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气象:2016,42(4):398-405
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Logistic判别模型在强降水预报中的应用
(国家气象中心,北京 100081)
Application of the Logistic Discriminant Model in Heavy Rain Forecasting
(National Meteorological Centre, Beijing 100081)
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投稿时间:2016-01-15    修订日期:2016-02-25
中文摘要: 利用Logistic判别模型进行强降水预报,并设计3种方案进行对比分析。方案1直接使用14个影响因子进行判别预报,受因子共线性作用及噪音信号影响,虽然拟合效果较好,但预报效果明显下降。方案2对14个影响因子进行主成分分析,利用前6个主成分建模,虽然拟合效果较方案1降低,但由于消除了因子共线性作用以及噪音信号影响,预报效果较方案1提高。方案3运用Bootstrap抽样技术得到若干子样本并建模计算模型参数,打乱了原有时间序列中的波动,仅保留平稳信息,拟合自由度进一步降低,导致拟合效果较方案2下降,但预报效果却是3种方案中最好且最稳定的。在上述研究基础上,利用欧洲中心数值预报模式的预报场资料,建立基于Logistic判别模型的强降水客观预报系统,并在中央气象台业务运行。2013和2014年连续两年汛期预报检验结果表明,该模型对强降水预报的TS评分高于数值模式本身,具有一定的业务参考价值。
Abstract:The logistic discriminant model (LDM) is used in the heavy rainfall forecasting with three different schemes. In Scheme 1, 14 impact factors are imported into the model directly so that the model has high simulation skill but low forecast skill because of the colinearity effect among the factors and noise signals. In Scheme 2, the principal component analysis is applied to all the impact factors and only the first 6 leading principal components are used in building the model. Compared to Scheme 1, the simulation skill in Scheme 2 is lower but the forecast skill is higher due to the elimination of both colinearity effect and noise signals. In Scheme 3, the Bootstrap sampling technique is applied to get sub samples in order to obtain the model parameters. Thus the fluctuations in the original time series have been disturbed and only the stable information is remained. Though both the degree of freedom in the fitting and the simulation skill in this scheme are lower than in Scheme 2, the forecast skill is the highest of all the three schemes. Based on above results and by using the forecast data of ECMWF (European Centre for Medium Range Weather Forecasts), an objective LDM heavy rainfall forecasting system has been established and used in the forecasting operation at the National Meteorological Centre of China. Verification results in 2013-2014 indicate that the TS skill using the LDM scheme is generally higher than using the numerical model outputs directly.
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基金项目:中国气象局预报预测核心业务发展专项(CMAHX20160101)、国家重点基础研究发展计划(973计划)(2012CB417205)和国家自然科学基金面上项目(41175048)共同资助
引用文本:
张芳华,曹勇,徐珺,陶亦为,金荣花,代刊,2016.Logistic判别模型在强降水预报中的应用[J].气象,42(4):398-405.
ZHANG Fanghua,CAO Yong,XU Jun,TAO Yiwei,JIN Ronghua,DAI Kan,2016.Application of the Logistic Discriminant Model in Heavy Rain Forecasting[J].Meteor Mon,42(4):398-405.