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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  • [1]
    Ware R, Rocken C, Solheim F, et al.GPS sounding of the atmosphere from low earth orbit: Preliminary results. Bull Amer Meteor Soc, 1996, 77:19-40. doi:  10.1175/1520-0477(1996)077<0019:GSOTAF>2.0.CO;2
    [2]
    Wickert J, Arras C, Ao C O, et al.CHAMP, GRACE, SAC-C, TerraSAR-X/TanDEM-X:Science Results, Status and Future Prospects.GRAS SAF Workshop on Applications of GPSRO Measurements, 2008:43-52.
    [3]
    Rocken C, Kuo Y H, William S S.COSMIC system description. TAO, 2000, 11(1):21-52. https://www.researchgate.net/publication/234422312_COSMIC_system_description
    [4]
    Anthes R A, and Coauthors.The COSMIC/FORMOSAT-3 mission:Early results. Bull Amer Meteor Soc, 2008, 89:313-333, doi: 10.1175/BAMS-89-3-313.
    [5]
    Kursinski E R, and Coauthors.Initial results of radio occultation observations of Earth's atmosphere using the Global Positioning System. Science, 1996, 127:1107-1110. http://authors.library.caltech.edu/53925/
    [6]
    Zou X, Kuo Y H, Guo Y R.Assimilation of atmospheric radio refractivity using a nonhydrostatic adjoint model. Mon Wea Rev, 1995, 123:2229-2249. doi:  10.1175/1520-0493(1995)123<2229:AOARRU>2.0.CO;2
    [7]
    Kuo Y H, Sergey V S, Richard A A, et al.Assimilation of GPS radio occultation data for numerical weather prediction. TAO, 2000, 11(1):157-186. https://www.researchgate.net/publication/260049622_Assimilation_of_GPS_Radio_Occultation_Data_for_Numerical_Weather_Prediction
    [8]
    Liu H, Zou X, Shao H, et al.Impact of 837 GPS/MET bending angle profiles on assimilation and forecasts for the period June 20-30, 1995. J Geophys Res, 2001, 106(D23):31771-31786. doi:  10.1029/2001JD000345
    [9]
    Chen S Y, Huang C Y, Kuo Y H, et al.Assimilation of GPS refractivity from FORMOSAT-3/COSMIC using a nonlocal operator with WRF 3DVAR and its impact on the prediction of a typhoon event. Terr Atmos Ocean Sci, 2009, 20(1):133-154. doi:  10.3319/TAO.2007.11.29.01(F3C)
    [10]
    Ma Z Z, Kuo Y H, Wang B, et al.Comparison of local and nonlocal observation operators for the assimilation of GPS RO data with the NCEP GSI System:An OSSE study. Mon Wea Rev, 2009, 137:3575-3587, doi:0.1175/2009MWR2809.1.
    [11]
    Huang C Y, Kuo Y H, Chen S Y, et al. Impact of GPS radio occultation data assimilation on regional weather predictions. GPS Solut, 2010, 14:35-49, doi: 10.1007/s10291-009-0144-1.
    [12]
    Ma Z Z, Kuo Y H, Martin F R, et al.Assimilation of GPS radio occultation data for an intense atmospheric river with the NCEP regional GSI system. Mon Wea Rev, 2011, 139:2170-2183, doi: 10.1175/2011MWR3342.1.
    [13]
    Cucurull L, Derber J C.Operational implementation of COSMIC observations into NCEP's Global Data Assimilation System. Wea Forecasting, 2008, 23:702-711. doi:  10.1175/2008WAF2007070.1
    [14]
    唐细坝, 薛纪善.COSMIC资料在GRAPES全球三维变分同化系统的初步研究.热带气象学报, 2009, 25(5):521-531. http://www.cnki.com.cn/Article/CJFDTOTAL-RDQX200905002.htm
    [15]
    Liu Y, Xue J S.Operational implementation of the assimilation of global navigation satellite radio occultation observations into GRAPES data assimilation system. J Meteor Res, 2014, 28, doi: 10.1007/s13351-014-4028-0.
    [16]
    赵德显, 郁红弟, 沈桐立.GPS折射率资料在梅雨锋暴雨数值模拟中的应用.气象, 2011, 39(12):1511-1518. doi:  10.7519/j.issn.1000-0526.2011.12.006
    [17]
    Syndergaard S, Kuo Y H, Lohmann M S.Atmosphere and Climate, Chapter: Observation Operators for the Assimilation of Occultation Data into Atmospheric Models: A Review. Berlin:Springer Berlin Heidelberg, 2006:205-224.
    [18]
    Poli P, Healy S B, Dee D P.Assimilation of Global Positioning System radio occultation data in the ECMWF ERA-Interim reanalysis. Q J R Meteorol Soc, 2010, 136:1972-1990. doi:  10.1002/qj.722
    [19]
    Rennie M P.The impact of GPS Radio occulation assimilation at the Met Office. Q J R Meteorol Soc, 2010, 136:116-131. doi:  10.1002/qj.v136:646
    [20]
    Wang B R, Liu X Y, Wang J K.Asessment of COSMIC radio occultation retrieval product using global radiosonde data. Atmos Meas Tech, 2013, 6:1073-1083, doi: 10.5194/amt-6-1073-2013.
    [21]
    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.
    [22]
    陈德辉, 杨学胜, 胡江林, 等.多尺度通用动力模式框架的设计策略.应用气象学报, 2003, 14(4):452-461. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20030456&flag=1
    [23]
    黄丽萍, 伍湘君, 金之雁.GRAPES模式标准初始化方案设计与实现.应用气象学报, 2005, 16(3):374-384. doi:  10.11898/1001-7313.20050312
    [24]
    万齐林, 薛纪善, 陈子通, 等.雷达TREC风的三维变分同化应用与试验.热带气象学报, 2005, 21(5):449-457. http://www.cnki.com.cn/Article/CJFDTOTAL-RDQX200505000.htm
    [25]
    伍湘君, 金之雁, 黄丽萍, 等.GRAPES模式软件框架与实现.应用气象学报, 2005, 16(4):539-546. doi:  10.11898/1001-7313.20050415
    [26]
    胡江林, 沈学顺, 张红亮, 等.GRAPES模式动力框架的长期积分特征.应用气象学报, 2007, 18(3):276-284. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20070349&flag=1
    [27]
    伍湘君, 金之雁, 陈德辉, 等.新一代数值预报模式GRAPES的并行计算方案设计与实现.计算机研究与发展, 2007, 44(3):510-515. http://www.cnki.com.cn/Article/CJFDTOTAL-JFYZ200703020.htm
    [28]
    万齐林, 薛纪善.曲率修正线性平衡方程及其在变分同化风压约束中的应用.热带气象学报, 2007, 23(5):417-423. http://www.cnki.com.cn/Article/CJFDTOTAL-RDQX200705000.htm
    [29]
    王莉莉, 陈德辉, 赵琳娜.GRAPES气象-水文模式在一次洪水预报中的应用.应用气象学报, 2012, 23(3):274-284. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20120303&flag=1
    [30]
    王瑞春, 龚建东, 张林.GRAPES变分同化系统中动力平衡约束的统计求解.应用气象学报, 2012, 23(2):129-138. doi:  10.11898/1001-7313.20120201
    [31]
    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):524-534. doi:  10.11898/1001-7313.20100502
    [32]
    庄世宇, 薛纪善, 朱国富, 等.GRAPES全球三维变分同化系统-基本设计方案与理想试验.大气科学, 2005, 29(6):872-884. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200506003.htm
    [33]
    Hollingsworth A, Lönnberg P.The statistical structure of short-range forecast errors as determined from radiosonde data.Part Ⅰ:The wind field. Tellus, 1986, 38:111-136. doi:  10.3402/tellusa.v38i2.11707
    [34]
    Xu Q, Li W, Andrew V T, et al.Estimation of three-dimensional error covariances.Part Ⅰ:Analysis of height innovation vectors. Mon Wea Rev, 2001, 129:2126-2135. doi:  10.1175/1520-0493(2001)129<2126:EOTDEC>2.0.CO;2
    [35]
    龚建东, 魏丽, 陶士伟, 等.全球资料同化中误差协方差三维结构的准确估计与应用Ⅰ:观测空间协方差的准确估计.气象学报, 2006, 64(6):669-684. doi:  10.11676/qxxb2006.065
    [36]
    Kuo Y H, Wee T K, Sokolovskiy S, et al.Inversion and error estimation of GPS radio occultation data. J Meteor Soc Japan, 2004, 82(1B):507-531. doi:  10.2151/jmsj.2004.507
    [37]
    Hsieh M E, Chang L Y, Hsiao L F, et al.Impact of Quality Control and Data Thinning of GPS RO Data in WRF-Var on Typhoon Track Forecast.Sixth FORMOSAT-3/COSMIC Data Users' Workshop, NCAR Center Green Campus, 2012.
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    • Received : 2014-05-13
    • Accepted : 2014-09-17
    • Published : 2014-11-30
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