Multi-model Consensus Forecasting Technology with Optimal Weight for Precipitation Intensity Levels

In the daily weather forecasting business, different model outputs are available for forecasters, but it's difficult to quickly and accurately make quantitative precipitation forecasts based on subjective analysis. Therefore, statistical post-processing techniques are required to scientifically and rationally integrate the multi-model forecast results, so as to obtain a forecast result that take advantages of each model, and the multi-model consensus forecasting technology is introduced. In the past, the research of multi-model consensus precipitation forecast is either based on global model or regional model, but they are rarely integrated. In addition, for a certain forecast, weights are constant, rarely considering variation of forecast ability among different models and precipitation intensity levels. It is found that the unrevised global and regional model present different advantages in forecasting precipitation of different intensities. Multi-model consensus forecasting for precipitation based on both global and regional models, integrating respective advantages of models at different precipitation levels would produce better objective forecasts.To synthesize both forecasting advantages in global and regional models, a consensus forecasting technology combining global and regional models with optimized weights for different precipitation intensity levels is designed. The consensus forecast combined revised ECMWF-IFS's(European Center for Medium-Range Weather Forecasts-Integrated Forecast System) and SMS-WARMS's(Shanghai Meteorological Service WRF ADAS Real-Time Modeling System) precipitation forecasts, which are revised by optimal threat score method(abbreviated as EC-OTS and SMS-OTS) in the Pan-Yangtze River region(23°-39°N, 101°-123°E). Take 2018 as the model training period of consensus weight and 2019 as the independent sample forecast test period. Comparing the consensus forecast with EC-OTS, SMS-OTS and subjective forecast of forecasters, results show that EC-OTS has a greater weight at low precipitation levels, with the increase of precipitation level, the weight of SMS-OTS gradually increases. The average absolute error of the consensus forecast is slightly smaller than EC-OTS and significantly smaller than SMS-OTS with all lead times. The consensus forecast has higher threat scores than EC-OTS and SMS-OTS with almost all lead times at all precipitation levels. The threat score of the 12 h accumulated precipitation of the consensus forecast is -0.009 to 0.041 higher than the subjective forecast of local forecasters, and the threat score of 24 h accumulated precipitation forecast is 0.009 to 0.023 higher than the subjective forecast of China National Meteorological Center forecasters.