Optimize Short-Term Rainfall Forecast with Combination of Ensemble Precipitation Nowcasts by Lagrangian Extrapolation

Abstract

The rainfall forecasts currently available in Korea are not sufficiently accurate to be directly applied to the flash flood warning system or urban flood warning system. As the lead time increases, the quality becomes even lower. In order to overcome this problem, this study proposes an ensemble forecasting method. The proposed method considers all available rainfall forecasts as ensemble members at the target time. The ensemble members are combined based on the weighted average method, where the weights are determined by applying the two conditions of the unbiasedness and minimum error variance. The proposed method is tested with McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) rainfall forecasts for four storm events that occurred during the summers of 2016 and 2017 in Korea. In Korea, rainfall forecasts are generated every 10 min up to six hours, i.e., there are always a total of 36 sets of rainfall forecasts. As a result, it is found that just six ensemble members is sufficient to make the ensemble forecast. Considering additional ensemble members beyond six does not significantly improve the quality of the ensemble forecast. The quality of the ensemble forecast is also found to be better than that of the single forecast, and the weighted average method is found to be better than the simple arithmetic average method.