Evaluation for the Correction of Radar Rainfall Due to the Spatial Distribution of Raingauge Network

This study proposes a new method to estimate the bias correction ratio for the rainfall forecast to be used as input for a flash flood warning system. This method requires a backward tracking to locate where the forecasted storm is at the present time, and the bias correction ratio is estimated at the tracked location, not at the warning site. The proposed method was applied to the rainfall forecasts provided by the Korea Meteorological Administration. A total of 300 warning sites considered in the flash flood warning system for mountain regions in Korea (FFWS-MR) were considered as study sites, along with four different storm events in 2016. As a result, it was confirmed that the proposed method provided more reasonable results, even in the case where the number of rain gauges was small. Comparison between the observed rain rate and the corrected rainfall forecasts by applying the conventional method and the proposed method also showed that the proposed method was superior to the conventional method.

Section: Bias Correction Ratiomentioning

confidence: 99%

A Bias Correction Method for Rainfall Forecasts Using Backward Storm Tracking

Yoo

2018

Water

“…As the precipitation events occur in a given time period and space interval, our precipitation data are assumed to follow the Poisson distribution, which represents a probability situation of a large number of observation with a small probability of occurrence. Many studies developed the Poisson distribution models to estimate rainfall and to cluster the rainfall systems (e.g., Rodriguez-Iturbe et al, 1987;Lee et al, 2014;Barton et al, 2016;Ritschel et al, 2017). We have chosen the threshold values when the cumulative percentage of precipitation events for each criterion (i.e., C1 and C2) reached approximately 80 %; thus obtaining the threshold values of 20 % for C1 and 3 mm h −1 for C2, respectively.…”

Section: • • •"mentioning

confidence: 99%

Geostatistical assessment of summertime rainfall observations in Korea based on composite precipitation and satellite water vapor data

Lee

2018

Preprint

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.

“…The agrometeorological observation network consists of 11 AAOS stations (Choi et al, 2015). Based on these precipitation observations, the mesoscale structures of precipitation as well as the hydrologic budgets in Korea have been extensively studied (e.g., Kim and Lee, 2006;Cassardo et al, 2009;Jeong et al, 2012Jeong et al, , 2016Jung and Lee, 2013;Lee et al, 2017a). Capturing the spatiotemporal features of precipitation systems from the observation networks is essential to runoff forecast, especially at the catchment scale and for the flooding cases (Volkmann et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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

Lee

Hydrol. Earth Syst. Sci.

2018

Among the meteorological disasters, heavy rainfalls 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 (∼ 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 monitoring, tracking, and prediction skills of high-impact weather phenomena as well as to enhance the utilization of numerical weather prediction.