| 基于频率匹配法的SWC-WARMS模式降水订正试验 |
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| 引用本文： | 伍清,李英.基于频率匹配法的SWC-WARMS模式降水订正试验[J].高原山地气象研究,2021,41(4):70-75. |
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| 作者姓名： | 伍清 李英 |
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| 作者单位： | 中国气象局成都高原气象研究所/高原与盆地暴雨灾害四川省重点实验室, 成都 610072 |
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| 基金项目： | 高原与盆地暴雨旱涝灾害四川省重点实验室科技发展基金青年专项（SCQXKJQN2020027）；四川省中央引导地方科技发展专项（2020ZYD032）；国家自然科学基金面上项目（42075045） |
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| 摘 要： | 基于西南区域数值预报模式（SWC-WARMS）2019年5~8月00时起报的24h累计降水预报资料和四川省气象站点降水观测资料，采用频率匹配法对6月1日~8月31日降水预报值进行了偏差订正。结果表明:模式预报的24h累计降水量总体为湿偏差；订正后降水量平均绝对误差减小；大雨和暴雨的偏差评分提高；小雨、中雨、大雨的TS评分提高，暴雨TS评分降低；各量级的空报率均有所降低，小雨和中雨漏报率减小，大雨和暴雨漏报率增大，尤其是暴雨漏报率显著增加；当模式对暴雨降水落区预报较好（差）时，频率匹配订正能提高（降低）TS评分。
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| 关 键 词： | 频率匹配法 降水 偏差订正 |
| 收稿时间： | 2021-07-26 |
Precipitation Correction of SWC-WARMS Model Based on Frequency Matching Method |
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| Institution: | Institute of Plateau Meteorology, CMA, Chengdu/Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072, China |
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| Abstract: | Based on the 24-hour accumulated precipitation forecasts produced by SouthWest Center-WRF ADAS Real-time Modeling System(SWC-WARMS) initialized at 0000 UTC during the period of May 1, 2019 to August 31, 2019, along with the corresponding precipitation observation data from meteorological stations in Sichuan Province, the Frequency Matching Method (FMM) was applied to calibrate the precipitation forecasts from June 1, 2019 to August 31, 2019. Results show that the 24-hour accumulated precipitation forecasts from SWC-WARMS perform with wet bias on the whole. The averaged absolute errors of precipitation forecasts are reduced after calibration. The bias scores of heavy rain and rainstorm are improved to a large extent. In addition, the TSs of little, medium and heavy rain are improved a lot while the TS of rainstorm is inferior to the raw forecasts. The false alarm rates of all magnitudes of precipitation are decreased, the miss rates of light and medium rain are decreased, while the miss rates of heavy rain and rainstorm are increased, which is more obvious in rainstorm. The FMM can improve (decrease) the TS of raw SWC-WARMS forecast when the SWC-WARMS predicts the rainstorm precipitation area well (poorly). |
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