Moving Average of Optimal Time-Window Method For 2 m Temperature Forecast Correction of GRAPES-GFS
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Abstract:
Using GRAPESGFS forecast data and temperature observation data of Guangxi regional automatic weather stations during 2017-2018, errors of the 2 m temperature forecast of the GRAPESGFS model over Guangxi are analyzed. It is found that the 2 m temperature forecast of the GRAPESGFS model is lower than the observation in Guangxi. Forecast errors increase with the forecast time and regularly appear in the mountain areas in the northern Guangxi, Zuojiang, Youjiang river valley, and coastal areas. The temperature forecast error at noon is systematic in spring, summer and autumn but the errors at noon in winter and that at night in all seasons are random. To develop the optimal timewindow of the moving average method, we compare different moving average solutions with the unfixed timewindows and verify its improvement with the trial correction products of the optimal timewindow moving average method during 2020. Results show that the moving average solutions of fixed timewindow, optimal seasonal timewindow, and monthly optimal time window are all effective in spring, summer, and autumn. The correction skill is higher at noon than that at night. Among all the solutions, fixed long timewindow (15 to 60 d) solution, seasonal optimal timewindow solution and monthly optimal timewindow solution are more effective. Running optimal timewindow method based on different moving average solutions can steadily improve the 2 m temperature forecast.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.