随机物理过程扰动方案在克拉玛依区域集合预报中的应用研究
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1.克拉玛依市气象局;2.北京城市气象研究院;3.中国气象科学研究院

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国家重点研发计划(批准号2021YFC3000901)


Study on the application of Stochastic Perturbed Physics Tendency perturbation in Regional Ensemble Prediction System of KelamaySHI Yongqiang1, ZHANG Hanbin2,LIU Yujue2, ZHANG Xinran3
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Affiliation:

1.Kelamayi Meteorological Bureau;2.Institute of Urban Meteorology, China Meteorological Administration;3.Chinese Academy of Meteorological Sciences

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    摘要:

    克拉玛依气象局研发了区域集合预报系统并已实现业务运行,该系统仅采用了集合变换卡尔曼滤波(ETKF)初值扰动,导致离散度发展受到限制,为改善区域集合预报的离散度,本文尝试在初值扰动基础上引入随机物理过程倾向(SPPT)模式扰动方案。本文首先开展了SPPT方案关键参数的敏感性试验,确定了适用于本系统的参数设置,构建了初值-物理过程扰动方案(ETKF-SPPT),并与仅采用初值扰动的集合方案(ETKF)进行了对比,结果表明: ETKF初值扰动方法能够产生具有动力学结构的初值扰动,但是随着预报时效的延长,集合整体离散度增长很快达到饱和,并在侧边界约束下逐渐减小;ETKF初值扰动结合SPPT模式扰动可使集合离散度在各个预报时效均保持增长状态;集合预报检验结果表明,仅采用ETKF初值扰动的集合预报概率分布可靠性较低,概率预报准确性也较差;ETKF-SPPT方法可获得更好的概率预报结果,可靠性更好,均方根误差更低。对克拉玛依城区一次大风预报个例表明,ETKF方案对大风起风时间和量级把握较差,而ETKF-SPPT可以增加集合离散度,起风时间和风速预报更准确。综合而言,增加SPPT扰动可以有效改善克拉玛依区域集合预报系统的预报技巧。

    Abstract:

    At present, a Regional Ensemble Prediction System has been developed by Kelamayi Meteorological Bureau. The system has only adopted initial condition perturbation of Ensemble Transform Kalman Filter(ETKF), and the system is lack of spread. In order to improve the skill of this Ensemble Prediction System, the model perturbation method of Stochastic Physics Parameterization Tendency(SPPT) is adopted and tested. This paper conducted sensitivity test on critical parameter of SPPT and parameter setting of SPPT is determined. Ensemble forecast experiment test is conducted and compared for both ETKF scheme and ETKF-SPPT scheme. The results show that the ETKF method can generate initial condition perturbation with dynamic structure, but the spread will saturated within short forecast lead time and will decrease due to the constraint of identical LBC for all members. With SPPT model perturbation method adopted, the ensemble spread can significantly improved. Ensemble verification scores indicate that the reliability of ETKF without model perturbation is small, and the root mean square error(RMSE) is relatively large, while add model perturbation to initial condition perturbation will improve the probabilistic forecast skill with larger forecast reliability and smaller(RMSE) . The results of gale forecast show that the adoption of model perturbation method can significantly improve the ensemble forecast that it has more accurate forecast on the magnitude and time period of local gale.

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历史
  • 收稿日期:2021-11-21
  • 最后修改日期:2022-06-19
  • 录用日期:2022-09-15
  • 在线发布日期: 2022-09-15
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