperature relationships of a priori databases. The databases were built with temporally and spatially collocated brightness temperatures at four channels (3.7, 6.7, 10.8, and 12 μm) and Jindo (126.3° E, 34.5° N) radar rain rate observations. The databases were updated with collocated data sets in a timespan 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 precipitation field. Over the radar gap areas, this method finally merged the bias-corrected satellite precipitation with the radar precipitation obtained by interpolating the adjacent radar observation data. The merging was based on optimal weights determined from the root-mean-square error (RMSE) with the reference sensor observation or equal weights in the absence of reference data. This method was tested for major precipitation events during the summer of 2011 with assumed radar gap areas. The results suggested that successful merging appears to be closely related to the quality of the satellite precipitation estimates.