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中国农学通报

中国农学通报 ›› 2016, Vol. 32 ›› Issue (30): 132-138.doi: 10.11924/j.issn.1000-6850.casb16030049

• 资源 环境 生态 土壤 气象 • 上一篇    下一篇

2种集合预报方法在内蒙古汛期降水中的对比试验

斯 琴,荀学义,张 旭   

  1. (内蒙古自治区气象台,呼和浩特 010051）
  • 收稿日期:2016-03-05 修回日期:2016-05-18 接受日期:2016-05-23 出版日期:2016-10-31 发布日期:2016-10-31
  • 通讯作者: 斯 琴
  • 基金资助:
    内蒙古自治区自然科学基金项目“集合预报数值产品的超级集合预报方法研究”(2014BS0403)。

Comparison Test of Two Kinds of Ensemble Forecasting Methods for Flood Season Precipitation in Inner Mongolia

Si Qin, Xun Xueyi, Zhang Xu   

  1. (Inner Mongolia Meteorological Observatory, Huhhot 010051)
  • Received:2016-03-05 Revised:2016-05-18 Accepted:2016-05-23 Online:2016-10-31 Published:2016-10-31

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摘要： 为了研究EC集合预报方法，笔者基于EC集合预报系统51个成员的温度及降水IDL格点资料，以内蒙古2014年汛期逐日降水过程为例，用择优法进行试验，并与集合平均预报进行对比。结果表明：（1）在短期预报中，集合成员个数增加到3或4时平均相关系数最高，成员个数增加到较多或减少时平均相关系数逐渐减小；（2）选择相关系数较大的成员作为集合成员进行集合降水预报试验，效果得到一定改善，并优于集合平均法的预报，因此需对集合预报中集合成员的个数的选取加以研究；（3）无论是集合平均法还是择优法，对大雨以上量级降水预报效果较差，因此有必要研究更好的集合预报方法，使集合预报在灾害性天气预报中的应用得到进一步发展。

关键词: 规模养殖, 规模养殖, 风险, 贝叶斯决策, 期望损益

Abstract: To study the EC ensemble forecast method, the authors took daily precipitation process in 2014 flood season in Inner Mongolia as an example, tested the optimization method and compared it with the ensemble average forecast, based on temperature of EC 51 member ensemble forecast system and IDL lattice data of precipitation. The results showed that: (1) in the short-term forecast, when the number of ensemble members increased to 3 or 4, it had the highest correlation coefficient, when the number of ensemble members increased more or less, the average correlation coefficient decreased; (2) the members with bigger coefficients were selected as the members for the optimization ensemble forecast, results showed that the forecast effect was improved, and it was better than the ensemble average method, therefore, it was necessary to study the selection of number of ensemble forecast member; (3) either the ensemble average method or the optimization method, the forecast effect was bad for heavy rain or above level. Therefore, it is necessary to develop a better ensemble forecasting method which could be more applicable to disastrous weather forecast.