Research on Error Correction and Integration Methods of Maximum and Minimum Temperature Forecasts Based on a Multimodel in Xinjiang

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 the 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 fore 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 predictions 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 predictions 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 sbheme.