| 湖南6月区域持续性暴雨概率预报模型及应用 |
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| 引用本文： | 李易芝,罗伯良,彭莉莉,张超,彭晶晶.湖南6月区域持续性暴雨概率预报模型及应用[J].气象,2023,49(11):1384-1395. |
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| 作者姓名： | 李易芝 罗伯良 彭莉莉 张超 彭晶晶 |
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| 作者单位： | 湖南省气象科学研究所，长沙 410118; 气象防灾减灾湖南省重点实验室，长沙 410118;长沙市气象局，长沙 410205 |
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| 基金项目： | 湖南省自然科学基金项目(2019JJ50318)资助 |
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| 摘 要： | 利用1979—2016年6月EAR5再分析资料,选取湿热力平流参数、热力螺旋度、散度通量、水汽散度通量和热力波作用密度5个综合因子,采用核密度估计方法,基于TS评分最优为检验标准筛选确立最优因子和权重组合,构建了湖南区域持续性暴雨概率预报模型,并进行了独立样本检验与业务试用。结果表明:2017—2019年独立样本回代检验,平均TS评分达到29.9%,相比于欧洲中期天气预报中心(ECMWF)细网格(平均TS评分为22.4%)为正技巧。在2021年、2022年汛期两次区域持续性暴雨个例的预报试验中,提前24 h的暴雨预报优于ECMWF、CMA-GFS等大尺度模式和CMA-SH、CMA-GD等区域中尺度模式,对湖南区域持续性暴雨有较强的预报能力。
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| 关 键 词： | 核密度估计 概率预报 区域持续性暴雨 湖南 |
| 收稿时间： | 2022/11/1 0:00:00 |
| 修稿时间： | 2023/6/19 0:00:00 |
Probabilistic Forecasting Model of Regional Persistent Rainstorm in June in Hunan and Its Application |
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| LI Yizhi,LUO Bailiang,PENG Lili,ZHANG Chao,PENG Jingjing.Probabilistic Forecasting Model of Regional Persistent Rainstorm in June in Hunan and Its Application[J].Meteorological Monthly,2023,49(11):1384-1395. |
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| Authors: | LI Yizhi LUO Bailiang PENG Lili ZHANG Chao PENG Jingjing |
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| Institution: | Hunan Institute of Meteorological Sciencees, Changsha 410118; Hunan Key Laboratory of Meteorological Disaster Prevention and Mitigation, Changsha 410118; Changsha Meteorological Bureau, Changsha 410205 |
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| Abstract: | Based on the EAR5 reanalysis data in June from 1979 to 2016, the moist thermodynamic advection parameter, thermal helicity, divergence flux, moisture divergence flux and the thermodynamic wave-activity density are selected as five comprehensive factors. The probability prediction model of regional persistent rainstorm in Hunan is constructed by the means of nuclear density estimation and based on the optimal factor and weight combination which is established with the best TS score as the test standard. The results show that the average TS of independent samples from 2017 to 2019 reaches 29.9%, which is a positive skill relative to the European Centre for Medium-Range Weather Forecasts (ECMWF) fine grid forecast (with an average TS score of 22.4%). During the two regional persistent rainstorm operational experiments in the 2021 and 2022 flood seasons, the rainstorm forecast with a 24 h leadtime by the Hunan regional perisistent rainstorm probability prediction model is superior to the forecasts of ECMWF and CMA-GFS large-scale model as well as CMA-SH and CMA-GD regional mesoscale model. Therefore, the Hunan regional persistent rainstorm prbability prediction model has a strong ability to forecast the regional persistent rainstorm in Hunan. |
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| Keywords: | nuclear density estimation probabilistic forecasting regional persistent rainstorm Hunan |
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