Geostatistical assessment of warm-season precipitation observations in Korea based on the composite precipitation and satellite water vapor data

摘要

Among the meteorological disasters, heavy rain-falls cause the second largest damage in Korea, following typhoons. To manage the potential disasters due to heavy rainfalls, understanding the observational characteristics of precipitation is of utmost importance. In this study, we investigate the spatial and temporal characteristics of warmseason precipitation in Korea, according to the precipitation types, by conducting the geostatistical analyses such as an autocorrelogram, Moran's I and general G on the composite (radar + station) precipitation data. The e-folding distance of precipitation ranges from 15 to 35 km, depending on the spatial distribution, rather than intensity, of precipitation, whereas the e-folding time ranges from 1 to 2 h. The directional analyses revealed that the warm-season precipitation systems in Korea, especially those with a high precipitation amount, have high spatial autocorrelations in the southwest-northeast and west-east directions, in association with the frontal rainfalls, convection bands, etc. Furthermore, the cluster versus dispersion patterns and the hot versus cold spots are analyzed through Moran's I and general G, respectively. Water vapor, represented by the brightness temperature, from three Himawari-8 water vapor bands also shows similar characteristics with precipitation but with strong spatial correlation over a much longer distance (similar to 100 km), possibly due to the continuity of water vapor. We found that, under the e-folding-based standard, the current observation network of Korea is sufficient to capture the characteristics of most precipitation systems; however, under a strict stan-dard (e.g., autocorrelation of 0.6), a higher-resolution observation network is essentially required - especially in local areas with frequent heavy rainfalls - depending on the directional features of precipitation systems. Establishing such an observation network based on the characteristics of precipitation enables us to improve