A General Comprehensive Evaluation Method for Cross-Scale Precipitation Forecasts

With the development of refined numerical forecasts, the problems such as the score distortion due to the division of precipitation thresholds in both traditional and improved scoring methods for precipitation forecast and the increasing subjective risk arisen from the scale setting of the neighbourhood spatial verification method have become increasingly prominent. To solve this issue, a general comprehensive evaluation method (GCEM) has been developed for cross-scale precipitation forecasts by directly analysing the proximity of precipitation forecasts and observations in this study. In addition to the core element of the precipitation forecast accuracy score (PAS) index, the GCEM system also includes score indices for insufficient precipitation forecasts, excessive precipitation forecasts, precipitation forecast biases and clear/rainy forecasts. The PAS does not distinguish the magnitude of precipitation and delimit the area of influence, it constitutes a fair scoring formula with objective performance and can be suitable for evaluating the rainfall events such as general and extreme precipitation. The PAS can be used to calculate the accuracy of numerical models or quantitative precipitation forecasts, enabling the quantitative evaluation of the comprehensive capability of various refined precipitation forecasting products. Based on the GCEM, comparative experiments between the PAS and TS are conducted for two typical precipitation weather processes. The results show that relative to TS, the PAS aligns with subjective expectations much more, indicating that the PAS is more reasonable than the TS. In addition, other indices of the GCEM are utilized to analyse the range and extent of both insufficient and excessive forecasts of precipitation, as well as the precipitation forecast ability in two weather processes. These indices not only provide overall scores for individual cases similar to the TS but also offer two-dimensional score distribution plots, which can comprehensively reflect the performance and characteristics of precipitation forecasts. Both theoretical and practical applications demonstrate that the GCEM exhibits distinct advantages and potential promotion and application value compared to the various mainstream precipitation forecast verification methods.