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随机参数扰动方法对中国冬季降水集合预报的影响
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1.成都信息工程大学;2.中国气象科学研究院;3.中国气象局数值预报中心

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Influence of Stochastically Perturbed Parameterization method on ensemble forecasting of winter precipitation in China
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Numerical Weather Prediction Center, China Meteorological Administration

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    摘要:

    降水数值预报有很大的不确定性,与降水预报密切相关的物理过程参数化方案中关键参数的不确定性是降水数值预报误差来源之一,对这些参数引入随机扰动的随机参数扰动方法(Stochastically Perturbed Parameterization,简称SPP方法)可以代表模式降水预报的不确定性,是国际集合预报前沿研究领域。为了认识该方法能否代表中国冬季降水数值预报的不确定性,为业务应用提供科学依据,基于中国气象局中尺度区域集合预报模式(Global/Regional Assimilation and Prediction System-Regional Ensemble Prediciton System, GRAPES-REPS),从对模式降水预报不确定性有较大影响的积云对流、云微物理、边界层及近地面层等四个参数化方案中选取了16个与降水密切相关的关键参数,引入了随机参数扰动方法,并通过2018年12月12日至2019年1月12日总计31天的冬季集合预报试验,对比分析了SPP方法对等压面要素及降水的集合预报效果。结果显示:在冬季应用SPP方法时,等压面要素的概率预报技巧总体来说优于无SPP方法扰动的对比试验,且对于低层、近地面要素的改进效果优于对中高层等压面要素的改进;但对降水概率预报而言,尽管检验评分数值略优于对比预报试验,但并未通过显著性检验,这表明,在东亚冬季风影响下,随机参数扰动方法对中国冬季降水概率预报技巧没有明显的改进。究其原因,可能是由于SPP方法主要代表对流性降水预报的不确定性,而中国冬季降水过程主要与斜压不稳定发生发展有关,模式降水以大尺度格点降水为主,对流性降水较少,故对冬季降水预报改进不明显,这为业务集合预报模式中应用随机参数扰动方法提供了科学依据。

    Abstract:

    Precipitation ensemble forecasting has great uncertainty, the uncertainty of parameters in the physical process parameterization scheme closely related to precipitation forecast is one of the sources of precipitation numerical prediction error. By introducing Stochastically Perturbed Parameterization (SPP method) on these parameters the uncertainty of representative model precipitation forecast is the frontier research field of international ensemble forecasting. In order to understand whether this method can reflect the uncertainty of numerical prediction of winter precipitation in Chinaand provide a scientific basis for business applications,based on China Meteorological Administration’s GRAPES(Global/Regional Assimilation and Prediction System)mesoscale regional ensemble prediction model,the 16 key parameters are selected from four parameterization schemes such as cumulus convection, cloud microphysics, boundary layer and near-surface layer, which have great influences on the uncertainty of model precipitation forecast,and introducing Stochastically Perturbed Parameterization (SPP),then through the ensemble prediction experiment from December 12, 2018 to January 12, 2019, a total of 31 days, comparing and analyzing the effect of SPP method on winter weather situation and precipitation ensemble prediction.The results show that the test for the random parameter perturbation method is added,the probability prediction techniques for precipitation and isobaric elements are better than control predictions without SPP method,and the improvement effect on low-level and near-surface elements is better than the improvement of the iso-surface elements in the middle or upper floors,The precipitation prediction result is superior to the control prediction test in scoring,but because it did not pass the significance test,there are no statistically significant differences.The above results indicate: under the influence of the East Asian winter monsoon,SPP method has no obvious improvement on the prediction technique of winter precipitation in China. Analyzing the reason,it may be related to the SPP method mainly represents the uncertainty of convective precipitation forecasting,but the winter precipitation process in China is mainly related to the development of baroclinic instability, model precipitation is dominated by large-scale grid precipitation, with less convective precipitation, therefore, the improvement of winter precipitation forecast is not obvious,this provides a scientific basis for applying Stochastically Perturbed Parameterization methods in the operation ensemble forecasting model.

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  • 收稿日期:2019-05-05
  • 最后修改日期:2019-10-18
  • 录用日期:2020-01-06
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