Vol.25, NO.6, 2014
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Article Navigation >  Journal of Applied Meteorological Science  > 2014  >  25(6): 654-668
Wang Jincheng, Gong Jiandong, Deng Liantang. Operational assimilation of data retrieved by GNSS observations into GRAPES_MESO 3DVar system. J Appl Meteor Sci, 2014, 25(6): 654-668.
Citation: Wang Jincheng, Gong Jiandong, Deng Liantang. Operational assimilation of data retrieved by GNSS observations into GRAPES_MESO 3DVar system. J Appl Meteor Sci, 2014, 25(6): 654-668.
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Operational Assimilation of Data Retrieved by GNSS Observations into GRAPES_Meso 3DVar System

  • Numerical Weather Prediction Center of CMA, Beijing 100081

  • Received Date: 2014-05-13
  • Rev Recd Date: 2014-09-17
  • Publish Date: 2014-11-30
    Abstract
  • Radio occultation (RO) observations using the Global Navigation Satellite System (GNSS) provides valuable data to support operational numerical weather prediction, and it is proved that the assimilation of RO data has the potential to significantly improve the accuracy of global and regional meteorological analysis and weather prediction. RO observations have many advantages such as high precision, global coverage and high vertical resolution compared with other observations. RO observations provide phase (Level 1a), bending angle (Level 1b), refractivity (Level 2a), retrieved pressure, temperature and humidity profiles (Level 2b), all of which can be assimilated into numerical model. Bending angle and refractivity are proved better choices for assimilation as observation operators are less complicated, and have no disadvantages associated with the modeling of ionospheric effects in the assimilation model. However, assimilation of water vapor, temperature or pressure derived from RO observations have advantages that data forms are same as model variables, and it is proved that assimilation of retrieved data also improve the accuracy of global analyses and forecasts significantly.In the operational GRAPES (Global/Regional Assimilation and Prediction System) regional (GRAPES_Meso) 3-dimensional variational (3DVar) system, the analysis is performed in model vertical level, but it can only assimilate the upper-level wind, pressure and humidity from radiosonde report (TEMP), upper-level wind and humidity by AIREP, cloud drift wind (SATOB), the pressure from surface station report over land (SYNOP) and over sea, integrated column precipitable water vapor (IPW) retrieved by ground-based GPS observations and radar wind profiles. In order to assimilate RO retrieved atmosphere data and address their impacts on analyses and forecasts, observation operators of retrieved humidity and pressur eassimilation are developed first, and then quality control and vertical thinning scheme are formed and discussed. Results of one month experiments show that the assimilation of GNSS/RO retrieved pressure and humidity data have considerable positive impacts on analyses of geopotential height, minor positive impacts on humidity analyses, geopotential height, humidity forecasts, rainfall forecasts, and major positive impacts on rainfall forecast for Qinghai-Tibet region.
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  • Fig. 1  The observation error of pressure and relative humidity retrieved by GNSS/RO data

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    Fig. 2  Variations of the gradient norm and the cost-function for background and observation part with the iteration number for assimilating the GNSS/RO retrieved pressure and relative humidity at 0000 UTC 12 July 2013

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    Fig. 3  Number of profiles of bending angle (a), refractivity (b), retrieved atmosphere pressure, temperature and humidity (c) for [-3 h, +3 h) observation window located in GRAPES_Meso simulated domain from 0000 UTC 1 July 2013 to 1800 UTC 31 July 2013

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    Fig. 4  The standard deviation of retrieved pressure and relative humidity from GNSS/RO at analysis time of 0000 UTC and 1200 UTC in July 2013

    (a) the standard deviation of difference between retrieved pressure and the background, (b) the standard deviation of difference between retrieved pressure and the analysis, (c) the standard deviation of difference between retrieved relative humidity and the background, (d) the standard deviation of difference between retrived relative humidity and the analysis

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    Fig. 5  The same as in Fig. 4, but for analysis time of 0600 UTC and 1800 UTC

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    Fig. 6  Variations of the analysis error for OPER and GNSS for analysis time of 0000 UTC and 1200 UTC in July 2013

    (a) analysis error of geopotential height, (b) analysis error of specific humidity, (c) monthly mean analysis error of geopotential height, (d) monthly mean analysis error of specific humidity (in Fig. 6a and Fig. 6b, the shaded denotes the analysis error of experiment OPER, the contour denotes the analysis error difference of GNSS to OPER, the net denotes the error of GNSS lager than that of OPER)

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    Fig. 7  The same as in Fig. 6, but for analysis time of 0600 UTC and 1800 UTC

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    Fig. 8  Variations of 24-h and 48-h forecast errors of geopotential height for analysis time of 0000 UTC and 1200 UTC in July 2013

    (a)24-h forecast error, (b)48-h forecast error, (c) monthly mean 24-h forecast error, (d) monthly mean 48-h forecast error (in Fig. 8a and Fig. 8b, the shaded denotes the error of OPER, the contour denotes the forecast error difference of GNSS to OPER, the net denotes the forecast error of GNSS larger than that of OPER)

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    Fig. 9  Differences of ETS values of 0-24 h (a), 12-36 h (b) and 24-48 h (c) accumulated rainfall of GNSS to OPER for analysis time of 0000 UTC and 1200 UTC in July 2013

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    Fig. 10  Differences of ETS values of 6-30 h (a), 18-42 h (b) accumulated rainfall of GNSS to OPER for analysis time of 0600 UTC and 1800 UTC in July 2013

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    • Received : 2014-05-13
    • Accepted : 2014-09-17
    • Published : 2014-11-30
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