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Korean Meteorological Society (한국기상학회)
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The Improvement of Summer Season Precipitation Predictability by Optimizing the Parameters in Cumulus Parameterization Using Micro-Genetic Algorithm

마이크로 유전알고리즘을 이용한 적운물리과정 모수 최적화에 따른 여름철 강수예측성능 개선

  • Jang, Ji-Yeon (Numerical Data Application Division, Numerical Modeling Center, KMA) ;
  • Lee, Yong Hee (Numerical Data Application Division, Numerical Modeling Center, KMA) ;
  • Choi, Hyun-Joo (Numerical Data Application Division, Numerical Modeling Center, KMA)
  • 장지연 (기상청 수치모델링센터 수치자료응용과) ;
  • 이용희 (기상청 수치모델링센터 수치자료응용과) ;
  • 최현주 (기상청 수치모델링센터 수치자료응용과)
  • Received : 2020.06.28
  • Accepted : 2020.09.25
  • Published : 2020.12.31
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Abstract

Three free parameters included in a cumulus parameterization are optimized by using micro-genetic algorithm for three precipitation cases occurred in the Korea Peninsula during the summer season in order to reduce biases in a regional model associated with the uncertainties of the parameters and thus to improve the predictability of precipitation. The first parameter is the one that determines the threshold in convective trigger condition. The second parameter is the one that determines boundary layer forcing in convective closure. Finally, the third parameter is the one used in calculating conversion parameter determining the fraction of condensate converted to convective precipitation. Optimized parameters reduce the occurrence of convections by suppressing the trigger of convection. The reduced convection occurrence decreases light precipitation but increases heavy precipitation. The sensitivity experiments are conducted to examine the effects of the optimized parameters on the predictability of precipitation. The predictability of precipitation is the best when the three optimized parameters are applied to the parameterization at the same time. The first parameter most dominantly affects the predictability of precipitation. Short-range forecasts for July 2018 are also conducted to statistically assess the precipitation predictability. It is found that the predictability of precipitation is consistently improved with the optimized parameters.

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References

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