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