基于集合预报系统的日最高和最低气温预报

摘要

BP neural network-Self Memory method (BP-SM) is used to calibrate daily 2 m maximum (minimum) air temperature forecasts at 512 stations in China with the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) in March 2016.Seven statistical characteristics used as predictors are calculated based on 2 m air temperature model output of EPS.Daily maximum (minimum) air temperature forecasts by BP-SM,BP and direct model output (DMO) are compared.The post-processing with BP-SM is shown to improve the forecast accuracy.Compared with BP method,more advantages of BP-SM method are attained in longer predictable time.The accurate rate of daily maximum (minimum)air temperature forecasts with absolute errors less than 2℃ reaches above 60％ and even over 90％ at some stations.Compared with DMO,the forecasting skill score of BP-SM is 30％ on average,and above 60％ over the eastern Tibetan Plateau.This program is obviously superior with forecast errors within 2℃C (1℃).The calibrated daily 2 m maximum air temperature is slightly better than the daily 2 m minimum air temperature.By BP-SM method,the systematic deficiencies of daily 2 m maximum (minimum) air temperature forecasts are significantly reduced.%根据欧洲中心集合预报系统2 m气温预报的集合统计值,提出了BP-SM方法,针对中国512个台站2016年3月的日最高(低)气温做预报分析.将集合预报系统的模式直接输出、BP和BP-SM方法得到的日最高(低)气温进行了比较,结果表明:预报时效越长,BP-SM方法较之BP方法的预报优势也更明显;在1至5d的预报中,BP SM方法显著降低了预报绝对误差,误差在2℃以内的准确率大部分在60％以上,部分站点达到90％;正技巧评分均值大多高于30％,在青藏高原东部和南部地区超过了60％.预报正技巧站点次数在绝对误差≤2℃(1℃)范围内有所提高,对日最高气温预报准确率的提高略好于日最低气温;BP-SM方法有效地降低了预报系统偏差,较大预报误差出现次数显著减少.