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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2018-83
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
12 Mar 2018
Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Hydrology and Earth System Sciences (HESS) and is expected to appear here in due course.
Geostatistical assessment of summertime rainfall observations in Korea based on composite precipitation and satellite water vapor data
Sojung Park1,3,4, Seon Ki Park1,2,3,4, Jeung Whan Lee5, and Yunho Park5 1Department of Climate and Energy Systems Engineering, Ewha Womans University, Seoul, Republic of Korea
2Department of Environmental Science and Engineering, Ewha Womans University, Seoul, Republic of Korea
3Severe Storm Research Center, Ewha Womans University, Seoul, Republic of Korea
4Center for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, Republic of Korea
5Korea Meteorological Administration, Seoul, Republic of Korea
Abstract. Among the meteorological disasters, heavy rainfalls cause the second largest damage in Korea, following typhoons. To confront with 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 summertime precipitation in Korea, according to the precipitation types, by conducting the geostatistical analyses such as 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 summertime precipitation in Korea has high spatial correlations in the southwest–northeast and west–east directions, mainly due to frontal rainfalls during the monsoon season. 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 show similar characteristics with precipitation but with strong spatial correlation over much longer distance (~ 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 standard (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 monitoring/tracking/prediction skills of high-impact weather phenomena as well as to enhance the utilization of numerical weather prediction.
Citation: Park, S., Park, S. K., Lee, J. W., and Park, Y.: Geostatistical assessment of summertime rainfall observations in Korea based on composite precipitation and satellite water vapor data, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-83, in review, 2018.
Sojung Park et al.
Sojung Park et al.

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Short summary
Understanding the precipitation characteristics is essential to design an optimal observation network. We studied the spatial and temporal characteristics of summertime precipitation systems in Korea via geostatistical analyses on the ground-based precipitation and satellite water vapor data. We found that, under a strict standard, an observation network with higher resolution is required in local areas with frequent heavy rainfalls, depending on directional features of precipitation systems.
Understanding the precipitation characteristics is essential to design an optimal observation...
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