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四川地区精细化降水预报融合订正试验及检验

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精细化定量降水网格预报是天气预报业务的重点和难点,基于西南区域智能数值网格模式预报系统(Southwest China WRF-based Intelligent Numeric Grid forecast System,SWC-WINGS)1 km×1 km分辨率的小时降水预报,利用时间滞后和概率匹配方法开展融合订正试验,再利用中国气象局多源融合降水系统(CMA Multi-source Precipitation Analysis System,CMPAS)三源融合降水实况格点数据,对2022年7-8月四川地区的小时降水预报融合订正结果进行检验,并在四川盆地西部一次短时强降水天气过程中进行应用,结果表明:(1)时间滞后集合降水预报相较于模式降水预报,存在小量级预报过度,大量级预报过于保守的问题;(2)时间滞后结合概率匹配的降水预报融合订正方法有效提升了各量级降水预报的TS评分,尤其1～2 h预报时效提升显著,小时雨量超过0.1 mm、5 mm、10 mm和20 mm量级的TS评分平均提升率分别为7.2%、17.2%、28.3%和36.3%;(3)一次短时强降水天气过程的应用结果表明,时间滞后结合概率匹配的融合订正方法对模式小时降水预报有较好的改进效果,尤其对大量级降水预报有较强的订正能力.
Experiment and verification of fine gridded precipitation forecast fusion correction in Sichuan
Fine-scale quantitative precipitation forecast is a key issue and challenge in weather forecasting services.In this study,based on hourly precipitation from the 1 km×1 km resolution Southwest China WRF-based Intelligent Numeric Grid forecast System(SWC-WINGS),a fusion-corrected experiment was conducted using time lag and probability matching methods.The fusion-corrected forecast of hourly pre-cipitation was then verified utilizing the CMA Multi-source Precipitation Analysis System(CMAPS)three-source merged precipitation obser-vation grid data from 1 July to 31 August 2022 in Sichuan.Finally,the fusion-corrected method was applied to a short-term heavy precipita-tion process over the western Sichuan Basin.The results show that:(1)Compared with the model precipitation forecasts,the time-lagged en-semble forecast was over-optimistic for small-scale precipitation and over-conservative for large-scale precipitation.(2)However,the fu-sion-corrected method by time lag and probability matching methods overcame the above difficulties and showed significant improvement in the TS score,particularly in 1～2 h nowcast time.The TS score for hourly precipitation exceeding 0.1 mm,5 mm,10 mm,and 20 mm were in-creased on average by 7.2%,17.2%,28.3%,and 36.3%,respectively.(3)A case studies also showed that the fusion-corrected method had good improvement and correction capabilities on the hourly precipitation forecast,especially for large-scale precipitation forecasts.

SWC-WINGS modelprobability matchingtime lagfusion correction

张武龙、陈朝平、杨康权

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四川省气象台,成都 610072

高原与盆地暴雨旱涝灾害四川省重点实验室,成都 610072

SWC-WINGS模式 概率匹配 时间滞后 融合订正

国家重点研发计划项目四川智能网格预报创新团队项目中国气象局气象能力提升联合研究项目重点专项四川省重点实验室项目四川省重点实验室项目四川省重点研发项目四川省重点研发项目西南区域创新团队项目中国气象局创新发展专项中国气象局创新发展专项

2021YFC3000900SCQXCXTD-20220122NLTSZ006SCQXKJZD202101SCQXKJYJXMS2021122022YFS05422022YFS0540XNQYCXTD-202202CXFZ2023J016CXFZ2024J013

2024

10.12406/byzh.2023-077

暴雨灾害
中国气象局武汉暴雨研究所

暴雨灾害

CSTPCD
影响因子：1.533
ISSN：1004-9045
年,卷(期)：2024.43(2)
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