• Bannister, R. N., 2008a: A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances. Quart. J. Roy. Meteor. Soc., 134 , 19511970.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bannister, R. N., 2008b: A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics. Quart. J. Roy. Meteor. Soc., 134 , 19711996.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bender, M. A., , Ginnis I. , , Tuleya R. , , Thomas B. , , and Marchok T. , 2007: The operational GFDL coupled hurricane–ocean prediction system and a summary of its performance. Mon. Wea. Rev., 135 , 39653989.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cucurull, L., , Derber J. C. , , Treadon R. , , and Purser R. J. , 2007: Assimilation of global positioning system radio occultation observations into NCEP’s Global Data Assimilation System. Mon. Wea. Rev., 135 , 31743193.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daley, R., , and Barker E. , 2001: NAVDAS: Formulation and diagnostics. Mon. Wea. Rev., 129 , 869883.

  • Dee, D. P., , and da Silva A. M. , 2003: The choice of variable for atmospheric moisture analysis. Mon. Wea. Rev., 131 , 155171.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • De Pondeca, M., and Coauthors, 2007: The status of the real time mesoscale analysis at NCEP. Preprints, 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Park City, UT, Amer. Meteor. Soc., 4A.5. [Available online at http://ams.confex.com/ams/pdfpapers/124364.pdf].

    • Search Google Scholar
    • Export Citation
  • Derber, J. C., , and Bouttier F. , 1999: A reformulation of the background error covariance in the ECMWF Global Data Assimilation System. Tellus, 51A , 195221.

    • Search Google Scholar
    • Export Citation
  • Derber, J. C., , Parrish D. F. , , and Lord S. J. , 1991: The new global operational analysis system at the National Meteorological Center. Wea. Forecasting, 6 , 538547.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Devenyi, D., , Benjamin S. G. , , Middlecoff J. M. , , Schlatter T. W. , , and Weygandt S. S. , 2005: Gridpoint Statistical Interpolation for Rapid Refresh. Preprints, 21st Conf. on Weather Analysis and Forecasting/17th Conf. on Numerical Weather Prediction, Washington, DC, Amer. Meteor. Soc., P1.56. [Available online at http://ams.confex.com/ams/pdfpapers/95149.pdf].

    • Search Google Scholar
    • Export Citation
  • Devenyi, D., , Weygandt S. S. , , Schlatter T. W. , , Benjamin S. G. , , and Hu M. , 2007: Hourly data assimilation with the Gridpoint Statistical Interpolation for Rapid Refresh. Preprints, 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Park City, UT, Amer. Meteor. Soc., 4A.2. [Available online at http://ams.confex.com/ams/pdfpapers/124535.pdf].

    • Search Google Scholar
    • Export Citation
  • Fisher, M., , and Courtier P. , 1995: Estimating the covariance matrices of analysis and forecast error in variational data assimilation. ECMWF Research Dept. Tech. Memo. 220, 26 pp. [Available from European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading RG2 9AX, United Kingdom].

    • Search Google Scholar
    • Export Citation
  • Han, Y., , van Delst P. , , Liu Q. , , Weng F. , , Yan B. , , Treadon R. , , and Derber J. , 2006: JCSDA Community Radiative Transfer Model (CRTM)—Version 1. NOAA Tech. Rep. NESDIS 122, 33 pp.

    • Search Google Scholar
    • Export Citation
  • Holm, E., 2003: Revision of the ECMWF humidity analysis: Construction of a Gaussian control variable. Proc. Workshop on Humidity Analysis, Reading, United Kingdom, ECMWF/GEWEX, 1–6. [Available from European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading RG29AX, United Kingdom].

    • Search Google Scholar
    • Export Citation
  • Kleist, D. T., , Parrish D. F. , , Derber J. C. , , Treadon R. , , Errico R. M. , , and Yang R. , 2009: Improving incremental balance in the GSI 3DVAR analysis system. Mon. Wea. Rev., 137 , 10461060.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenc, A. C., and Coauthors, 2000: The Met. Office global 3-dimensional variational data assimilation scheme. Quart. J. Roy. Meteor. Soc., 126 , 29913012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parrish, D. F., , and Derber J. C. , 1992: The National Meteorological Center’s spectral statistical-interpolation system. Mon. Wea. Rev., 120 , 17471763.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Purser, R. J., , Wu W-S. , , Parrish D. F. , , and Roberts N. M. , 2003a: Numerical aspects of the application of recursive filters to variational statistical analysis. Part I: Spatially homogeneous and isotropic Gaussian covariances. Mon. Wea. Rev., 131 , 15241535.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Purser, R. J., , Wu W-S. , , Parrish D. F. , , and Roberts N. M. , 2003b: Numerical aspects of the application of recursive filters to variational statistical analysis. Part II: Spatially inhomogeneous and anisotropic general covariances. Mon. Wea. Rev., 131 , 15361548.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rabier, F., , McNally A. , , Anderson E. , , Courtier P. , , Unden P. , , Eyre J. , , Hollingsworth A. , , and Bouttier F. , 1998: The ECMWF implementation of three-dimensional variational assimilation (3D-Var). II: Structure functions. Quart. J. Roy. Meteor. Soc., 124 , 18091829.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rabier, F., , Järvinen H. , , Klinker E. , , Mahfouf J-F. , , and Simmons A. , 2000: The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics. Quart. J. Roy. Meteor. Soc., 126 , 11431170.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rawlins, F., , Ballard S. P. , , Bovis K. J. , , Clayton A. M. , , Li D. , , Inverarity G. W. , , Lorenc A. C. , , and Payne T. J. , 2007: The Met Office global four-dimensional variational data assimilation scheme. Quart. J. Roy. Meteor. Soc., 133 , 347362.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, W-S., 2005: Background error for NCEP’s GSI analysis in regional mode. Proc. Fourth Int. Symp. on Analysis of Observations in Meteorology and Oceanography, Prague, Czech Republic, WMO, 3A.27.

    • Search Google Scholar
    • Export Citation
  • Wu, W-S., , Parrish D. F. , , and Purser R. J. , 2002: Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev., 130 , 29052916.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 340 340 46
PDF Downloads 271 271 41

Introduction of the GSI into the NCEP Global Data Assimilation System

View More View Less
  • 1 National Centers for Environmental Prediction Environmental Modeling Center, Camp Springs, Maryland
© Get Permissions
Restricted access

Abstract

At the National Centers for Environmental Prediction (NCEP), a new three-dimensional variational data assimilation (3DVAR) analysis system was implemented into the operational Global Data Assimilation System (GDAS) on 1 May 2007. The new analysis system, the Gridpoint Statistical Interpolation (GSI), replaced the Spectral Statistical Interpolation (SSI) 3DVAR system, which had been operational since 1991. The GSI was developed at the Environmental Modeling Center at NCEP as part of an effort to create a more unified, robust, and efficient analysis scheme. The key aspect of the GSI is that it formulates the analysis in model grid space, which allows for more flexibility in the application of the background error covariances and makes it straightforward for a single analysis system to be used across a broad range of applications, including both global and regional modeling systems and domains.

Due to the constraints of working with an operational system, the final GDAS package included many changes other than just a simple replacing of the SSI with the new GSI. The new GDAS package contained an upgrade to the Global Forecast System model, including a new vertical coordinate, as well as new features in the GSI that were never developed for the SSI. Some of these new features included changes to the observation selection, quality control, minimization algorithm, dynamic balance constraint, and assimilation of new observation types. The evaluation of the new system relative to the SSI-based system was performed for nearly an entire year of analyses and forecasts. The objective and subjective evaluations showed that the new package exhibited superior forecast performance relative to the old SSI-based system. The new system has been shown to improve forecast skill in the tropics and substantially reduce the short-term forecast error in the extratropics. This implementation has laid the groundwork for future scientific advancements in data assimilation at NCEP.

* Additional affiliation: Science Applications International Corporation, Camp Springs, Maryland.

Corresponding author address: Daryl T. Kleist, NOAA Science Center 207, 5200 Auth Rd., Camp Springs, MD 20746-4304. Email: daryl.kleist@noaa.gov

Abstract

At the National Centers for Environmental Prediction (NCEP), a new three-dimensional variational data assimilation (3DVAR) analysis system was implemented into the operational Global Data Assimilation System (GDAS) on 1 May 2007. The new analysis system, the Gridpoint Statistical Interpolation (GSI), replaced the Spectral Statistical Interpolation (SSI) 3DVAR system, which had been operational since 1991. The GSI was developed at the Environmental Modeling Center at NCEP as part of an effort to create a more unified, robust, and efficient analysis scheme. The key aspect of the GSI is that it formulates the analysis in model grid space, which allows for more flexibility in the application of the background error covariances and makes it straightforward for a single analysis system to be used across a broad range of applications, including both global and regional modeling systems and domains.

Due to the constraints of working with an operational system, the final GDAS package included many changes other than just a simple replacing of the SSI with the new GSI. The new GDAS package contained an upgrade to the Global Forecast System model, including a new vertical coordinate, as well as new features in the GSI that were never developed for the SSI. Some of these new features included changes to the observation selection, quality control, minimization algorithm, dynamic balance constraint, and assimilation of new observation types. The evaluation of the new system relative to the SSI-based system was performed for nearly an entire year of analyses and forecasts. The objective and subjective evaluations showed that the new package exhibited superior forecast performance relative to the old SSI-based system. The new system has been shown to improve forecast skill in the tropics and substantially reduce the short-term forecast error in the extratropics. This implementation has laid the groundwork for future scientific advancements in data assimilation at NCEP.

* Additional affiliation: Science Applications International Corporation, Camp Springs, Maryland.

Corresponding author address: Daryl T. Kleist, NOAA Science Center 207, 5200 Auth Rd., Camp Springs, MD 20746-4304. Email: daryl.kleist@noaa.gov

Save