ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 45 Issue 3 > 2019,45(3):305-317. DOI:10.7519/j.issn.1000-0526.2019.03.002 Prev Next
Improvement of Hourly Precipitation Forecast Using a TimeLagged Ensemble Based on Rapid Refresh Assimilation and Forecast
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Abstract:
The formation and development of shortterm heavy precipitation caused by small and mesoscale convective systems are very rapid, and the prediction and warning of the location and period of precipitation are always difficult in forecasting operation. In recent years, the accuracy and resolution of mesoscale model have been improved, and it plays more and more important role in forecasting and warning of severe convective weather. In this paper, based on hourly precipitation forecast from Rapid Analysis and Forecasting System GRAPESRAFS (0.1°×0.1°) during August-September 2017, ensemble members are formed by the timelagged ensemble forecast method. The average TS score is used to calculate weight coefficients of corresponding ensemble members, and then frequency matching method is adopted to correct precipitation forecast bias. The conclusions drawn from this study are as follows. (1) For GRAPESRAFS, the most accurate precipitation forecast does not always come from the most recent ensemble member. Timelagged ensemble method can significantly improve the prediction ability of the model by automatically identifying the optimal forecast members. (2) The GRAPESRAFS hourly precipitation forecast presents systematic weak biases. After corrected by the frequency matching method, the hourly precipitation forecast gets more close to the actual situation in magnitude. (3) The timelagged ensemble method works better for central and eastern parts of China. (4) The frequency matching method works better for south of the Yangtze River, South China and Southwest China, where precipitation occurs more frequently with greater intensities. (5) The method can significantly improve the prediction capacity of the model for the location, amount and patterns of rainfalls in severe precipitation process of small and medium scales.
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