한국형수치예보모델 KIM의 폭염 예측 성능 검증
Evaluation of Heat Waves Predictability of Korean Integrated Model
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초록
The global weather prediction model, Korean Integrated Model (KIM), has been inoperation since April 2020 by the Korea Meteorological Administration. This study assessed theperformance of heat waves (HWs) in Korea in 2020. Case experiments during 2018–2020 wereconducted to support the reliability of assessment, and the factors which affect predictabilityof the HWs were analyzed. Simulated expansion and retreat of the Tibetan High and NorthPacific High during the 2020 HW had a good agreement with the analysis. However, the modelshowed significant cold biases in the maximum surface temperature. It was found that the temperaturebias was highly related to underestimation of downward shortwave radiation at surface,which was linked to cloudiness. KIM tended to overestimate nighttime clouds that delayed thedissipation of cloud in the morning, which affected the shortage of downward solar radiation. The vertical profiles of temperature and moisture showed that cold bias and trapped moisturein the lower atmosphere produce favorable conditions for cloud formation over the Yellow Sea,which affected overestimation of cloud in downwind land. Sensitivity test was performed to reducemodel bias, which was done by modulating moisture mixing parameter in the boundarylayer scheme. Results indicated that the daytime temperature errors were reduced by increasein surface solar irradiance with enhanced cloud dissipation. This study suggested that not onlythe synoptic features but also the accuracy of low-level temperature and moisture conditionplayed an important role in predicting the maximum temperature during the HWs in medium-range forecasts.
Abstract
The global weather prediction model, Korean Integrated Model (KIM), has been inoperation since April 2020 by the Korea Meteorological Administration. This study assessed theperformance of heat waves (HWs) in Korea in 2020. Case experiments during 2018–2020 wereconducted to support the reliability of assessment, and the factors which affect predictabilityof the HWs were analyzed. Simulated expansion and retreat of the Tibetan High and NorthPacific High during the 2020 HW had a good agreement with the analysis. However, the modelshowed significant cold biases in the maximum surface temperature. It was found that the temperaturebias was highly related to underestimation of downward shortwave radiation at surface,which was linked to cloudiness. KIM tended to overestimate nighttime clouds that delayed thedissipation of cloud in the morning, which affected the shortage of downward solar radiation. The vertical profiles of temperature and moisture showed that cold bias and trapped moisturein the lower atmosphere produce favorable conditions for cloud formation over the Yellow Sea,which affected overestimation of cloud in downwind land. Sensitivity test was performed to reducemodel bias, which was done by modulating moisture mixing parameter in the boundarylayer scheme. Results indicated that the daytime temperature errors were reduced by increasein surface solar irradiance with enhanced cloud dissipation. This study suggested that not onlythe synoptic features but also the accuracy of low-level temperature and moisture conditionplayed an important role in predicting the maximum temperature during the HWs in medium-range forecasts.
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