| 基于集合预报系统的日最高和最低气温预报 |
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| 引用本文： | 熊敏诠.基于集合预报系统的日最高和最低气温预报[J].气象学报,2017,75(2):211-222. |
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| 作者姓名： | 熊敏诠 |
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| 作者单位： | 国家气象中心, 北京, 100081 |
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| 基金项目： | 发展格点预报融合和订正关键技术（YBGJXM201703）。 |
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| 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。
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| 关 键 词： | 集合预报系统 日最高气温 日最低气温 BP-SM方法 |
| 收稿时间： | 2016/8/25 0:00:00 |
| 修稿时间： | 2016/12/26 0:00:00 |
Calibrating daily 2 m maximum and minimum air temperature forecasts in the ensemble prediction system |
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| XIONG Minquan.Calibrating daily 2 m maximum and minimum air temperature forecasts in the ensemble prediction system[J].Acta Meteorologica Sinica,2017,75(2):211-222. |
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| Authors: | XIONG Minquan |
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| Institution: | National Meteorological Center, Beijing 100081, China |
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| Abstract: | 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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| Keywords: | Ensemble prediction system Daily maximum air temperature Daily minimum air temperature BP neural network-Self Memory method |
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