Abstract
Heavy rainfall events often cause great societal and economic impacts. The prediction ability of traditional extrapolation techniques decreases rapidly with the increase in the lead time. Moreover, deficiencies of high-resolution numerical models and high-frequency data assimilation will increase the prediction uncertainty. To address these shortcomings, based on the hourly precipitation prediction of Global/Regional Assimilation and Prediction System-Cycle of Hourly Assimilation and Forecast (GRAPES-CHAF) and Shanghai Meteorological Service-WRF ADAS Rapid Refresh System (SMS-WARR), we present an improved weighting method of time-lag-ensemble averaging for hourly precipitation forecast which gives more weight to heavy rainfall and can quickly select the optimal ensemble members for forecasting. In addition, by using the cross-magnitude weight (CMW) method, mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (CC), the verification results of hourly precipitation forecast for next six hours in Hunan Province during the 2019 typhoon Bailu case and heavy rainfall events from April to September in 2020 show that the revised forecast method can more accurately capture the characteristics of the hourly short-range precipitation forecast and improve the forecast accuracy and the probability of detection of heavy rainfall.
Highlights
Short, extreme rainfall events are a frequent occurrence in China, often with great societal and economic impacts
It has been revealed that due to the lack of the physical mechanism of the evolution of the severe convective system, the prediction ability of this method decreases rapidly with the increase in the forecast lead times, and the available time limit of extrapolation is less than one hour [6]
In this study, based on the hourly precipitation forecast of the GRAPES-CHAF and SMS-WARR models, 16 different ensemble members are constructed for each model by using the time-lag-ensemble method
Summary
Extreme rainfall events are a frequent occurrence in China, often with great societal and economic impacts. Ensemble forecasting has significantly improved short and medium-term forecasting skills, it is still insufficient for local short-range heavy precipitation weather forecasting [14]. We pay more attention to the occurrence of heavy precipitation for very short-range precipitation forecast (0–6 h), but previous methods do not consider any specific weighting allocated to events more likely to cause significant impacts such as extreme rainfall events. In order to solve these problems, based on the objective classification of hourly rainfall intensity, we present a cross-magnitude weight (CMW) method for very short-range precipitation. This method adds more weight to heavy rain and determines the final forecast result by evaluating forecasts of hourly precipitation at different forecast initial times.
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