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http://dx.doi.org/10.7780/kjrs.2016.32.2.8

A Multi-sensor basedVery Short-term Rainfall Forecasting using Radar and Satellite Data - A Case Study of the Busan and Gyeongnam Extreme Rainfall in August, 2014-  

Jang, Sangmin (APEC Climate Center)
Park, Kyungwon (APEC Climate Center)
Yoon, Sunkwon (APEC Climate Center)
Publication Information
Korean Journal of Remote Sensing / v.32, no.2, 2016 , pp. 155-169 More about this Journal
Abstract
In this study, we developed a multi-sensor blending short-term rainfall forecasting technique using radar and satellite data during extreme rainfall occurrences in Busan and Gyeongnam region in August 2014. The Tropical Z-R relationship ($Z=32R^{1.65}$) has applied as a optimal radar Z-R relation, which is confirmed that the accuracy is improved during 20mm/h heavy rainfall. In addition, the multi-sensor blending technique has applied using radar and COMS (Communication, Ocean and Meteorological Satellite) data for quantitative precipitation estimation. The very-short-term rainfall forecasting performance was improved in 60 mm/h or more of the strong heavy rainfall events by multi-sensor blending. AWS (Automatic Weather System) and MAPLE data were used for verification of rainfall prediction accuracy. The results have ensured about 50% or more in accuracy of heavy rainfall prediction for 1-hour before rainfall prediction, which are correlations of 10-minute lead time have 0.80 to 0.53, and root mean square errors have 3.99 mm/h to 6.43 mm/h. Through this study, utilizing of multi-sensor blending techniques using radar and satellite data are possible to provide that would be more reliable very-short-term rainfall forecasting data. Further we need ongoing case studies and prediction and estimation of quantitative precipitation by multi-sensor blending is required as well as improving the satellite rainfall estimation algorithm.
Keywords
Weather Radar; COMS; Extreme Rainfall; Multi-Sensor Blending;
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