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CMA-GFS V4.0模式关键技术研发和业务化

张进 孙健 沈学顺 苏勇 马占山 井浩 刘奇俊 张红亮 蒋沁谷 陈峰峰 李喆 金之雁 伍湘君 梁妙玲 刘琨

张进, 孙健, 沈学顺, 等. CMA-GFS V4.0模式关键技术研发和业务化. 应用气象学报, 2023, 34(5): 513-526. DOI:  10.11898/1001-7313.20230501..
引用本文: 张进, 孙健, 沈学顺, 等. CMA-GFS V4.0模式关键技术研发和业务化. 应用气象学报, 2023, 34(5): 513-526. DOI:  10.11898/1001-7313.20230501.
Zhang Jin, Sun Jian, Shen Xueshun, et al. Key model technologies of CMA-GFS V4.0 and application to operational forecast. J Appl Meteor Sci, 2023, 34(5): 513-526. DOI:  10.11898/1001-7313.20230501.
Citation: Zhang Jin, Sun Jian, Shen Xueshun, et al. Key model technologies of CMA-GFS V4.0 and application to operational forecast. J Appl Meteor Sci, 2023, 34(5): 513-526. DOI:  10.11898/1001-7313.20230501.

CMA-GFS V4.0模式关键技术研发和业务化

DOI: 10.11898/1001-7313.20230501
资助项目: 

国家自然科学基金重大项目 42090032

详细信息
    通信作者:

    孙健, 邮箱:sunjn@cma.gov.cn

Key Model Technologies of CMA-GFS V4.0 and Application to Operational Forecast

  • 摘要: 针对CMA-GFS V3.3强降水预报偏弱、西北太平洋副热带高压等天气系统预报衰减偏快以及模式计算效率偏低等问题,对模式物理过程与动力框架关键技术开展研发改进。在预报性能方面,通过在云微物理方案中增加霰粒子相关的微物理过程、调整蒸发速率,并在积云对流方案中改进触发条件、卷入率、准平衡闭合假定等关键因子的参数化方法,缓解模式强降水预报不足和小雨过多的问题;采用质量守恒修正算法解决模式长时间积分质量损失问题,改善天气形势预报。在计算效率方面,研制二维参考廓线方案延长模式积分时间步长,开发预条件经典斯蒂菲尔迭代(PCSI)算法提高Helmholtz方程的求解效率,对辐射方案、预估-修正算法等进行计算效率优化。通过上述关键技术的研发和应用,CMA-GFS在降水和天气形势方面的预报技巧得到显著提升,计算效率提高1/3左右,满足模式在0.125°分辨率下业务运行的时效要求,为CMA-GFS V3.3升级到V4.0奠定了基础。
  • 图  1  2013年1月—2022年9月CMA,ECMWF与NCEP全球业务数值预报系统北半球逐月可预报日数

    Fig. 1  Monthly predictable days of operational global numerical prediction system of CMA, ECMWF and NCEP in the Northern Hemisphere from Jan 2013 to Sep 2022

    图  2  2021年7月11日00:00—12日00:00 CMA-GFS云微物理方案改进前后预报的热带地区(20°S~20°N) 平均水凝物垂直廓线

    Fig. 2  Vertical profiles of hydrometeor mass contents over the tropics (20°S-20°N) before and after cloud microphysics improvement of CMA-GFS from 0000 UTC 11 Jul to 0000 UTC 12 Jul in 2021

    图  3  2021年7月11日00:00—12日00:00观测及CMA-GFS云微物理方案改进前后预报的累积降水量

    Fig. 3  Accumulated precipitation of observed and forecasted before and after cloud microphysics improvement of CMA-GFS from 0000 UTC 11 Jul to 0000 UTC 12 Jul in 2021

    图  4  2022年6月26日00:00—27日00:00观测及GMA-GFS对流参数化方案改进前后预报的累积降水量(+ 表示雨强超过20 mm·h-1的站点)

    Fig. 4  Accumulated precipitation of observed and forecasted before and after convective parameterization scheme improvement of CMA-GFS from 0000 UTC 26 Jul to 0000 UTC 27 Jul in 2022 (+ denotes station with precipitation rate exceeding 20 mm·h-1)

    图  5  2022年7月平均500 hPa位势高度(单位:dagpm)

    Fig. 5  500 hPa geopotential height mean in Jul 2022 (unit:dagpm)

    图  6  改进试验预报的2022年8月全球500 hPa高度场距平相关系数和均方根误差及其与控制试验的差异

    (矩形外区域表示差异达到0.05显著性水平)

    Fig. 6  Anomaly correlation coefficient and root mean square error of global 500 hPa geopotnetial height forecasted by improved experiment with differences to control experiment in Aug 2022

    (the area outside the rectangle passing the test of 0.05 level)

    图  7  2021年9月1日—2022年8月31日CMA-GFS V3.3与V4.0连续试验24 h累积降水量预报检验评分

    Fig. 7  Scores for 24 h accumulated precipitation forecasted by CMA-GFS V3.3 and V4.0 from 1 Sep 2021 to 31 Aug 2022

    表  1  CMA-GFS V3.3积分30 d大气总质量相对于初始场的变化

    Table  1  Change of total mass relative to the initial field during 30-day integration for CMA-GFS V3.3

    预报日数/d 控制试验 质量守恒修正试验
    0 1 1
    3 0.999622 0.999999
    6 0.999387 0.999999
    9 0.999316 0.999999
    12 0.999026 0.999999
    15 0.998792 0.999999
    18 0.998868 0.999999
    21 0.998428 0.999999
    24 0.997662 0.999999
    27 0.997217 0.999999
    30 0.996886 0.999999
    下载: 导出CSV

    表  2  CMA-GFS V4.0模式关键技术改进

    Table  2  Improvement of the key technologies of CMA-GFS V4.0

    关键技术与方案 具体改进内容
    云微物理方案 增加霰粒子的微物理转化过程,液滴蒸发由1个时步内完成调整为2个时步内完成
    对流参数化方案 引入次云层环境相对湿度对陆地格点对流触发函数的影响,增强云内侧向卷入率对环境相对湿度的敏感性,优化准平衡假设闭合计算
    质量守恒性处理 引入质量守恒修正算法
    参考廓线算法 参考廓线由三维调整为二维
    Helmholtz方程求解器 求解器由GCR升级为PCSI
    其他优化 辐射方案采用跳点计算,动力预估过程采用QMSL标量平流方法,应用平流和插值等采用向量化方法,优化冗余操作、资料交换、读写效率
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-04-11
  • 修回日期:  2023-07-14
  • 刊出日期:  2023-09-30

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