레이더기반 다중센서활용 강수추정기술의 개발
Development of Radar-Based Multi-Sensor Quantitative Precipitation Estimation Technique
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초록
Although the Radar-AWS Rainrate (RAR) calculation system operated by KoreaMeteorological Administration estimated precipitation using 2-dimensional composite componentsof single polarization radars, this system has several limitations in estimating the precipitationaccurately. To to overcome limitations of the RAR system, the Korea MeteorologicalAdministration developed and operated the RMQ (Radar-based Multi-sensor Quantitative PrecipitationEstimation) system, the improved version of NMQ (National Mosaic and Multi-sensorQuantitative Precipitation Estimation) system of NSSL (National Severe StormsLaboratory) for the Korean Peninsula. This study introduced the RMQ system domestically forthe first time and verified the precipitation estimation performance of the RMQ system. TheRMQ system consists of 4 main parts as the process of handling the single radar data, merging3D reflectivity, QPE, and displaying result images. The first process (handling of the singleradar data) has the pre-process of a radar data (transformation of data format and quality control),the production of a vertical profile of reflectivity and the correction of bright-band, andthe conduction of hydrid scan reflectivity. The next process (merger of 3D reflectivity) producesthe 3D composite reflectivity field after correcting the quality controlled single radarreflectivity. The QPE process classifies the precipitation types using multi-sensor informationand estimates quantitative precipitation using several Z-R relationships which are proper forprecipitation types. This process also corrects the precipitation using the AWS position withlocal gauge correction technique. The last process displays the final results transformed intoimages in the web-site. This study also estimated the accuracy of the RMQ system with fiveevents in 2012 summer season and compared the results of the RAR (Radar-AWS Rainrate)and RMQ systems. The RMQ system (2.36 mm hr−1 in RMSE on average) is superior to theRAR system (8.33 mm hr−1 in RMSE) and improved by 73.25% in RMSE and 25.56% in correlationcoefficient on average. The precipitation composite field images produced by theRMQ system are almost identical to the AWS (Automatic Weather Statioin) images. Therefore,the RMQ system has contributed to improve the accuracy of precipitation estimationusing weather radars and operation of the RMQ system in the work field in future enables to cope with the extreme weather conditions actively.
Abstract
Although the Radar-AWS Rainrate (RAR) calculation system operated by KoreaMeteorological Administration estimated precipitation using 2-dimensional composite componentsof single polarization radars, this system has several limitations in estimating the precipitationaccurately. To to overcome limitations of the RAR system, the Korea MeteorologicalAdministration developed and operated the RMQ (Radar-based Multi-sensor Quantitative PrecipitationEstimation) system, the improved version of NMQ (National Mosaic and Multi-sensorQuantitative Precipitation Estimation) system of NSSL (National Severe StormsLaboratory) for the Korean Peninsula. This study introduced the RMQ system domestically forthe first time and verified the precipitation estimation performance of the RMQ system. TheRMQ system consists of 4 main parts as the process of handling the single radar data, merging3D reflectivity, QPE, and displaying result images. The first process (handling of the singleradar data) has the pre-process of a radar data (transformation of data format and quality control),the production of a vertical profile of reflectivity and the correction of bright-band, andthe conduction of hydrid scan reflectivity. The next process (merger of 3D reflectivity) producesthe 3D composite reflectivity field after correcting the quality controlled single radarreflectivity. The QPE process classifies the precipitation types using multi-sensor informationand estimates quantitative precipitation using several Z-R relationships which are proper forprecipitation types. This process also corrects the precipitation using the AWS position withlocal gauge correction technique. The last process displays the final results transformed intoimages in the web-site. This study also estimated the accuracy of the RMQ system with fiveevents in 2012 summer season and compared the results of the RAR (Radar-AWS Rainrate)and RMQ systems. The RMQ system (2.36 mm hr−1 in RMSE on average) is superior to theRAR system (8.33 mm hr−1 in RMSE) and improved by 73.25% in RMSE and 25.56% in correlationcoefficient on average. The precipitation composite field images produced by theRMQ system are almost identical to the AWS (Automatic Weather Statioin) images. Therefore,the RMQ system has contributed to improve the accuracy of precipitation estimationusing weather radars and operation of the RMQ system in the work field in future enables to cope with the extreme weather conditions actively.
참고문헌 (19)
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