Abstract
Precipitation forecasts from the ECMWF model from March to September during 2015–18 were evaluated using observed precipitation at 2411 stations from the China Meteorological Administration. To eliminate the influence of varying climatology in different regions in China, the stable equitable error in probability space method was used to obtain criteria for 3- and 6-h accumulated precipitation at each station and classified precipitation into light, medium, and heavy precipitation. The model was evaluated for these categories using categorical and continuous methods. The threat score and the equitable threat score showed that the model’s forecasts of rainfall were generally more accurate at shorter lead times, and the best performance occurred in the middle and lower reaches of the Yangtze River basin. The miss ratio for heavy precipitation was higher in the northern region than in the southern region, while heavy precipitation false alarms were more frequent in southwestern China. Overall, the miss ratio and false alarm ratio for heavy precipitation were highest in northern China and western China, respectively. For light and medium precipitation, the model performed best in the middle and lower reaches of the Yangtze River basin. The model predicted too much light and medium precipitation, but too little heavy precipitation. Heavy precipitation was generally underestimated over all of China, especially in the western region of China, South China, and the Yungui Plateau. Heavy precipitation was systematically underestimated because of the resolution and the related parameterization of convection.
Significance Statement
Quantitative precipitation forecast is an important reference for operational weather forecasting. Verification of model-forecast precipitation in China is complicated because of its complex climatology. To reveal the spatiotemporal performance of the ECMWF model for 3- and 6-h precipitation forecasts in different regions of China, we defined thresholds for different rainfall categories from the cumulative precipitation at each station and evaluated the model after eliminating the influence of climatology. These verification results will help numerical model developers to improve their models and will also help forecasters have a better understanding of model predictions. Future research will focus on the accuracy of the model’s predictions under different circulation situations.
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