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Key Technologies of CMA-MESO and Application to Operational Forecast
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摘要: 基于GRAPES-MESO 10 km系统，提高模式动力框架计算精度和稳定性，选择调试适合高分辨率模式的物理过程参数化方案组合，建立面向数值天气预报的全国雷达质量控制拼图系统，通过云分析系统融合全国三维组网反射率因子拼图，建立面向中小尺度系统的对流可分辨同化系统和陆面资料同化系统，实现雷达径向风、风廓线雷达、FY-4A成像仪辐射率、卫星云导风、卫星GNSSRO、地面降水观测以及近地面资料等非常规局地稠密资料的同化应用，发展快速循环技术，建立全国3 km间隔3 h的快速循环同化预报系统——CMA-MESO（GRAPES-MESO 3 km）并实现业务化运行。2020年6—9月汛期业务检验结果表明：CMA-MESO预报的近地面要素（降水、2 m温度、10 m风场）检验评分全面超越GRAPES-MESO 10 km结果；CMA-MESO的24 h累积降水TS评分略低于欧洲中期天气预报中心（ECMWF）的结果，但逐3 h累积降水预报TS评分尤其是对于较大降水阈值评分明显优于ECMWF结果；同时，对于能够表征模式对降水时空精细化特征预报能力的降水频次和降水强度等检验，CMA-MESO对我国汛期的预报准确率超过了ECMWF细网格模式结果。Abstract: To meet the requirement of numerical weather prediction for local severe convective weather, especially disastrous weather and extreme weather events, based on GRAPES-MESO 10 km system, many works have been completed, which include improving the calculation accuracy and stability of the model dynamic framework, selecting and testing the physical parameterization schemes suitable for high-resolution model, establishing a national radar quality control preprocess system, applying the national (SA/SB/CB) three-dimensional network mosaic data through the cloud analysis system, establishing a convective resolvable assimilation system and land surface data assimilation system for small and medium-scale systems, implementing the assimilation and application of unconventional local dense data such as radar radial wind, wind profile radar, FY-4A imager emissivity, satellite cloud motion wind, satellite GNSSRO, surface precipitation and the near surface data, and developing the rapid cycle technology. By integrating all the jobs mentioned above, the nationwide rapid analysis and forecast system CMA-MESO (GRAPES-MESO 3 km)has been established and put into operational run since June 2020 with 3 km horizontal resolution and 3 h time interval. The operational verification results in flood season from June to September of 2020 show that the forecasts of near surface elements (precipitation, 2 m temperature and 10 m wind) of CMA-MESO forecast surpass the results of GRAPES-MESO 10 km system, and the threat score for 3 h accumulated precipitation forecast is outstanding. The threat score for 24 h accumulated precipitation of CMA-MESO is slightly worse than the result of ECMWF, but the threat score for 3 h accumulated precipitation forecast is significantly better. For the precipitation exceeding 10.0 mm, CMA-MESO performs better than ECMWF within all the lead times, and the advantages are more obvious with the increase of precipitation threshold. Compared to ECMWF, CMA-MESO shows more obvious advantages on daytime forecast. For 25 mm precipitation threshold, the improvement rate exceeds 50% in most of the daytime and reaches about 100% in the later stage of forecast. The spatial distribution of mean 24 h accumulated precipitation predicted by CMA-MESO and ECMWF models is close to the observation, but the amount predicted by CMA-MESO is slightly larger. The frequency and intensity of precipitation simulated by CMA-MESO, which can characterize the ability of model to predict the spatial-temporal fine characteristics of precipitation, are consistent with observation in terms of both horizontal distribution and magnitude. The comprehensive performance of CMA-MESO in flood season in China exceeds that of ECMWF fine grid model.
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图 1 流函数ψ、非平衡势函数χu、Exner气压πu、纬向风u、经向风v、温度T和地面气压ps水平相关尺度随高度变化
Fig. 1 Horizontal correlation length changes with height for stream function,unbalanced velocity potential,Exner pressure variable,zonal wind,meridional wind, temperature and surface pressure
图 2 2019年6月1日—8月31日CMA-MESO逐3 h累积降水量(不小于5 mm) 预报TS评分
Fig. 2 Threat score for 3 h accumulated precipitation forecast for 5 mm threshold from 1 Jun to 31 Aug in 2019
图 3 2018年3—8月GRAPES-MESO 10 km和CMA-MESO逐6 h累积降水预报检验TS评分
Fig. 3 Threat score for 6 h accumulated precipitation by GRAPES-MESO 10 km and CMA-MESO from Mar to Aug 2018
图 4 2018年3—8月GRAPES-MESO 10 km和CMA-MESO预报检验
Fig. 4 Verification of 2 m temperature and 10 m wind by GRAPES-MESO 10 km and CMA-MESO from Mar to Aug in 2018
图 5 2020年7月27日00：00背景误差协方差改进前(a)和改进后(b)的第10层纬向风分析增量
Fig. 5 Zonal wind analysis increment at model level 10 in GRAPES_3DVAR by original(a) and improved(b) background error covariance at 0000 UTC 27 Jul 2020
图 6 2019年6—9月CMA-MESO 24 h累积降水预报检验评分
Fig. 6 Scores for 24 h accumulated precipitation by CMA-MESO from Jun to Sep in 2019
图 7 2020年6月26日06：00起报的地面气压倾向
Fig. 7 Surface pressure tendency starting from 0600 UTC 26 Jun 2020
图 8 2020年6—9月CMA-MESO和GRAPES-MESO 10 km业务预报检验
(a)逐3 h累积降水TS评分，(b)2 m温度均方根误差
Fig. 8 Threat score for 3 h accumulated precipitation(a) and root mean square error for 2 m temperature(b) by CMA-MESO and GRAPES-MESO 10 km from Jun to Sep 2020
图 9 2020年6－9月CMA-MESO和ECMWF的3 h累积降水预报TS评分
(a)0.1 mm, 1.0 mm, 5.0 mm, (b)10 mm, 25 mm, 50 mm
Fig. 9 Threat score for 3 h accumulated precipitation by CMA-MESO and ECMWF from Jun to Sep 2020
(a)0.1 mm, 1.0 mm, 5.0 mm, (b)10 mm, 25 mm, 50 mm
图 10 2020年6—9月CMA-MESO和ECMWF3 h累积降水(不小于25 mm) 预报检验
Fig. 10 Scores for 3 h accumulated precipitation (no less than 25 mm) by CMA-MESO and ECMWF from Jun to Sep in 2020
图 11 2020年6—9月观测与模式预报的24 h平均降水量、降水频次以及降水强度分布
(a)观测降水量，(b)ECMWF预报降水量，(c)CMA-MESO预报降水量，(d)观测降水频次，(e)ECMWF预报降水频次，(f)CMA-MESO预报降水频次(g)观测降水强度，(h)ECMWF预报降水强度，(i)CMA-MESO预报降水强度
Fig. 11 Averaged 24 h precipitation, frequency and intensity of observation and forecast by ECMWF and CMA-MESO from Jun to Sep 2020
(a)observed precipitation,(b)precipitation by ECMWF, (c)precipitation by CMA-MESO, (d)observed frequency, (e)frequency by ECMWF, (f)frequency by CMA-MESO, (g)observed intensity, (h)intensity by ECMWF, (i)intensity by CMA-MESO
表 1 CMA-MESO同化融合的观测资料
Table 1 Observations assimilated in the CMA-MESO system
资料种类 观测类型 同化变量 常规观测 探空报 u和v分量、温度、相对湿度 地面报 u和v分量、地表气压、相对湿度、小时降水量 船舶报 u和v分量、地表气压、相对湿度 浮标报 u和v分量 飞机报 u和v分量、温度 雷达 多普勒天气雷达 VAD风、径向风、反射率因子 风廓线雷达 u和v分量 卫星 云导风(FY-2G, HIMAWARI-8) u和v分量 无线电掩星(GNSSRO)
(COSMIC-1, Metop-A, B, FY-3C, D)折射率 FY-4A成像仪(AGRI) 辐射率 FY-2G反演资料 云总量、黑体亮度温度 其他非常规观测 GPS大气水汽含量(GPSPW) 可降水量 
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[1] 齐道日娜, 何立富, 王秀明, 等. "7·20"河南极端暴雨精细观测及热动力成因. 应用气象学报, 2022, 33(1): 1-15. doi: 10.11898/1001-7313.20220101Chyi D, He L F, Wang X M, et al. Fine observation characteristics and thermodynamic mechanisms of extreme heavy rainfall in Henan on 20 July 2021. J Appl Meteor Sci, 2022, 33(1): 1-15. doi: 10.11898/1001-7313.20220101 [2] 何立富, 齐道日娜, 余文. 引发东北极端暴雪的黄渤海气旋爆发性发展机制. 应用气象学报, 2022, 33(4): 385-399. doi: 10.11898/1001-7313.20220401He L F, Chyi D, Yu W. Development mechanisms of the Yellow Sea and Bohai Sea cyclone causing extreme snowstorm in Northeast China. J Appl Meteor Sci, 2022, 33(4): 385-399. doi: 10.11898/1001-7313.20220401 [3] 薛纪善, 陈德辉. 数值预报系统GRAPES的科学设计与应用. 北京: 科学出版社, 2008.Xue J S, Chen D H. Scientific Design and Application of Numerical Prediction System GRAPES. Beijing: Science Press, 2008. [4] 沈学顺, 苏勇, 胡江林, 等. GRAPES_GFS全球中期预报系统的研发和业务化. 应用气象学报, 2017, 28(1): 1-10. doi: 10.11898/1001-7313.20170101Shen X S, Su Y, Hu J L, et al. Development and operation transformation of GRAPES global middle-range forecast system. J Appl Meteor Sci, 2017, 28(1): 1-10. doi: 10.11898/1001-7313.20170101 [5] 王雨, 李莉. GRAPES_Meso V3. 0模式预报效果检验. 应用气象学报, 2010, 21(5): 393-399. http://qikan.camscma.cn/article/id/20100502Wang Y, Li L. Verification of GRAPES_Meso V3. 0 model forecast results. J Appl Meteor Sci, 2010, 21(5): 393-399. http://qikan.camscma.cn/article/id/20100502 [6] 熊秋芬. GRAPES_Meso模式的降水格点检验和站点检验分析. 气象, 2011, 37(2): 185-193. doi: 10.3969/j.issn.1000-6362.2011.02.006Xiong Q F. Verification of GRAPES_Meso precipitation forecasts based on fine-mesh and station datasets. Meteor Mon, 2011, 37(2): 185-193. doi: 10.3969/j.issn.1000-6362.2011.02.006 [7] 黄丽萍, 陈德辉, 邓莲堂, 等. GRAPES_Meso V4. 0主要技术改进和预报效果检验. 应用气象学报, 2017, 28(1): 25-37. doi: 10.11898/1001-7313.20170103Huang L P, Chen D H, Deng L T, et al. Main technical improvements of GRAPES_Meso V4. 0 and verification. J Appl Meteor Sci, 2017, 28(1): 25-37. doi: 10.11898/1001-7313.20170103 [8] Brousseau P, Berre L, Bouttier F, et al. Background-error covariances for a convective-scale data-assimilation system: AROME-France 3D-Var. Quart J Roy Meteor Soc, 2011, 137: 409-422. [9] Ingleby N B, Lorenc A C, Ngan K, et al. Improved variational analyses using a nonlinear humidity control variable. Quart J Roy Meteor Soc, 2013, 139: 1875-1887. [10] Aranami K, Hara T, Ikuta Y, et al. A new operational regional model for convection-permitting numerical weather prediction at JMA. CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling, 2015, 45: 505-506. [11] Schraff C, Reich H, Rhodin A, et al. Kilometre-scale ensemble data assimilation for the COSMO model(KENDA). Quart J Roy Meteor Soc, 2016, 142: 1453-1472. [12] Benjamin S G, Weygandt S S, Brown J M, et al. A North American hourly assimilation and model forecast cycle: The rapid refresh. Mon Wea Rev, 2016, 144: 1669-1694. [13] Weygandt S S, Benjamin S. Radar Reflectivity-based in Initialization of Precipitation Systems Using a Diabatic Digital Filter within the Rapid Update Cycle. 22nd Conf on Weather Analysis and Forecasting/18th Conf on Numerical Weather Prediction, Park City, UT, Amer Meteor Soc, 1B. 7, 2007. [14] Gustafsson N, Janjić T, Schraff C, et al. Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres. Quart J Roy Meteor Soc, 2018, 144: 1218-1256. [15] Lean H W, Clark P A, Dixon M, et al. Characteristics of high-resolution versions of the Met Office Unified Model for forecasting convection over the United Kingdom. Mon Wea Rev, 2008, 136: 3408-3424. [16] Baldauf M, Seifert A, Förstner J, et al. Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities. Mon Wea Rev, 2011, 139: 3887-3905. [17] Saito K, Fujita T, Yamada Y, et al. The operational JMA nonhydrostatic mesoscale model. Mon Wea Rev, 2006, 134: 1266-1298. [18] Peckham S E, Smirnova T G, Benjamin S G, et al. Implementation of a digital filter initialization in the WRF model and its application in the rapid refresh. Mon Wea Rev, 2016, 144: 99-106. [19] Bloom S C, Takacs L L, Da Silva A M, et al. Data assimilation using incremental analysis updates. Mon Wea Rev, 1996, 124: 1256-1271. [20] 麻素红, 张进, 沈学顺, 等. 2016年GRAPES_TYM改进及对台风预报影响. 应用气象学报, 2018, 29(3): 257-269. doi: 10.11898/1001-7313.20180301Ma S H, Zhang J, Shen X S, et al. The upgrade of GRAPE_TYM in 2016 and its impacts on tropical cyclone prediction. J Appl Meteor Sci, 2018, 29(3): 257-269. doi: 10.11898/1001-7313.20180301 [21] Sardeshmukh P D, Hoskins B J. Spatial smoothing on the sphere. Mon Wea Rev, 1984, 112: 2524-2529. [22] Xue M. High-order monotonic numerical diffusion and smoothing. Mon Wea Rev, 2000, 128: 2853-2864. [23] 许晨璐, 王建捷, 黄丽萍. 千米尺度分辨率下GRAPES-Meso4. 0模式定量降水预报性能评估. 气象学报, 2017, 75(6): 851-876. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201706001.htmXu C L, Wang J J, Huang L P. Evaluation on QPF of GRAPES-Meso4. 0 model at convection-permitting resolution. Acta Meteor Sinica, 2017, 75(6): 851-876. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201706001.htm [24] Troen I, Mahrt L. A simple model of the atmospheric boundary layer: Sensitivity to the surface evaporation. Bound-Layer Meteor, 1986, 37: 129-148. [25] Hong S Y, Pan H L. Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon Wea Rev, 1996, 124: 2322-2339. [26] 陈炯, 马占山, 苏勇. 适用于GRAPES模式C-P边界层方案的设计和实现. 应用气象学报, 2017, 28(1): 52-61. doi: 10.11898/1001-7313.20170105Chen J, Ma Z S, Su Y. Boundary layer coupling to Charney-Phillips vertical grid in GRAPES model. J Appl Meteor Sci, 2017, 28(1): 52-61. doi: 10.11898/1001-7313.20170105 [27] Stevens B. Quasi-steady analysis of a PBL model with an eddy-diffusivity profile and nonlocal fluxes. Mon Wea Rev, 2000, 128: 824-836. [28] 王金成, 陆慧娟, 韩威, 等. GRAPES全球三维变分同化业务系统性能. 应用气象学报, 2017, 28(1): 11-24. doi: 10.11898/1001-7313.20170102Wang J C, Lu H J, Han W, et al. Improvements and performances of the operational GRAPES_GFS 3DVar System. J Appl Meteor Sci, 2017, 28(1): 11-24. doi: 10.11898/1001-7313.20170102 [29] 王瑞春, 龚建东. 变分同化框架通过背景误差协方差构建动力平衡约束的研究进展. 气象, 2016, 42(9): 1033-1044. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201609001.htmWang R C, Gong J D. Review of dynamic balance constraints construction using background error covariance in variational assimilation schemes. Meteor Mon, 2016, 42(9): 1033-1044. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201609001.htm [30] 庄照荣, 王瑞春, 王金成, 等. GRAPES_Meso背景误差特征及应用. 应用气象学报, 2019, 30(3): 316-331. doi: 10.11898/1001-7313.20190306Zhuang Z R, Wang R C, Wang J C, et al. Characteristics and application of background errors in GRAPES_Meso. J Appl Meteor Sci, 2019, 30(3): 316-331. doi: 10.11898/1001-7313.20190306 [31] 庄照荣, 李兴良. 尺度叠加高斯相关模型在GRAPES-RAFS中的应用. 气象学报, 2021, 79(1): 79-93. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202101006.htmZhuang Z R, Li X L. The application of superposition of Gaussian components in GRAPES-RAFS. Acta Meteor Sinica, 2021, 79(1): 79-93. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202101006.htm [32] Sun J, Wang H, Tong W, et al. Comparison of the impacts of momentum control variables on high-resolution variational data assimilation and precipitation forecasting. Mon Wea Rev, 2016, 144: 149-169. [33] 王丹, 阮征, 王改利, 等. 风廓线雷达资料在GRAPES-Meso模式中的同化应用研究. 大气科学, 2019, 43(3): 634-654. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201903012.htmWang D, Ruan Z, Wang G L, et al. A study on assimilation of wind profiling radar data in GRAPES-Meso model. Chinese J Atmos Sci, 2019, 43(3): 634-654. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201903012.htm [34] 江源, 刘黎平, 庄薇. 多普勒天气雷达地物回波特征及其识别方法改进. 应用气象学报, 2009, 20(2): 203-213. http://qikan.camscma.cn/article/id/20090210Jiang Y, Liu L P, Zhuang W, et al. Statistical characteristics of clutter and improvements of ground clutter identification technique with Doppler weather radar. J Appl Meteor Sci, 2009, 20(2): 203-213. http://qikan.camscma.cn/article/id/20090210 [35] 仰美霖, 江源, 刘黎平, 等. 北京SA雷达电磁干扰回波特征及质控算法初探. 干旱气象, 2018, 36(5): 805-812.Yang M L, Jiang Y, Liu L P, et al. Characteristics of electromagneticinterference echo of SA radar and quality control method in Beijing. J Arid Meteor, 2018, 36(5): 805-812. [36] Jiang Y, Liu L P. The study of "test pattern" identification algorithm to data from China new generation weather radar system(CINRAD)/SA(B). Adv Atoms Sci, 2014, 31(2): 331-343. [37] 朱立娟, 龚建东, 黄丽萍, 等. GRAPES三维云初始场形成及在短临预报中的应用. 应用气象学报, 2017, 28(1): 38-51. doi: 10.11898/1001-7313.20170104Zhu L J, Gong J D, Huang L P, et al. Three-dimensional cloud initial field created and applied to GRAPES numerical weather prediction nowcasting. J Appl Meteor Sci, 2017, 28(1): 38-51. doi: 10.11898/1001-7313.20170104 [38] 王莉莉, 龚建东. 两种OI陆面同化方法在GRAPES-Meso模式中的初步应用试验. 气象, 2018, 44(7): 857-868. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201807001.htmWang L L, Gong J D. Application of two OI land surface assimilation techniques in GRAPES_Meso. Meteor Mon, 2018, 44(7): 857-868. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201807001.htm [39] Mahfouf J F. Analysis of soil moisture from near-surface parameters: A feasibility study. J Appl Meteor, 1991, 30(11): 1534-1547. [40] Douville H, Viterbo P, Mahfouf J F, et al. Evaluation of the optimum interpolation and nudging techniques for soil moisture analysis using fife data. Mon Wea Rev, 2000, 128(6): 5424-5432. [41] 王瑞春, 龚建东, 王皓. 公里尺度区域变分同化中引入大尺度约束的影响研究. 大气科学, 2021, 45(5): 1-16. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK202105006.htmWang R C, Gong J D, Wang H. Impact studies of introducing a large-scale constraint into the kilometer-scale regional variational data assimilation. Chinese J Atmos Sci, 2021, 45(5): 1-16. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK202105006.htm [42] 庄照荣, 王瑞春, 李兴良. 全球大尺度信息在3 km GRAPES-RAFS系统中的应用. 气象学报, 2020, 78(1): 33-47. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202001003.htmZhuang Z R, Wang R C, Li X L. Application of global large scale information to GRAEPS RAFS system. Acta Meteor Sinica, 2020, 78(1): 33-47. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202001003.htm [43] 庄照荣, 陈静, 黄丽萍, 等. 全球和区域分析的混合方案对区域预报的影响试验. 气象, 2018, 44(12): 1517-1525. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201812001.htmZhuang Z R, Chen J, Huang L P, et al. Impact experiments for regional forecast using blending method of global and regional analyses. Meteor Mon, 2018, 44(12): 1517-1525. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201812001.htm [44] 庄照荣, 李兴良, 刘艳, 等. 数字滤波初始化方案在高分辨率GRAPES区域模式中的应用研究. 气象学报, 2021, 79(3): 443-457. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202103006.htmZhuang Z R, Li X L, Liu Y, et al. A study on digital filter initialization in high-resolution GRAPES regional model. Acta Meteor Sinica, 2021, 79(3): 443-457. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202103006.htm [45] Zhou T J, Yu R C, Chen H M, et al. Summer precipitation frequency, intensity, and diurnal cycle over China: A comparison of satellite data with rain gauge observations. J Climate, 2008, 21(16): 3997-4010. -
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