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CMA-GFS云预报的偏差分布特征

李喆 陈炯 马占山 陆慧娟 胡江凯 刘奇俊

李喆, 陈炯, 马占山, 等. CMA-GFS云预报的偏差分布特征. 应用气象学报, 2022, 33(5): 527-540. DOI:  10.11898/1001-7313.20220502..
引用本文: 李喆, 陈炯, 马占山, 等. CMA-GFS云预报的偏差分布特征. 应用气象学报, 2022, 33(5): 527-540. DOI:  10.11898/1001-7313.20220502.
Li Zhe, Chen Jiong, Ma Zhanshan, et al. Deviation distribution features of CMA-GFS cloud prediction. J Appl Meteor Sci, 2022, 33(5): 527-540. DOI:  10.11898/1001-7313.20220502.
Citation: Li Zhe, Chen Jiong, Ma Zhanshan, et al. Deviation distribution features of CMA-GFS cloud prediction. J Appl Meteor Sci, 2022, 33(5): 527-540. DOI:  10.11898/1001-7313.20220502.

CMA-GFS云预报的偏差分布特征

DOI: 10.11898/1001-7313.20220502
资助项目: 

国家重点研发计划 2017YFA0604500

详细信息
    通信作者:

    李喆, 邮箱:liz@cma.gov.cn

Deviation Distribution Features of CMA-GFS Cloud Prediction

  • 摘要: 利用2021年3月—2022年2月ERA5再分析数据云量、云水凝物对中国气象局研发的全球数值预报系统CMA-GFS同期云量产品和由云量、云水凝物产品计算的云发生、云水凝物积分的偏差特征进行诊断评估, 初步探讨了CMA-GFS云预报偏差存在的可能原因。结果显示:CMA-GFS云量、云水凝物的分布较为合理, CMA-GFS能够描绘全球云量、云水凝物的分布特征, 并能够反映季节特征;CMA-GFS预报高云和中云的云量偏差大于低云的云量偏差, 而高云和中云的云量均方根误差较低云偏小, 说明模式高云和中云的预报稳定性优于低云;与ERA5再分析数据相比, CMA-GFS液相水凝物积分以负偏差为主, 冰相水凝物积分以正偏差为主;云量、云水凝物的偏差在不同地区成因不同, 在热带地区的偏差与对流参数化和微物理方案不协调有关, 在南北半球中高纬度地区的偏差与相对湿度偏差相关。
  • 图  1  季节平均ERA5再分析数据云量分布

    Fig. 1  Seasonal mean cloud fraction from ERA5 reanalysis data

    图  2  云发生频率偏差分布

    Fig. 2  Frequency bias of cloud occurrence

    图  3  CMA-GFS云量产品与ERA5再分析数据云量经向分布

    Fig. 3  Meridional mean cloud fraction from CMA-GFS and ERA5 reanalysis data

    图  4  CMA-GFS云发生与云量综合评估

    Fig. 4  Evaluation of CMA-GFS cloud occurrence and cloud fraction prediction skill

    图  5  季节平均液相水凝物积分偏差分布

    Fig. 5  Seasonal mean liquid water hydrometer integration

    图  6  季节平均冰相水凝物积分偏差分布

    Fig. 6  Seasonal mean solid water hydrometer integration

    图  7  季节平均CMA-GFS水凝物积分与ERA5再分析数据水凝物积分经向分布

    Fig. 7  Meridional distribution of seasonal mean cloud hydrometeor integration from CMA-GFS and ERA5 reanalysis data

    图  8  水凝物积分偏差与均方根误差评估

    Fig. 8  Evaluation of the seasonal mean cloud hydrometeors integration bias and root mean square error prediction skill

    图  9  季节平均CMA-GFS预报与ERA5再分析数据、GPM降水率经向分布

    Fig. 9  Meridional distribution of seasonal mean precipitation rate from CMA-GFS forecasting and ERA5 reanalysis data, GPM data

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  • 收稿日期:  2022-03-25
  • 修回日期:  2022-06-30
  • 刊出日期:  2022-09-15

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