Sampling Error of Areal Average Rainfall due to Radar Partial Coverage

Abstract

This study estimated the error involved in the areal average rainfall derived from incomplete radar information due to radar partial coverage of a basin or sub-basin. This study considers the Han-River Basin as an application example for the rainfall observation using the Ganghwa rain radar. Among the total of 20 mid-sized sub-basins of the Han-River Basin evaluated in this study, only five sub-basins are fully covered by the radar and three are totally uncovered. Remaining 12 sub-basins are partially covered by the radar to result in incomplete radar information available. When only partial radar information is available, the sampling error decreases proportional to the size of the radar coverage, which also varies depending on the number of clusters. Conditioned that the total area coverage remains the same, the sampling error decreases as the number of clusters increases. This study estimated the sampling error of the areal average rainfall of partially-covered mid-sized sub-basins of the Han-River Basin, and the results show that the sampling error could be at least several % to maximum tens % depending on the relative coverage area.

Temporal precipitation estimation from nearby radar reflectivity using dynamic factor analysis in the mountainous watershed - a case during Typhoon Morakot

“…The radar reflectivity near Ali station was not completely related to the rainfall intensity because the mountain blockage resulted in incomplete radar information. The error involved in the estimation of the areal average rainfall may result from the partial radar coverage of a watershed (Yoo et al ., ). The Cishan and Yuemei stations, located at elevations of 63 m and 112 m, respectively, were not limited by the visibility of the radar scan range.…”

“…In addition, the spatial representations of rain gauges that are installed at particular locations in space are markedly inferior. Therefore, rainfall estimation based on meteorological radar reflectivity and rain gauge measurements is one of the most actively studied topics and offers advantageous procedures in hydrometeorology (Atlas et al ., ; Gourley et al ., ; Seo and Breidenbach, ; Kidd et al ., ; Yoo et al ., ). The advantages of applying radar measurement are that spatial and temporal rainfall intensity can be estimated in real time and in a large area, which established through the finer resolution of radar reflectivity that may not be captured by rain gauges (for example, Changnon and Vogel, ).…”

“…Numerous studies (Grecu and Krajewski, ; Morin et al ., ; Yoo et al ., ) raised issues regarding the accuracy of radar‐based rainfall estimation, which are subjected to different sources of errors such as variations of drop size distribution, partial beam blocking, attenuation, beam overshooting, and variations of the vertical reflectivity profile (Harrison et al ., ; Rico‐Ramirez et al ., ). Radar‐based rainfall estimation is mainly identified from the relationships between reflectivity (Z) and rain intensity (R), which may vary from temporal and spatial scales (Xiao and Chandrasekar, ; Chiang et al ., ).…”

Real-Time Parameter Estimation of a Dual-Pol Radar Rain Rate Estimator Using the Extended Kalman Filter

“…The distributed rainfall-runoff model can directly use this systematic data for various analyses, such as flood inundation and flash flood [3][4][5]. This trend is the same in Korea, where flash floods in mountain areas with steep and complex terrain structure are of serious concern [6]. For the radar rain rate to secure more accurate quality, the use of radar data must be located at the center of the flash flood warning system [7].…”

Using Extended Kalman Filter for Real-time Decision of Parameters of Z-R Relationship

“…따라서 관측 대상지역에 대한 제약이 적다 (Michelson and Koistinen, 2000;Brown et al, 2001;Costa and Alpuim, 2010). 특히, 우리나라와 같이 복잡한 산악지역이 국토의 대부분을 차지하고, 국지성 집중호우의 발생이 잦은 경우 에는 더욱 그러하다 (Yoo et al, 2008). 물론, 강우추정기술 이 보다 발전된다면 정확도가 향상된 레이더 강우자료의 생산도 가능할 것이다 (Alfieri et al, 2010).…”