| 摘要: |
| 针对高分辨率数值天气预报的时空不确定性，利用邻域最优概率方法对华南区域GRAPES快速更新循环同化预报系统的24 h预报进行逐时降水订正和检验评估。结果表明:（1） 邻域法能改善模式降水预报的空间不确定性，最优邻域半径随降水等级增加而减小，强降水的最优邻域半径约为60 km；（2） 通过引入时间滞后因子，可进一步改善模式不同时间起报的不确定性，结合Brier评分确定了时间滞后窗为4 h；（3） 提出基于邻域最优概率阈值的降雨进行分级订正方法，有效提升了降水客观预报能力，晴雨预报较模式全部为正技巧，TS评分达到0.89以上，总体提升幅度约5.3%；强降水预报同样均为正技巧，TS评分呈先降后升趋势，在12 h时效前后预报效果最优，进一步提升了GRAPES快速更新循环同化预报系统的业务预报水平。 |
| 关键词: 邻域法 时间滞后 最优概率 降水预报 |
| DOI：10.16032/j.issn.1004-4965.2021.054 |
| 分类号: |
| 基金项目: |
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| CORRECTION METHOD BASED ON NEIGHBORHOOD OPTIMAL PROBABILITY FOR HOURLY PRECIPITATION FORECAST FROM GRAPES RAPID UPDATING CYCLE ASSIMILATION AND FORECASTING SYSTEM |
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LUO Cong1, SHI Yang1, WU Naigeng2,3, ZHANG Hualong1, HUANG Xiaoying1, SU Ran4
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1.Guangdong Meteorological Observatory, Guangzhou 510641, China;2. Guangdong Ecological Meteorology Center, Guangzhou 510641, China;3. Guangzhou Institute of Tropical and Marine Meteorology/ Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, CMA, Guangzhou 510641, China;4.Guangzhou Meteorological Observatory, Guangzhou 511430, China
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| Abstract: |
| To tackle the spatial-temporal uncertainty of high-resolution numerical models, in this paper, the neighborhood optimal probability method is developed to improve the hourly precipitation forecast in the 24 h forecasts from the GRAPES rapid updating cycle assimilation and forecasting system (GRAPES_GZ_R) for southern China. Results show that: (1) The spatial neighborhood probability can help reduce the spatial uncertainty of precipitation forecast from the GRAPES_GZ_R. The optimal neighborhood radius decreases with the increase of precipitation grade, and it is about 60 km for heavy rainfall prediction. (2) The time-lagged factor can help further reduce the uncertainty of forecasting initialized at different time, and the time-lagged window is determined to be 4 h by using the Brier score. (3) The precipitation classification correction method based on the optimal probability threshold of neighborhood is proposed, which effectively improves the objective precipitation forecast. Compared with previous forecasts, the present clear-rainy forecast shows all positive skills with the TS score above 0.89 and the overall improvement being about 5.3%. The heavy rain forecast also shows all positive skills; corresponding TS score decreases first and then increases, and finally achieves the best effect at about 12h, which further improves the operational application capacity of the GRAPES_GZ_R. |
| Key words: neighborhood method time-lagged optimal probability precipitation forecasts |
