Wang Jincheng, Lu Huijuan, Han Wei, 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.
Citation: Wang Jincheng, Lu Huijuan, Han Wei, 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.

Improvements and Performances of the Operational GRAPES_GFS 3DVar System

DOI: 10.11898/1001-7313.20170102
  • Received Date: 2016-03-22
  • Rev Recd Date: 2016-10-12
  • Publish Date: 2017-01-31
  • In recent years, the capability and stability of GRAPES (Global/Regional Assimilation and PrEdiction System) three-dimensional variation data assimilation system (3DVar) is upgraded and improved gradually in Numerical Weather Prediction Center of China Meteorological Administration. Improvements in analysis scheme and assimilating data technique for GRAPES 3DVar in the past two years are overviewed. Then the capability and performance of G-M3DVar latest version are evaluated by two-year length experiments. The accuracy and precision of G-M3DVar analyses is evaluated against radiosonde observation and ERA-Interim reanalysis and is compared with NCEP FNL and T639 analysis.Taken radiosonde data as a reference, the root mean square error and bias of pressure analyses of G-M3DVar are smaller than ERA-Interim reanalysis and NCEP FNL analysis data in all domains in both winter and summer seasons. The root mean square error and bias of u wind analysis of G-M3DVar are larger than ERA-Interim reanalysis and NCEP FNL analysis in the Tropics. However, in the Northern Hemisphere, the root mean square error and bias of u wind of G-M3DVar are similar to ERA-Interim below 250 hPa. In the Southern Hemisphere, the root mean squared error of u wind of G-M3DVar is the largest compared to EAR-Interim reanalysis and NCEP FNL analysis. For humidity field, the bias of G-M3DVar analysis is smaller than EAR-Interim reanalysis and NCEP FNL analysis in the middle and high troposphere, which means that the humidity analysis of G-M3DVar is much drier than ERA-Interim and NCEP FNL data especially in the middle and high troposphere. Taken ERA-Interim reanalysis data as a reference, the root mean square error of G-M3DVar analysis is smaller than the T639 analysis but larger than NCEP FNL analysis data for all fields excluded the humidity.In conclusion, the quality of G-M3DVar analysis is better than T639 analysis and satisfies requirements of operational run. In recent years, the gap of analyses between G-M3DVar and advanced numerical weather centers such as ECMWF keeps growing, although the accuracy of G-M3DVar analysis is improved significantly in the past two years. Much more focus and works should be paid in the following aspects. First, the background error covariance (BE) is estimated by National Meteorological Center of USA (NMC) method, which is static and climatological. The static and climatological BE is far from meeting requirements of the modern numerical weather prediction. Second, the quality control scheme for all observations in G-M3DVar is still relatively inexactly and incapable. Third, the bias correction scheme for microwave radiance in G-M3DVar is still static which has been proved to have some shortcomings.
  • Fig. 1  Mean and standard deviations of OMERA, OMEFNL and OMGRP on standard pressure in winter and summer over the North Hemisphere, the Tropics and the South Hemisphere (a) over the North Hemisphere in winter, (b) over the North Hemisphere in summer, (c) over the Tropics in winter, (d) over the Tropics in summer, (e) over the South Hemisphere in winter, (f) over the South Hemisphere in summer

    Fig. 2  The same as in Fig. 1, but for u wind

    Fig. 3  The same as in Fig. 1, but for relative humidity

    Fig. 4  The root mean square error of NCEP FNL, T639 and G-M3DVar analyses for geopotential height against ERA-Interim reanalysis over the North Hemisphere (a), the Tropics (b) and the South Hemisphere (c)(from 0000 UTC 1 Sep 2013 to 1800 UTC 31 August 2014)

    Fig. 5  The same as in Fig. 4, but for u wind

    Fig. 6  The same as in Fig. 4, but for specific humidity

    Table  1  Observations assimilated in the experiment by G-M3DVar

    资料种类 仪器 平台 同化要素
    常规观测 探空 气压,uv风分量,相对湿度
    地面 地表气压
    船舶 地表气压
    小球测风 uv风分量
    飞机 uv风分量,温度
    卫星 AMSU-A微波温度计 NOAA-15,NOAA-18,NOAA-19,Metop-A 亮温
    AIRS红外高光谱 AQUA 亮温
    GNSS掩星 COSMIC,GRAS,GRACE-A,TerraSAR-X 折射率
    云导风 uv风分量
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    • Received : 2016-03-22
    • Accepted : 2016-10-12
    • Published : 2017-01-31

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