熊敏诠. 2017. 基于集合预报系统的日最高和最低气温预报[J]. 气象学报, 75(2):211-222, doi:10.11676/qxxb2017.023
基于集合预报系统的日最高和最低气温预报
Calibrating daily 2 m maximum and minimum air temperature forecasts in the ensemble prediction system
投稿时间:2016-08-25  修订日期:2016-12-26
DOI:10.11676/qxxb2017.023
中文关键词:  集合预报系统  日最高气温  日最低气温  BP-SM方法
英文关键词:Ensemble prediction system  Daily maximum air temperature  Daily minimum air temperature  BP neural network-Self Memory method
基金项目:发展格点预报融合和订正关键技术(YBGJXM201703)。
作者单位
熊敏诠 国家气象中心, 北京, 100081 
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中文摘要:
      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5 d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。
英文摘要:
      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℃(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.
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