基于EOF分解的中期平均氣溫距平客觀預報方法

doi:10.3969/2016jms.0060

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基于EOF分解的中期平均氣溫距平客觀預報方法
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                        10.3969/2016jms.0060
                    
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基金項目:國家重點研發(fā)計劃課題（2016YFC0203301）；國家科技支撐計劃項目（2015BAC03B06，2014BAC16B02）

Objective forecast of medium-term average temperature anomalies based on EOF technique

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本文通過將500 hPa高度、850 hPa溫度、海平面氣壓的預報與歷史要素場進行EOF分解，并假定EOF分解空間函數（即特征向量）是基本穩(wěn)定的，建立以EOF分解主分量系列的時間系數、預報站點附近基本要素值的時間序列與預報站點平均氣溫距平的多元線性逐步回歸預報方程，結合ECMWF集合預報數值產品，生成全國范圍未來10 d平均氣溫距平客觀預報產品投入業(yè)務應用，并采用同號率方法檢驗。結果表明，客觀預報產品在檢驗時段內，同號率平均值為0.77，明顯高于ECMWF集合預報模式2 m溫度直接計算得出的同類產品同號率0.63；該客觀預報產品不僅在分布范圍上，同時在距平幅度上預報效果均較好。此外，相關分析也證明同號率方法在檢驗中期時效氣溫距平預報中有其合理性。

Abstract:

Based on the EOF decomposing of 500 hPa height, 850 hPa temperature, sea level pressure field forecasts with historical elements, and assuming that the EOF spatial functions (feature vectors) are basically stable, the time function based on EOF principal components series, multiple linear regression prediction equation of time sequence of basic element value and the average temperature anomaly near and at the forecasting and prediction sites were established, moreover, combining with the ECMWF ensemble prediction products, the objective forecasting products with nationwide medium term average temperature anomaly were generated and applied into work, which was verified by using the same symbol ratio. Results show that the objective forecasting products in the test period has 0.77 symbol ratio, significantly higher than the same symbol ratio (0.63) of the similar products directly calculated by ECMWF 2 m temperature products; the objective forecasting products perform well not only in the distribution scope, but in the anomaly amplitude effects. In addition, the correlation analysis also proved that the same symbol ratio method is reasonable in the medium-term temperature anomaly forecast verification.

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黃威,張恒德,鮑媛媛,康志明.基于EOF分解的中期平均氣溫距平客觀預報方法.氣象科學,2017,37(4):561-566 HUANG Wei, ZHANG Hengde, BAO Yuanyuan, KANG Zhiming. Objective forecast of medium-term average temperature anomalies based on EOF technique. Journal of the Meteorological Sciences,2017,37(4):561-566

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收稿日期:2016-05-09
最后修改日期:2016-08-08
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在線發(fā)布日期: 2017-09-20
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