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60d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1～5d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30d与预报期前一年同日的前后各15d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25mm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60d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。
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 GBR60 is only 55.9%. Especially in the situation of correcting the local heavy precipitation prediction, the correcting effect of GBRh is much better. Furthermore, the correlation coefficient is also calculated, and the result shows that the pattern of precipi-tation 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 opera- tion, this grading bias-removing method of mixed training period will make a very useful product for real time events and have favorable application prospects.
出处
《应用气象学报》
CSCD
北大核心
2015年第2期173-184,共12页
Journal of Applied Meteorological Science
基金
公益性行业(气象)科研专项(GYHY201106010
GYHY201206005)
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
grading bias removed （GBR） method
the mixed training period
summer monsoon rainfall
precipitation correction
