基于土壤濕度和年際增量方法的中國夏季氣溫預(yù)測試驗(yàn)

doi:10.3969/2015jms.0060

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基于土壤濕度和年際增量方法的中國夏季氣溫預(yù)測試驗(yàn)
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                        10.3969/2015jms.0060
                    
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基金項(xiàng)目:國家自然科學(xué)基金資助項(xiàng)目（41230422）；公益性行業(yè)（氣象）科研專項(xiàng)（GYHY201206017）；江蘇省自然科學(xué)基金-杰出青年基金項(xiàng)目（BK20130047）；新世紀(jì)優(yōu)秀人才支持計(jì)劃

Prediction of summer temperature in China based on soil moisture and interannual increment approach

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本文利用中國160站月平均氣溫資料和歐洲中心ERA-Interim逐月再分析表層土壤濕度資料，通過相關(guān)分析選取歐亞大陸9個(gè)關(guān)鍵區(qū)的土壤濕度年際增量作為預(yù)測因子，采用變形的典型相關(guān)分析（BP-CCA）結(jié)合集合典型相關(guān)分析（ECC）的方法建立集合預(yù)測模型，對(duì)我國東部夏季氣溫年際增量進(jìn)行預(yù)測，進(jìn)而預(yù)測夏季氣溫。其中，1980-2004年的資料用于歷史擬合試驗(yàn)，而2005-2014年的資料用于獨(dú)立樣本預(yù)測試驗(yàn)。首先利用BP-CCA方法對(duì)9個(gè)因子分別建立單因子預(yù)測模型，然后采用ECC方法對(duì)9個(gè)預(yù)測因子按照不同的組合方式建立集合預(yù)測模型，并且分析預(yù)測技巧。結(jié)果表明，不同預(yù)測因子的組合對(duì)我國夏季氣溫的預(yù)測能力不同：勒拿河下游地區(qū)、中國黃河以南地區(qū)、葉尼塞河下游地區(qū)、西西伯利亞平原地區(qū)以及印度半島西北部地區(qū)的土壤濕度對(duì)華北夏季氣溫預(yù)測效果較好；中國黃河以南地區(qū)、葉尼塞河下游地區(qū)、印度半島西北部地區(qū)、貝加爾湖東北地區(qū)以及貝加爾湖以西地區(qū)的土壤濕度對(duì)江淮夏季氣溫有較高預(yù)測技巧。所建立的兩組集合預(yù)測模型均顯示了較好的實(shí)際預(yù)測能力：華北氣溫預(yù)測模型預(yù)測氣溫距平的同號(hào)率為8/10，平均均方根誤差為3.4%；江淮氣溫預(yù)測模型預(yù)測氣溫距平的同號(hào)率為7/10，平均均方根誤差為2.7%。并且兩組模型預(yù)測出的華北和江淮氣溫的預(yù)測評(píng)分（PS）均超過80分，而國際上通用的距平相關(guān)系數(shù)（ACC）均在0.3以上。這說明土壤濕度因子中包含對(duì)我國夏季氣溫有用的預(yù)測信號(hào)，可以考慮將土壤濕度應(yīng)用于夏季氣溫預(yù)測業(yè)務(wù)中。

Abstract:

Based on the monthly Surface Air Temperature (SAT) of 160 stations in China and the ERA-Interim monthly surface soil moisture reanalysis data from ECMWF, the yearly increment of soil moisture of nine key regions over Eurasia was selected as the predictors through correlation analysis, the Barnett-Preisendorfer Canonical Correlation Analysis (BP-CCA) combining the Ensemble Canonical Correlation analysis (ECC) method were used to develop the ensemble prediction model in order to predict the yearly increment of summer SAT over eastern China, and finally carry out the prediction of summer SAT. The data during 1980-2004 and 2005-2014 are used to perform the historical prediction test and the independent sample test, respectively. Firstly the single factor prediction models of nine predictors were set up with the BP-CCA method, then the ensemble prediction models with the ECC method based on different combinations of the nine predictors were developed, and the predictive skills are also analyzed. Results show that the combinations of different predictors have different predictive skills for summer SAT in China. The soil moisture in the lower reaches of the Lena River, south of the Yellow River, lower reaches of the Yenisei River, West Siberian Plain, and Northwest of the Indian Peninsula, have good predictive effect for summer SAT in North China. The soil moisture in the South of the Yellow River, lower reaches of the Yenisei River, Northwest of the Indian Peninsula, Northeast of the Baikal Lake, and West of the Baikal Lake, have good predictive effect for summer SAT in the Yangtze-Huaihe region. Both of the two ensemble prediction models show good actual predictive skills. The coherence rate between the observed and the predicted SAT anomaly over North China and Yangtze-Huaihe region, is 8/10,7/10, and the average relative root mean square error is 3.4%, 2.7%, respectively. Prediction Scores (PS) of SAT in North China and the Yangtze-Huaihe region by the two models all exceed 80 points, and the generally used anomaly correlation coefficients (ACC) all exceed 0.3, which means that soil moisture implies useful signals for summer SAT in China, and soil moisture can be considered to apply in the summer SAT prediction operations.

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李啟芬,劉婷婷,陳海山,蔣薇,李忠賢.基于土壤濕度和年際增量方法的中國夏季氣溫預(yù)測試驗(yàn).氣象科學(xué),2016,36(5):629-638 LI Qifen, LIU Tingting, CHEN Haishan, JIANG Wei, LI Zhongxian. Prediction of summer temperature in China based on soil moisture and interannual increment approach. Journal of the Meteorological Sciences,2016,36(5):629-638

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收稿日期:2015-05-07
最后修改日期:2015-07-30
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在線發(fā)布日期: 2016-11-01
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