马占山, 刘奇俊, 秦琰琰, 康志明, 延昊. 2009: 利用TRMM卫星资料对人工增雨云系模式云微观场预报能力的检验. 气象学报, (2): 260-271. DOI: 10.11676/qxxb2009.026
引用本文: 马占山, 刘奇俊, 秦琰琰, 康志明, 延昊. 2009: 利用TRMM卫星资料对人工增雨云系模式云微观场预报能力的检验. 气象学报, (2): 260-271. DOI: 10.11676/qxxb2009.026
Ma Zhanshan, Liu Qijun, Qin Yanyan, Kang Zhiming, Yan Hao. 2009: Verification of forecasting efficiency to cloud microphysical characters of mesoscale numerical model for artificial rainfall enhancement by using TRMM satellite data. Acta Meteorologica Sinica, (2): 260-271. DOI: 10.11676/qxxb2009.026
Citation: Ma Zhanshan, Liu Qijun, Qin Yanyan, Kang Zhiming, Yan Hao. 2009: Verification of forecasting efficiency to cloud microphysical characters of mesoscale numerical model for artificial rainfall enhancement by using TRMM satellite data. Acta Meteorologica Sinica, (2): 260-271. DOI: 10.11676/qxxb2009.026

利用TRMM卫星资料对人工增雨云系模式云微观场预报能力的检验

Verification of forecasting efficiency to cloud microphysical characters of mesoscale numerical model for artificial rainfall enhancement by using TRMM satellite data

  • 摘要: 文中利用TRMM卫星测雨雷达探测反演的云水、雨水、云冰和降冰4种云参数产品及实况降水资料,对比检验该人工增雨云系业务模式对云微观场和地面降水场的预报能力。结果表明,人工增雨云系模式系统对降水的预报能力要略优于现行业务运行的GRAPES模式;人工增雨云系模式系统能较好地预报云系系统云物理微观量的垂直结构特征,模式预报的微观场与卫星监测吻合较好;在播撒窗区的水平分布上,模式预报的各水凝物分布形势和强中心位置与卫星监测一致,其大小也接近监测值;人工增雨云系模式能较好地预报云的微观场和天气形势场,可作为云系人工增雨条件决策的重要参考依据。

     

    Abstract: An analysis is preformed on forecast capabilities of cloud microphysical properties and rainfall by using cloud liquid water, precipitation water, cloud ice water and precipitation ice data retrieved from TRMM satellite and observed precipitation data. Results show that: (1) Precipitation forecast capability of artificial rainfall model has better performance than that of operational model GRAPES. (2) It is capable of better predicting vertical distributions of microphysical quantities of cloud. (3) The horizontal distribution structures, variable values and the position of the most intensity of each hydrometeors of model are consistent with those of TRMM satellite at operation height levels. (4) Because of its good performance on predicting cloud microphysical fields and synoptic situations, artificial rainfall enhancement model will provide valuable information for decision making of operation divisions.

     

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