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集合预报在渤海极大风预报中的应用
胡海川
(国家气象中心)
Study on the application of ensemble forecast in extreme wind prediction in Bohai Sea
huhaichuan
(National Meteorological Center)
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投稿时间:2018-06-21    修订日期:2019-03-10
中文摘要: 利用2015年2月-2018年2月地面气象常规观测中逐小时极大风及欧洲中期天气预报中心(ECMWF)集合预报中6小时极大风预报数据,选取渤海海域代表站点,对集合预报极大风产品进行预报误差特征分析。分析表明:集合预报极大风产品的离散度明显偏小于均方根误差,各个预报成员的预报结果集中与否并不能反映出预报可信度。受模式预报能力所限,无法简单的通过集合预报选取出最为可信的预报结果。集合平均、75百分位、最大值在极大风预报中各有优劣,因此基于以上三个统计量及不同量级风速发生的频率建立了渤海极大风预报客观订正方法,试验对比分析表明,该订正方法可以使极大风预报准确率有效地提高,为大风天气过程预报提供重要的参考。
中文关键词: 集合预报  极大风 订正
Abstract:By using the hourly extreme wind from the routine surface meteorological observation and the 6-hour extreme wind forecast data from ECMWF between February 2015 and February 2018, the representative stations in the Bohai Sea are selected to analyze the prediction error characteristics of the extreme wind products in the ensemble forecast. The analysis shows that the spread of the maximum wind product in ensemble forecasting is obviously smaller than the root mean square error, and whether the forecasting results of each forecasting member are concentrated or not cannot reflect the reliability of forecasting. Due to the limitation of model prediction ability, it is impossible to select the most credible prediction results simply by ensemble prediction. The ensemble average, 75 percentile and maximum ensemble forecasting have their own advantages and disadvantages in maximum wind forecasting. Therefore, based on the above three statistics and the frequency of different magnitude wind speed occurrence, an objective correction method for maximum wind forecasting in the Bohai Sea is established. The comparative analysis of experiments shows that the correction method can effectively improve the accuracy of maximum wind forecasting. The forecast of extreme wind process provides important reference.
文章编号:201806210268     中图分类号:    文献标志码:
基金项目:国家气象中心预报员专项(项目编号:201706)
引用文本:
胡海川,0.[en_title][J].Meteor Mon,():-.
huhaichuan,0.Study on the application of ensemble forecast in extreme wind prediction in Bohai Sea[J].Meteor Mon,():-.