• Alexandru, A., , R. de Elia, , R. Laprise, , L. Separovic, , and S. Biner, 2009: Sensitivity study of regional climate model simulations to large-scale nudging parameters. Mon. Wea. Rev., 137, 16661686.

    • Search Google Scholar
    • Export Citation
  • Bukovsky, M. S., , and D. J. Karoly, 2007: A brief evaluation of precipitation from the North American Regional Reanalysis. J. Hydrometeor., 8, 837846.

    • Search Google Scholar
    • Export Citation
  • Bukovsky, M. S., , and D. J. Karoly, 2009: Precipitation simulations using WRF as a nested regional climate model. J. Appl. Meteor. Climatol., 48, 21522159.

    • Search Google Scholar
    • Export Citation
  • Castro, C. L., , R. A. Pielke Sr., , and G. Leoncini, 2005: Dynamical downscaling: Assessment of value retained and added using the Regional Atmospheric Modeling System (RAMS). J. Geophys. Res., 110, D05108, doi:10.1029/2004JD004721.

    • Search Google Scholar
    • Export Citation
  • Chen, F., , and J. Dudhia, 2001: Coupling an advanced land surface–hydrology model with the Penn State NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569585.

    • Search Google Scholar
    • Export Citation
  • Davies, H. C., 1976: A lateral boundary formulation for multi-level prediction models. Quart. J. Roy. Meteor. Soc., 102, 405418, doi: 10.1002/qj.49710243210.

    • Search Google Scholar
    • Export Citation
  • de Elía, R., and Coauthors, 2008: Evaluation of uncertainties in the CRCM-simulated North American climate. Climate Dyn., 30, 113132, doi:10.1007/s00382-007-0288-z.

    • Search Google Scholar
    • Export Citation
  • Denis, B., , R. Laprise, , D. Caya, , and J. Côté, 2002: Downscaling ability of one-way nested regional climate models: The Big-Brother Experiment. Climate Dyn., 18, 627646, doi:10.1007/s00382-001-0201-0.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., 2006: Regional climate modeling: Status and perspectives. J. Phys. IV, 139, 101118, doi:10.1051/jp4:2006139008.

  • Kanamitsu, M., , W. Ebisuzaki, , J. Woollen, , S.-K. Yang, , J. J. Hnilo, , M. Fiorino, , and G. L. Potter, 2002: NCEP–DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 16311643.

    • Search Google Scholar
    • Export Citation
  • Kucharski, F., , I. Kang, , D. Straus, , and M. P. King, 2010: Teleconnections in the atmosphere and oceans. Bull. Amer. Meteor. Soc., 91, 381383.

    • Search Google Scholar
    • Export Citation
  • Laprise, R., and Coauthors, 2008: Challenging some tenets of regional climate modelling. Meteor. Atmos. Phys., 100, 322, doi:10.1007/s00703-008-0292-9.

    • Search Google Scholar
    • Export Citation
  • Leung, L. R., , and W. I. Gustafson Jr., 2005: Potential regional climate change and implications to U.S. air quality. Geophys. Res. Lett., 32, L16711, doi:10.1029/2005GL022911.

    • Search Google Scholar
    • Export Citation
  • Leung, L. R., , and Y. Qian, 2009: Atmospheric rivers induced heavy precipitation and flooding in the western U.S. simulated by the WRF regional climate model. Geophys. Res. Lett., 36, L03820, doi:10.1029/2008GL036445.

    • Search Google Scholar
    • Export Citation
  • Leung, L. R., , Y.-H. Kuo, , and J. Tribbia, 2006: Research needs and directions of regional climate modeling using WRF and CCSM. Bull. Amer. Meteor. Soc., 87, 17471751.

    • Search Google Scholar
    • Export Citation
  • Lo, J. C.-F., , Z.-L. Yang, , and R. A. Pielke Sr., 2008: Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model. J. Geophys. Res., 113, D09112, doi:10.1029/2007JD009216.

    • Search Google Scholar
    • Export Citation
  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343360.

  • Miguez-Macho, G., , G. L. Stenchikov, , and A. Robock, 2004: Spectral nudging to eliminate the effects of domain position and geometry in regional climate model simulations. J. Geophys. Res., 109, D13104, doi:10.1029/2003JD004495.

    • Search Google Scholar
    • Export Citation
  • Miguez-Macho, G., , G. L. Stenchikov, , and A. Robock, 2005: Regional climate simulations over North America: Interaction of local processes with improved large-scale flow. J. Climate, 18, 12271246.

    • Search Google Scholar
    • Export Citation
  • Namias, J., 1991: Spring and summer 1988 drought over the contiguous United States: Causes and prediction. J. Climate, 4, 5465.

  • Nolte, C. G., , A. B. Gilliland, , C. Hogrefe, , and L. J. Mickley, 2008: Linking global to regional models to assess future climate impacts on surface ozone levels in the United States. J. Geophys. Res., 113, D14307, doi:10.1029/2007JD008497.

    • Search Google Scholar
    • Export Citation
  • Otte, T. L., 2008: The impact of nudging in the meteorological model for retrospective air quality simulations. Part I: Evaluation against national observation networks. J. Appl. Meteor. Climatol., 47, 18531867.

    • Search Google Scholar
    • Export Citation
  • Pielke, R. A., Sr., 2002: Mesoscale Meteorological Modeling. 2nd ed. International Geophysics Series, Vol. 78, Academic Press, 676 pp.

  • Pleim, J. E., , J. O. Young, , D. Wong, , R. C. Gilliam, , T. L. Otte, , and R. Mathur, 2008: Two-way coupled meteorology and air quality modeling. Air Pollution Modeling and Its Applications XIX, C. Borrego and A. I. Miranda, Eds., NATO Science for Peace and Security Series, Vol. 19, Springer, 235–242.

  • Rockel, B., , C. L. Castro, , R. A. Pielke Sr., , H. von Storch, , and G. Leoncini, 2008: Dynamical downscaling: Assessment of model system dependent retained and added variability for two different regional climate models. J. Geophys. Res., 113, D21107, doi:10.1029/2007JD009461.

    • Search Google Scholar
    • Export Citation
  • Salathé, E. P., , R. Steed, , C. F. Mass, , and P. H. Zahn, 2008: A high-resolution climate model for the U.S. Pacific Northwest: Mesoscale feedbacks and local responses to climate change. J. Climate, 21, 57085726.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 30193032.

  • Skamarock, W. C., and Coauthors, 2008: A description of the advanced research WRF version 3. NCAR Tech. Rep. NCAR/TN-475+STR, 113 pp.

  • Stauffer, D. R., , and N. L. Seaman, 1994: Multiscale four-dimensional data assimilation. J. Appl. Meteor., 33, 416434.

  • Trenberth, K. E., , and C. J. Guillemot, 1996: Physical processes involved in the 1988 drought and 1993 floods in North America. J. Climate, 9, 12881298.

    • Search Google Scholar
    • Export Citation
  • von Storch, H., , H. Langenberg, , and F. Feser, 2000: A spectral nudging technique for dynamical downscaling purposes. Mon. Wea. Rev., 128, 36643673.

    • Search Google Scholar
    • Export Citation
  • Yin, J. H., 2005: A consistent poleward shift of the storm tracks in simulations of 21st century climate. Geophys. Res. Lett., 32, L18701, doi:10.1029/2005GL023684.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 167 167 33
PDF Downloads 137 137 27

Examining Interior Grid Nudging Techniques Using Two-Way Nesting in the WRF Model for Regional Climate Modeling

View More View Less
  • 1 Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina
© Get Permissions
Restricted access

Abstract

This study evaluates interior nudging techniques using the Weather Research and Forecasting (WRF) model for regional climate modeling over the conterminous United States (CONUS) using a two-way nested configuration. NCEP–Department of Energy Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (R-2) data are downscaled to 36 km × 36 km by nudging only at the lateral boundaries, using gridpoint (i.e., analysis) nudging and using spectral nudging. Seven annual simulations are conducted and evaluated for 1988 by comparing 2-m temperature, precipitation, 500-hPa geopotential height, and 850-hPa meridional wind to the 32-km North American Regional Reanalysis (NARR). Using interior nudging reduces the mean biases for those fields throughout the CONUS compared to the simulation without interior nudging. The predictions of 2-m temperature and fields aloft behave similarly when either analysis or spectral nudging is used. For precipitation, however, analysis nudging generates monthly precipitation totals, and intensity and frequency of precipitation that are closer to observed fields than spectral nudging. The spectrum of 250-hPa zonal winds simulated by the WRF model is also compared to that of the R-2 and NARR. The spatial variability in the WRF model is reduced by using either form of interior nudging, and analysis nudging suppresses that variability more strongly than spectral nudging. Reducing the nudging strengths on the inner domain increases the variability but generates larger biases. The results support the use of interior nudging on both domains of a two-way nest to reduce error when the inner nest is not otherwise dominated by the lateral boundary forcing. Nevertheless, additional research is required to optimize the balance between accuracy and variability in choosing a nudging strategy.

Corresponding author address: Dr. Jared H. Bowden, Institute for the Environment, University of North Carolina at Chapel Hill, CB#6116, 137 E. Franklin St., Chapel Hill, NC 27599. E-mail: jhbowden@unc.edu

Abstract

This study evaluates interior nudging techniques using the Weather Research and Forecasting (WRF) model for regional climate modeling over the conterminous United States (CONUS) using a two-way nested configuration. NCEP–Department of Energy Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (R-2) data are downscaled to 36 km × 36 km by nudging only at the lateral boundaries, using gridpoint (i.e., analysis) nudging and using spectral nudging. Seven annual simulations are conducted and evaluated for 1988 by comparing 2-m temperature, precipitation, 500-hPa geopotential height, and 850-hPa meridional wind to the 32-km North American Regional Reanalysis (NARR). Using interior nudging reduces the mean biases for those fields throughout the CONUS compared to the simulation without interior nudging. The predictions of 2-m temperature and fields aloft behave similarly when either analysis or spectral nudging is used. For precipitation, however, analysis nudging generates monthly precipitation totals, and intensity and frequency of precipitation that are closer to observed fields than spectral nudging. The spectrum of 250-hPa zonal winds simulated by the WRF model is also compared to that of the R-2 and NARR. The spatial variability in the WRF model is reduced by using either form of interior nudging, and analysis nudging suppresses that variability more strongly than spectral nudging. Reducing the nudging strengths on the inner domain increases the variability but generates larger biases. The results support the use of interior nudging on both domains of a two-way nest to reduce error when the inner nest is not otherwise dominated by the lateral boundary forcing. Nevertheless, additional research is required to optimize the balance between accuracy and variability in choosing a nudging strategy.

Corresponding author address: Dr. Jared H. Bowden, Institute for the Environment, University of North Carolina at Chapel Hill, CB#6116, 137 E. Franklin St., Chapel Hill, NC 27599. E-mail: jhbowden@unc.edu
Save