Abstract:

It is very difficult to predict accurate temperature, especially for maximum and minimum temperature, due to the large diurnal temperature range in arid area. Based on temperature forecast products from ECMWF, T639, DOGRAFS and GRAPES models and hourly temperature observations at 105 automatic weather stations in Xinjiang during 2013-2015, two kinds of error correction and integration schemes were designed by using the decaying averaging method, ensemble average and weighted ensemble average method, the effects of error correction and integration on predicted maximum and minimum temperature in four seasons in different partitions Xinjiang were tested contrastively. The first scheme was integrating forecast temperature before correcting errors, while the second scheme was correcting forecast errors firstly and then giving an integration. The results are follows as: (1) The accuracy of temperature prediction from ECMWF model was the best in Xinjiang as a whole, while that from DOGRAFS model was the worst, and the improvement to minimum temperature prediction was higher than that of maximum temperature prediction. (2) With regarding to different partitions Xinjiang, the accuracies of predicted maximum and minimum temperature in northern Xinjiang, west region and plain areas were correspondingly higher than those in southern Xinjiang, east region and mountain areas, and the correction capability to temperature prediction in winter was higher than that in other seasons. (3) The integrated prediction of maximum and minimum temperature by weighted ensemble average method was better than that of ensemble average method. The second scheme was superior to the first scheme. (4) The improvement to maximum (minimum) temperature prediction in the extreme high (low) temperature event process from 13 to 30 July 2017 (from 22 to 24 April 2014) in Xinjiang was significant by using the second scheme.

Key words: the maximum (minimum) temperature, error correction, integration, decaying averaging method, ensemble average and weighted ensemble average

摘要：

干旱区由于气温日较差大，气温预报难度偏大，尤其是最高、最低气温预报。利用2013—2015年ECMWF、T639、DOGRAFS、GRAPES 4种模式24 h内气温预报产品，采用递减平均订正法以及集合平均和加权集合平均法，设计2种订正集成方案，即方案1是对多模式气温预报先集成后订正，方案2是先订正后集成，对新疆地区日最高气温和最低气温预报的误差订正及集成效果进行对比检验。结果表明：（1）4种模式对新疆气温预报的准确率表现为ECMWF模式整体最好，DOGRAFS模式最差，且最低气温的预报准确率提高程度高于最高气温；（2）对于新疆不同区域，最高（低）气温预报准确率北疆高于南疆，西部高于东部，平原高于山区，且冬季的订正能力大于其他季节；（3）加权集合平均法优于集合平均法，先订正后集合方案优于先集合后订正方案；（4）方案2对2015年7月13—30日和2014年4月22—24日两次极端高、低温天气过程的最高（低）气温订正效果明显。

关键词: 最高（低）气温, 误差订正, 集成, 递减平均法, 集合平均和加权集合平均