极端气温集成预报方法对比

用2003-2009年ECMWF和庆阳市极端气温资料建立最高最低气温SVM、Kalman、多元线性回归3种统计方法的预报模型,采用平均、加权、回归3种方法进行预报集成,对庆阳市2010年6-12月各预报方法及5个时次集成预报进行评估.结果表明:单一的SVM、多元回归和集成方法最低气温预报5个时次的准确率均高于最高气温0.8％～24.2％,集成后加权法准确率最高,但最高和最低气温选取权重不同,SVM权重大时最高气温效果好,多元回归权重大时最低气温效果好.随着预报时效的增加,单一的预报方法和集成预报,预报准确率降低.逐月评估表明,单一的SVM准确率较高且预报性能稳定,Kalman准确率较低,回归方法各月差异大,预报不稳定,集成后,3种集成方法的预报比单一的预报方法均有所改善和提高.绝对误差分析表明,加权集成后最高和最低气温误差都较小,优于平均集成法和回归集成法.

Comparitive Analysis of Consensus Forecast for Extreme Temperature

The statistical models of SVM, Kalman and multi-dimensional linear regression are established for extreme temperature with the ECMWF （European Centre for Medium-Range Weather Forecasts） grid data and the observation data of Qingyan from 2003 to 2009. The methods of average, weighting, and regression are used in forecast integration. The integrated forecasts indicate that the accuracies of minimum temperature are 0.8% to 24.2% higher than those of maximum temperature by means of SVM, multiple regression and integrated methods at various time from June to December 2010. The weighting method is the best, then the integration, but the weight is different for maximum and minimum temperature： the accuracy of maximum temperature is better when the SVM weight is greater, and the accuracy of minimum temperature is better when the weight of multiple regression is greater. The forecasting accuracy decreases with increasing led time for both single and consensus forecast methods. The month-to-month verification indicates that the accuracy of the single SVM method is relatively high and stable; that of the Kalman is relatively low; and that of the regression is unstably. The integrated results of three methods show improving, and the absolute errors of both maximum and minimum temperature after weighting integration are small, better than those of the average and regression methods.