Improvement of Impact-based Forecast Using Multi Model Ensemble in 2024

 The Korea Meteorological Administration(KMA) is producing an impact-based forecast data based on Multi-Model Ensemble(MME) system which integrates Unified Model(global, global ensemble, local, and local ensemble models), ECMWF(global and global ensemble models) and KIM(Korean Integrated Model) global model for heat waves (HW) and cold waves (CW). MME-based impact forecast(MEPS) determines the impact(safe, concern, caution, warning, alarm) by using the probability of occurrence of maximum feels-like temperature for HW and lowest temperature for CW in Korea. 
   The distribution from 93 MME members was converted to a GEV(Generalized Extreme Value) distribution until 2023, but there is a problem that only the daily temperature can be considered. Definition of HW and CW should take into account the 2-day duration/falling temperature compared to the previous day. Therefore, the probability calculation method was modified with the ratio of the number of members satisfying the HW and CW condition among all members and its performance was compared with the previous method. 
  Verification was conducted by evaluating how well impact-based forecast was matched to the observed impact in 177 regions about 1~9 forecast day. HW was verified for August and September 2023, and CW was verified for December 2023 and January 2024.
  As a result, in the case of HW forecasting, impact-based forecast with new method showed a little better performance than previous method with GEV. New method has better Bias at concern(4-9day), warning(3-9day), alarm, and Equitable Thread Score at safe(2-9day), concern(2-9day), alarm. In addition, there are cases in which the definition of guidance is more accurately satisfied compared to the previous MEPS guidance, which was overestimated. Also, new method required much less calculation time than previous method. On the other hand, new method are not applied to CW MEPS due to its overall low performance. 
  It is presumed that the reason for the degration of performance of new method for CW is that the probability table for determining the impact in the probability distribution has not been tuned. If this table is optimized, it is expected that the performance can be improved in CW case, too.