銀川河?xùn)|機(jī)場(chǎng)小樣本雷暴分類客觀預(yù)報(bào)方法研究

doi:10.3969/2019jms.0010

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銀川河?xùn)|機(jī)場(chǎng)小樣本雷暴分類客觀預(yù)報(bào)方法研究
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                        10.3969/2019jms.0010
                    
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基金項(xiàng)目:四川省基礎(chǔ)應(yīng)用研究計(jì)劃重點(diǎn)項(xiàng)目（2018JY0056）；中國(guó)氣象局預(yù)報(bào)員專項(xiàng)（CMAYBY2018-083）；西北空管局《銀川河?xùn)|機(jī)場(chǎng)雷暴生消規(guī)律及其個(gè)例的數(shù)值模擬研究》資助

The variation characteristics of summer precipitation in Jiangsu Province during the last 50 years and its relationship with ENSO

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利用2000-2016年歐洲中心再分析資料、探空及地面自動(dòng)氣象站觀測(cè)資料，根據(jù)天氣過(guò)程的強(qiáng)度和對(duì)應(yīng)物理量，分別對(duì)銀川河?xùn)|機(jī)場(chǎng)雷暴伴隨大風(fēng)、降水等不同天氣現(xiàn)象類別進(jìn)行定量化轉(zhuǎn)換，采用峰度偏度系數(shù)、χ²以及Q-Q圖3種方法對(duì)定量轉(zhuǎn)換的數(shù)據(jù)進(jìn)行正態(tài)性檢驗(yàn)，結(jié)果表明：按天氣現(xiàn)象分類的樣本服從正態(tài)分布，未分類樣本基本服從。利用逐步回歸、多元回歸、非線性回歸、BP人工神經(jīng)元網(wǎng)絡(luò)以及支持向量機(jī)5種方法，分別建立了雷暴現(xiàn)象與強(qiáng)度預(yù)報(bào)模型。結(jié)果表明：BP網(wǎng)絡(luò)以及SVM對(duì)天氣現(xiàn)象的預(yù)報(bào)能力較強(qiáng)；分類逐步、多元以及非線性回歸模型分別對(duì)弱雨、強(qiáng)雨以及大風(fēng)和降雨同時(shí)發(fā)生的天氣強(qiáng)度預(yù)報(bào)效果較好。并在此基礎(chǔ)上通過(guò)最優(yōu)分析設(shè)計(jì)了河?xùn)|機(jī)場(chǎng)不同種類雷暴天氣定性和定量預(yù)報(bào)相結(jié)合的業(yè)務(wù)系統(tǒng)。

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Based on the ECMWF reanalysis data and AWS data from 2000 to 2016, the intensity of the weather process and the corresponding physical field, and quantitative transformation of thunderstorm cases with different weather phenomena such as gale and precipitation in Yinchuan Hedong International Airport, the normality tests were conducted on the quantitatively transformed data by using the coefficients of Kurtosis and Skewness, Chi-square and Q-Q plot. The results showed that the samples classified by weather phenomena passed normality, and the unclassified samples basically obeyed normality distribution. Thunderstorms phenomenon and intensity prediction models were developed in five ways of stepwise regression, multiple regression, nonlinear regression, BP artificial neural network and Support Vector Machine(SVM). The forecast results showed that the BP artificial neural network and SVM had strong forecasting ability for weather phenomena, and the classified stepwise, multivariate and nonlinear regression models showed better effects on the intensity of weak weather, heavy rain, simultaneous occurrence with gale and rainfall forecasting. Based on the analysis and research above, an operational platform system combining qualitative and quantitative prediction of different types of thunderstorms in Yinchuan Hedong International Airport was established.

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谷思雨,胡文東,彭維耿,朱冬梅,胡亮帆,丁禹欽.銀川河?xùn)|機(jī)場(chǎng)小樣本雷暴分類客觀預(yù)報(bào)方法研究.氣象科學(xué),2019,39(6):763-774 GU Siyu, HU Wendong, PENG Weigeng, ZHU Dongmei, HU Liangfan, DING Yuqin. The variation characteristics of summer precipitation in Jiangsu Province during the last 50 years and its relationship with ENSO. Journal of the Meteorological Sciences,2019,39(6):763-774

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收稿日期:2018-07-12
最后修改日期:2018-12-20
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在線發(fā)布日期: 2020-01-07
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