수치모델에서 레이더 자료동화가 강수 예측에 미치는 영향
The Effect of Radar Data Assimilation in Numerical Models on Precipitation Forecasting
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초록
Accurately predicting localized heavy rainfall is challenging without high-resolutionmesoscale cloud information in the numerical model's initial field, as precipitation intensity andamount vary significantly across regions. In the Korean Peninsula, the radar observation networkcovers the entire country, providing high-resolution data on hydrometeors which is suitablefor data assimilation (DA). During the pre-processing stage, radar reflectivity is classifiedinto hydrometeors (e.g., rain, snow, graupel) using the background temperature field. The mixingratio of each hydrometeor is converted and inputted into a numerical model. Moreover,assimilating saturated water vapor mixing ratio and decomposing radar radial velocity into athree-dimensional wind vector improves the atmospheric dynamic field. This study presentsradar DA experiments using a numerical prediction model to enhance the wind, water vapor,and hydrometeor mixing ratio information. The impact of radar DA on precipitation predictionis analyzed separately for each radar component. Assimilating radial velocity improves thedynamic field, while assimilating hydrometeor mixing ratio reduces the spin-up period in cloudmicrophysical processes, simulating initial precipitation growth. Assimilating water vapor mixingratio further captures a moist atmospheric environment, maintaining continuous growth ofhydrometeors, resulting in concentrated heavy rainfall. Overall, the radar DA experiment showeda 32.78% improvement in precipitation forecast accuracy compared to experiments without DAacross four cases. Further research in related fields is necessary to improve predictions of mesoscaleheavy rainfall in South Korea, mitigating its impact on human life and property.
Abstract
Accurately predicting localized heavy rainfall is challenging without high-resolutionmesoscale cloud information in the numerical model's initial field, as precipitation intensity andamount vary significantly across regions. In the Korean Peninsula, the radar observation networkcovers the entire country, providing high-resolution data on hydrometeors which is suitablefor data assimilation (DA). During the pre-processing stage, radar reflectivity is classifiedinto hydrometeors (e.g., rain, snow, graupel) using the background temperature field. The mixingratio of each hydrometeor is converted and inputted into a numerical model. Moreover,assimilating saturated water vapor mixing ratio and decomposing radar radial velocity into athree-dimensional wind vector improves the atmospheric dynamic field. This study presentsradar DA experiments using a numerical prediction model to enhance the wind, water vapor,and hydrometeor mixing ratio information. The impact of radar DA on precipitation predictionis analyzed separately for each radar component. Assimilating radial velocity improves thedynamic field, while assimilating hydrometeor mixing ratio reduces the spin-up period in cloudmicrophysical processes, simulating initial precipitation growth. Assimilating water vapor mixingratio further captures a moist atmospheric environment, maintaining continuous growth ofhydrometeors, resulting in concentrated heavy rainfall. Overall, the radar DA experiment showeda 32.78% improvement in precipitation forecast accuracy compared to experiments without DAacross four cases. Further research in related fields is necessary to improve predictions of mesoscaleheavy rainfall in South Korea, mitigating its impact on human life and property.
참고문헌 (38)
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