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清江流域降水的多模式BMA概率预报试验
(1.中国气象局武汉暴雨研究所暴雨监测预警湖北省重点实验室;2.南京信息工程大学气象灾害教育部重点实验室;3.武汉区域气候中心;4.武汉农业气象试验站)
Probabilistic Forecasting of the Precipitation over the Qingjiang River Basin Using BMA Multimodel Ensemble Technique
QI Haixia1, pengtao1, linchunze2, pengting3,4,5, jiluying3,4,5, lilan6, 孟翠丽7
(1.Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain;2.Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration;3.Key Laboratory of Meteorological Disaster,Ministry of Education(KLME) /Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD),Nanjing University of Information Science&4.amp;5.Technology;6.Wuhan Regional Climate Center;7.Wuhan National Agricultural Meteorology Station)
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投稿时间:2018-08-22    修订日期:2019-11-05
中文摘要: 基于TIGGE资料中的ECMWF、UKMO、JMA、CMA 四个中心2016年6月1-7月31日逐日降水集合预报资料,结合清江流域10个国家基准站观测数据,建立了流域贝叶斯模型平均(Bayesian Model Averaging,BMA)概率预报模型,开展流域多模式集合BMA技术的概率预报试验与评估。结果表明,在清江流域多模式集合的BMA模型最佳滑动训练期长度为40天,BMA模型预报比原始集合预报有更高预报技巧,比四个原始集合预报MAE平均值减少近11%左右,而对于CRPS除了CMA中心无订正效果外,较其他三个模式平均值提高近15%左右。多模式集合BMA技术能预报降水全概率PDF曲线和大于某个降水量级的概率,同时能给出确定性降水预报,对于极端强降水(大暴雨-特大暴雨量级),BMA 75-90百分位数预报效果较好,对于强降水(暴雨量级),BMA 50-75百分位数预报效果较好,对于一般性降水(小雨-大雨量级),BMA确定性预报结果或50百分位数预报效果较好。
Abstract:ased on the TIGGE datasets including the European Centre for Medium Range Weather Forecasts(ECMWF),the United Kingdom Met Office(UKMO), the China Meteorological Administration (CMA), and the Japan Meteorological Agency (JMA),and its multi-center ensemble systems,and observations in the Qingjiang basin,Bayesian model averaging (BMA) probability forecast models were established.The results show that the optimal length of the training period is about 40 days, and the BMA models for multi- center ensemble outperform those for single center system for lead times of 24h.The mean absolute error (MAE) and continuous ranked probability score(CRPS) skills of the BMA models were improved approximately 11% and 15%, respectively,compared with those of raw ensemble forecasts. In business operation, when the BMA90 percentile site predicted precipitation to be extreme precipitation, the 75-90 percentile site predicted precipitation could be used as the forecast reference, and the heavy precipitation warning could be carried out. For the forecast of heavy precipitation, the forecast result of the 50 to 75 percent site predicted by BMA can be taken as a reference, while for the general precipitation, the reference of BMA deterministic forecast is relatively strong.BMA probability forecast could give both the PDF curve with full probability and the probability greater than a certain precipitation level, which could provide the basis for the probability forecast in the business. However, it is also limited, and the small probability value is often ignored, resulting in omission. So how to capture more useful information through the probabilistic prediction method and increase the accuracy of the prediction of extreme weather events will be a challenge for the probabilistic prediction technology.
文章编号:201808220375     中图分类号:    文献标志码:
基金项目:国家自然科学基金项目
引用文本:
祁海霞,彭涛,林春泽,彭婷,吉璐莹,李兰,孟翠丽,0.[en_title][J].Meteor Mon,():-.
QI Haixia,pengtao,linchunze,pengting,jiluying,lilan,孟翠丽,0.Probabilistic Forecasting of the Precipitation over the Qingjiang River Basin Using BMA Multimodel Ensemble Technique[J].Meteor Mon,():-.