| 一种改进的数值预报降水偏差订正方法及应用 |
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| 引用本文： | 孙靖,程光光,张小玲.一种改进的数值预报降水偏差订正方法及应用[J].应用气象学报,2015,26(2):173-184. |
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| 作者姓名： | 孙靖 程光光 张小玲 |
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| 作者单位： | 1.国家气象中心，北京 100081 |
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| 基金项目： | 公益性行业（气象）科研专项（GYHY201106010，GYHY201206005） |
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| 摘 要： | 对传统的消除偏差法进行改进，形成分等级消除偏差法，并使用混合训练期和60 d滑动训练期方案分别对2012年6—8月ECMWF (European Centre for Medium-Range Weather Forecasting) 模式夏季1~5 d的降水预报进行订正试验。为了尽可能符合中国东部夏季降水具有移动性及多种时间尺度变化的特点，混合训练期以预报期前30 d与预报期前一年同日的前后各15 d组成。结果表明:在使用分等级消除偏差法的基础上，相比ECMWF模式降水预报，两种训练期方案的订正结果几乎对各个阈值的ETS评分均有一定提高，特别是对25 mm以上降水预报评分的提高幅度，混合训练期方案的订正结果明显高于60 d滑动训练期方案；在区域性强降水预报的订正中，混合训练期方案优势更为明显。另外，通过分析两种训练期方案的预报偏差发现，分等级订正是此次消除偏差订正试验中提高强降水预报评分的关键，选择合适的训练期可以增加评分提高的幅度。由于上述试验使用的ECMWF模式预报和站点实况均是业务上常用数据，因此，该方法具有一定的业务应用价值。
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| 关 键 词： | 分等级消除偏差法 混合训练期 夏季风降水 降水订正 |
| 收稿时间： | 2014-11-07 |
| 修稿时间： | 1/9/2015 12:00:00 AM |
An Improved Bias Removed Method for Precipitation Prediction and Its Application |
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| Sun Jing,Cheng Guangguang and Zhang Xiaoling.An Improved Bias Removed Method for Precipitation Prediction and Its Application[J].Quarterly Journal of Applied Meteorology,2015,26(2):173-184. |
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| Authors: | Sun Jing Cheng Guangguang and Zhang Xiaoling |
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| Institution: | 1.National Meteorological Center, Beijing 1000812.Numerical Weather Prediction Center of CMA, Beijing 100081 |
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| Abstract: | On the basis of traditional bias removed (BR) method, grading bias removed (GBR) method is designed by adding the step of correcting according to three precipitation orders, which are more than 0.1 mm, 25 mm and 50 mm, respectively. Then, using observations of precipitation and numerical precipitation prediction of ECMWF from April to August in 2011 and 2012, the real-time precipitation forecast of 1-5 days at summer (June-August) over China in 2012 is corrected by GBR method using two different training periods, i.e., the mixed training phase and 60-day running training phase, and the results of them are called GBR_h and GBR_60, respectively. In order to contain information of heavy precipitation in forecast phase as much as possible, the mixed training period is composed of a 30-day period before the forecast phase and two 15-day periods before and after the same phase one year ago, according to characteristics of summer monsoon rainfall of China.Equitable-threat scores (ETS) of forecast over China at many thresholds of precipitation are examined, in order to compare results of the mixed training and the 60-day running training period using GBR. It reveals that both of two corrected results have higher skill than precipitation prediction of ECMWF, at the threshold of beneath 25 mm, the improving amplitude of them are very close (the improvement of GBR_h and GBR_60 are 19.5% and 19.1%, respectively). However, for those above 25 mm, GBR_h apparently has bigger amplitude which is up to 73.5%, and GBR_60 is only 55.9%. Especially in the situation of correcting the local heavy precipitation prediction, the correcting effect of GBR_h is much better. Furthermore, the correlation coefficient is also calculated, and the result shows that the pattern of precipitation prediction is also modified by GBR_h and GBR_60, and the former also has better performance.By analyzing errors of three orders calculated through two different training periods, it is clear that the key point of successfully improving the initial ECMWF forecasts is to add the step of grading bias removed, and a larger improvement of ETS can be expected if more appropriate mixed training period is chosen. It is assumed that according to the obvious effect of this experiment which are easy to apply in operation, this grading bias-removing method of mixed training period will make a very useful product for real time events and have favorable application prospects. |
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| Keywords: | grading bias removed (GBR) method the mixed training period summer monsoon rainfall precipitation correction |
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