乡镇精细化最高最低气温预报方法研究

A refined forecast method of township minimum and maximum air temperature

对2008年10月至2009年9月安徽省乡镇观测点与县观测站最低、最高气温的差异进行统计分析。结果表明:乡镇观测点与县观测站之间的最低、最高气温具有明显差异,且随着季节变化而不同。在此基础上,以预报员主观制作的县站最高、最低气温预报结果作为基础,利用修正Barnes插值和一阶卡尔曼滤波订正方法制作乡镇观测点的最低、最高气温预报表明,该方法制作的乡镇站点最低、最高气温具有较高准确率,前3 d的乡镇最低气温预报准确率与县站预报比较接近,预报准确率差异在1%之内,但随着预报时效的增加两者的差异略有增加;对于乡镇的最高气温预报,与县站最高气温预报小于2℃的准确率保持在3%之内,该方法优于基于WRF模式的MOS方法。对比一阶卡尔曼滤波订正前后效果发现,该方法对乡镇最低和最高气温的前4 d预报具有正订正效果,而对于第5—7 d没有订正效果或为负订正效果。当区分转折性天气后,可以提高第4—7 d的最高气温预报准确率。

Tbe differences of minimum and maximum air temperature between weather stations located in county and township of Anhui province were analyzed from October of 2008 to September of 2009. The results show that both difference is significant and it changes with the seasons. According to the forecast product of forecaster and the methods of the revised Barnes as well as the first-order Kalman filter,it forecasts the minimum and maximum air temperature in township. It suggests that the prediction accuracy rates are high using this method. The accuracy rates of the minimum temperature in the first 3 days are close to that in county and both differences are only within 1%, while the differences increase with the increasing of valid forecast time. For the maximum temperature,the ac- curacy rates that the forecast error is within 2℃ are still within 3 % compared with the forecast product in county. This method is better than the MOS method based on output products of the WRF model. The effects of the first- order Kalman filter after and before the revise show that the Kalman filter has a positive effect for township tem- perature in the first four days, while it has no effect or a negative effect from the fifth to seventh day. The forecast accuracy rates of the maximum temperature from the fourth to seventh days could be significantly improved after the turning synoptic processes are discriminated.