Huang Liping, Deng Liantang, Wang Ruichun, et al. Key technologies of CMA-MESO and application to operational forecast. J Appl Meteor Sci, 2022, 33(6): 641-654. DOI:  10.11898/1001-7313.20220601.
Citation: Huang Liping, Deng Liantang, Wang Ruichun, et al. Key technologies of CMA-MESO and application to operational forecast. J Appl Meteor Sci, 2022, 33(6): 641-654. DOI:  10.11898/1001-7313.20220601.

Key Technologies of CMA-MESO and Application to Operational Forecast

DOI: 10.11898/1001-7313.20220601
  • Received Date: 2022-08-01
  • Rev Recd Date: 2022-09-06
  • Available Online: 2022-11-21
  • Publish Date: 2022-11-17
  • 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.
  • 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

    Fig. 2  Threat score for 3 h accumulated precipitation forecast for 5 mm threshold from 1 Jun to 31 Aug in 2019

    Fig. 3  Threat score for 6 h accumulated precipitation by GRAPES-MESO 10 km and CMA-MESO from Mar to Aug 2018

    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

    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

    Fig. 6  Scores for 24 h accumulated precipitation by CMA-MESO from Jun to Sep in 2019

    Fig. 7  Surface pressure tendency starting from 0600 UTC 26 Jun 2020

    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

    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

    Fig. 10  Scores for 3 h accumulated precipitation (no less than 25 mm) by CMA-MESO and ECMWF from Jun to Sep in 2020

    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

    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 uv分量、温度、相对湿度
    地面报 uv分量、地表气压、相对湿度、小时降水量
    船舶报 uv分量、地表气压、相对湿度
    浮标报 uv分量
    飞机报 uv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 uv分量
    卫星 云导风(FY-2G, HIMAWARI-8) uv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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