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投稿时间：2022-10-10 修订日期：2023-06-20
投稿时间：2022-10-10 修订日期：2023-06-20
中文摘要: 为提高精细化网格降水的实际预报能力,评估了2021年汛期ECMWF（EC）、CMA-MESO、SXWRF和SCMOC降水预报产品在陕西的表现，讨论了卡尔曼动态频率匹配方法对不同模式的订正效果，然后针对该方法不足，基于最优TS评分阈值法和SCMOC在天气过程判定中占优信息对小量级降水进行了二次订正，最后利用分类降水过程建模和基于图像相似识别技术改进的卡尔曼动态频率匹配法对暴雨进行了订正研究。结果表明：SCMOC晴雨预报准确率和暴雨TS评分均最高，分别为81.60%和0.30，表现最好；卡尔曼动态频率匹配法可明显提高EC、CMA-MESO和SXWRF模式降水预报产品晴雨预报准确率，对暴雨预报的改善效果不稳定，对EC晴雨预报准确率和暴雨TS评分提升幅度均最大，分别为6.35%和6.99%，该订正方法更适合于EC模式；经晴雨消空二次订正后的EC模式晴雨和小雨预报准确率较一次订正后的EC模式均有提高，分别提高了0.51%和0.64%；分类降水过程建模订正可进一步提高EC暴雨TS评分，较未分类过程订正后的暴雨TS评分提高了1.05%，且暴雨其他评分指标也均变好；改进后的卡尔曼动态频率匹配法较改进前可进一步提高EC各量级降水TS评分，尤其是暴雨TS评分提高了2.79%。
Abstract:In order to improve the practical prediction ability of refined grid precipitation, the performance of ECMWF （EC）, CMA-MESO, SXWRF and SCMOC precipitation forecast products in Shaanxi Province during the rainy season of 2021 are compared and evaluated, and the correction effect of Kalman dynamic frequency matching method on different models is discussed. Then, for the shortcomings of this method, based on the optimal TS scoring threshold method and SCMOC’s judgment of weather process, the small-magnitude
precipitation is revised for the second time. Finally, the heavy precipitation is revised by using the precipitation sub process modeling and the improved Kalman dynamic frequency matching method which is based on image similarity recognition technology. The results show that SCMOC has the highest accuracy of sunny and rainy forecast and the highest TS score of heavy precipitation, which are 81.60% and 0.30 respectively. The Kalman dynamic frequency matching method can significantly improve the accuracy of sunny and rainy forecast of EC, CMA-MESO and SXWRF precipitation forecast products, but the improvement effect of heavy rainfall forecast is unstable. The improvement ranges of the EC model sunny and rainy forecast accuracy and the TS score of rainstorm forecast are the largest, 6.35% and 6.99% respectively. This correction method is more suitable for EC model. Compared with the EC model modified by Kalman dynamic frequency matching method, the accuracy of sunny rain and light rain prediction of EC model after the second correction of sunny rain spaced elimination is improved by 0.51% and 0.64%, respectively. The correction of the precipitation sub process modeling can further improve the TS score of EC model heavy precipitation, which is 1.05% higher than the TS score of heavy precipitation without sub process correction. Other scoring indicators of heavy precipitation are also better. The improved Kalman dynamic frequency matching method can significantly further improve the TS score of EC precipitation of all magnitudes, especially the TS score of heavy precipitation, improved by 2.79%.
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基金项目:中国气象局创新发展专项（CXFZ2022J023、CXFZ2023J031）、陕西省自然科学基础研究计划（2022JQ-249、2023-JC-YB-283）、秦岭和黄土高原生态环境气象重点实验室开放研究基金课题（2023G-2）和中国气象局复盘总结专项（FPZJ2023-129）共同资助
| 作者 | 单位 |
| 高星星 | 陕西省气象台，西安 710014 陕西省气象局秦岭和黄土高原生态环境气象重点实验室，西安 710014 |
| 潘留杰 | 陕西省气象台，西安 710014 陕西省气象局秦岭和黄土高原生态环境气象重点实验室，西安 710014 |
| 娄盼星 | 陕西省气象局秦岭和黄土高原生态环境气象重点实验室，西安 710014 陕西省气象科学研究所，西安 710014 |
| 杜莉丽 | 陕西省气象台，西安 710014 |
引用文本：
高星星,潘留杰,娄盼星,杜莉丽,2023.一种改进的频率匹配法在网格降水预报订正中的应用[J].气象,49(11):1371-1383.
GAO Xingxing,PAN Liujie,LOU Panxing,DU Lili,2023.Application of an Improved Frequency Matching Method in Grid Precipitation Forecast Correction[J].Meteor Mon,49(11):1371-1383.
高星星,潘留杰,娄盼星,杜莉丽,2023.一种改进的频率匹配法在网格降水预报订正中的应用[J].气象,49(11):1371-1383.
GAO Xingxing,PAN Liujie,LOU Panxing,DU Lili,2023.Application of an Improved Frequency Matching Method in Grid Precipitation Forecast Correction[J].Meteor Mon,49(11):1371-1383.
