Evaluation of the performance of GEFSv12 precipitation reforecast dataset in the Huaihe River basin
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Abstract:
Due to the complexity of atmospheric physical processes, numerical weather prediction often suffers from uncertainty. Ensemble forecasts can be used to represent the uncertainty and provide uncertainty information for risk-based decision-making. In recent years, hydrometeorological ensemble forecasting has become widely applied, which uses the ensemble outputs from the numerical weather prediction models to drive the hydrological models. Bias-free, accurate and reliable ensemble precipitation forecasts are important for producing accurate streamflow forecasts. Forecast verification is the process of assessing the quality of forecasts, which involves the investigation of the properties of the joint distribution of forecasts and observations. Objective evaluations of forecast quality can help monitor operational forecasts, assess the specific strengths and weaknesses of forecast systems and support decision-making. Global Ensemble Forecast System, version 12 (GEFSv12), is a new generation of reforecast dataset generated by the National Centers for Environmental Prediction (NCEP). Reforecast dataset is generated by retrospectively re-forecasting weather for previous years using the current dynamical model. It is an important dataset for improving weather predictions. The performance of GEFSv12 precipitation forecasts has not been discussed in the Huaihe River basin, so we evaluated the performance of GEFSv12 precipitation forecasts in the Huaihe River basin in this research. Due to the systematic bias of the raw forecasts, the Bayesian Joint Probability (BJP) model was used to perform statistical post-processing on the raw GEFSv12 reforecasts. The quality of the raw and post-processed GEFSv12 ensemble reforecasts was evaluated in three aspects, i. e. , bias, accuracy and reliability. Verification metrics included Root Mean Squared Error (ERMS), Brier Skill Score ( EBSS), Continuous Ranked Probability Skill Score (ECRPSS), αindex and Reliability Diagram. The main results are as follows:(1) In terms of forecast bias, according to the map of the spatial variation of precipitation and the results of ERMS, raw GEFSv12 precipitation forecasts suffer from a large bias, and have difficulty in reproducing the spatial variability of precipitation within the Huaihe River basin. Statistical post-processing approach can effectively reduce the systematic bias of the raw forecasts. (2) In terms of forecast accuracy, the accuracy of raw GEFSv12 precipitation forecasts is better than that of climatology at lead times of 1-7 days. The accuracy of the raw and post-processed forecasts decreases with lead times. According to the EBSS,when the lead times are up to 6 days, the accuracy of raw GEFSv12 precipitation forecasts for light rain events at some grid cells in the Huaihe River basin is lower than that of climatology. The statistical post-processing approach can improve the forecast accuracy of raw forecasts for light rain at long lead times(≥6 days). According to the ECRPSS, when the lead times are up to 7 days, the accuracy of raw GEFSv12 precipitation forecasts at some grid cells in the Huaihe River basin is lower than that of climatology. The statistical post-processing approach can improve the forecast accuracy of raw forecasts at long lead times(≥7 days). (3) In terms of forecast reliability, according to the αindex and reliability diagram, the raw GEFSv12 precipitation forecasts suffer from overestimation for light and moderate rain events. Statistical post-processing approach can significantly improve forecast reliability.The results show that the raw GEFSv12 forecasts perform well in the Huaihe River basin at lead times of 1-7 days. BJP post-processing approach can effectively reduce the systematic bias of the raw forecasts and increase forecast accuracy and reliability. Post-processed precipitation ensemble forecasts can be applied in further applications such as hydrological ensemble forecasting.
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