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

Zhang Jin; Sun Jian; Shen Xueshun; Su Yong; Ma Zhanshan; Jing Hao; Liu Qijun; Zhang Hongliang; Jiang Qingu; Chen Fengfeng; Li Zhe; Jin Zhiyan; Wu Xiangjun; Liang Miaoling; Liu Kun

doi:10.11898/1001-7313.20230501

Vol.34, NO.5, 2023

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Article Navigation > Journal of Applied Meteorological Science > 2023 > 34(5): 513-526

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.

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.

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

DOI: 10.11898/1001-7313.20230501

Zhang Jin^{1, 2},
Sun Jian^{1, 2
,
,},
Shen Xueshun^{1, 2},
Su Yong^{1, 2},
Ma Zhanshan^{1, 2},
Jing Hao^{1, 2},
Liu Qijun^{1, 2},
Zhang Hongliang^{1, 2},
Jiang Qingu^{1, 2},
Chen Fengfeng^{1, 2},
Li Zhe^{1, 2},
Jin Zhiyan^{1, 2},
Wu Xiangjun^{1, 2},
Liang Miaoling^{1, 2},
Liu Kun^{1, 2}

1.
CMA Earth System Modeling and Prediction Center, Beijing 100081
2.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081

Received Date: 2023-04-11
Rev Recd Date: 2023-07-14
Publish Date: 2023-09-30

Abstract

Abstract

To address problems including underestimation of heavy precipitation, rapid decay of synoptic systems and low computational efficiency in operational forecast of CMA-GFS V3.3, some key technologies related to physics and dynamics of the model are developed and applied.A suite of graupel-related microphysical processes is adopted in the cloud microphysics scheme to improve the forecast performance of heavy precipitation. These processes include graupel colliding with cloud water, ice crystals and snow, automatic conversions of ice crystals to graupel and snow to graupel, melting process of graupel to raindrop and sublimation process of graupel. In addition, the evaporation rate of cloud and rainwater is restricted, which can increase the liquid water content in warm areas and improve precipitation efficiency.In the convection parameterization scheme, the role of the sub-cloud environmental relative humidity to convection triggers is considered, and the unreasonable occurrence of convections in dry environment is suppressed. Also, the sensitivity of the entrainment rate of the convective updraft to the relative humidity outside the cloud is enhanced to weaken the convections in dry environment. At the same time, the quasi-equilibrium closure scheme is optimized to improve the accuracy in calculating cloud-base mass flux which is related to the convection intensity.To solve the problem of the mass loss in long time integration, a mass conservation correction method is introduced to the model dynamic framework. The method is developed to ensure the mass conservation by adjusting mass in each grid box according to different weight coefficients which are determined by the change of total atmospheric mass of the current time step relative to the previous step.In terms of computational efficiency, the two-dimensional reference profile algorithm is developed. Without losing calculation accuracy, the model integration time step is extended from 240 s to 300 s using the new profile instead of the original three-dimensional reference profile. Meanwhile, the PCSI method is adopted instead of the GCR method, which reduces the time consuming of solving Helmholtz equation. In addition, the radiation scheme and predictor-corrector algorithms are also optimized to improve the computational efficiency.Through the application of the above key technologies, the forecasting skills for weather pattern and precipitation of CMA-GFS are significantly improved. And its computational efficiency is increased by about 1/3, which meets operational time requirement for the model with 0.125° horizontal resolution. Based on the improved model and the research achievements in other aspects of the forecast system, CMA-GFS is upgraded to V4.0 with a significantly improved comprehensive performance.
- CMA-GFS;
- cloud microphysics;
- convection parameterization;
- mass conservation correction;
- reference profile

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References(51)

Figures and Tables

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

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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

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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

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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)

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

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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)

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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

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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

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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

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