Development and Application of KIM-based Local Ensemble Model

The KIM(Korean Integrated Model)-based local ensemble model, which has a 3km horizontal resolution and 13 members, was developed to improve the prediction of heavy rainfall. The members of the local ensemble model were generated by downloading the KIM global ensemble model. The initial and boundary fields for the members were provided by the KIM global ensemble model. The local ensemble model covers the Korean Peninsula and surrounding areas and produces a 5-day forecast twice a day.
With the introduction of the KIM local ensemble model, the CSI score for precipitation were improved alleviating the underestimation of precipitation in the KIM global model.  In summer, the 75% and 90% percentiles of the local ensemble model show the best performance in heavy rainfall forecasting, while in winter, the median provides the best results.
The analysis verification(RMSE) results also showed that the KIM local ensemble model generally provided improved outcomes compared to the KIM regional model and exhibited similar performance to the UM (Met Office Unified Model)-based local ensemble.

The summer season on the Korean Peninsula is characterized by frequent extreme rainfall events, and this extreme rainfall presents a major challenge for forecasters in producing accurate forecasts. Therefore, various strategies using local ensembles have been developed to predict these extreme rainfall events. Probability matching and percentiles are representative methods, and by employing these techniques, many of the issues associated with the underestimation of extreme rainfall in numerical weather prediction have been largely addressed.

Keywords: local ensemble model, regional model, member, RMSE, CSI, underestimation, extreme rainfall, probability matching, percentiles