Overlapping Interests: The Impact of Geographic Coordinate Assumptions on Limited-Area Atmospheric Model Simulations

Andrew J. Monaghan National Center for Atmospheric Research,* Boulder, Colorado

Search for other papers by Andrew J. Monaghan in
Current site
Google Scholar
PubMed
Close
,
Michael Barlage National Center for Atmospheric Research,* Boulder, Colorado

Search for other papers by Michael Barlage in
Current site
Google Scholar
PubMed
Close
,
Jennifer Boehnert National Center for Atmospheric Research,* Boulder, Colorado

Search for other papers by Jennifer Boehnert in
Current site
Google Scholar
PubMed
Close
,
Cody L. Phillips National Center for Atmospheric Research,* Boulder, Colorado

Search for other papers by Cody L. Phillips in
Current site
Google Scholar
PubMed
Close
, and
Olga V. Wilhelmi National Center for Atmospheric Research,* Boulder, Colorado

Search for other papers by Olga V. Wilhelmi in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

There is growing use of limited-area models (LAMs) for high-resolution (<10 km) applications, for which consistent mapping of input terrestrial and meteorological datasets is critical for accurate simulations. The geographic coordinate systems of most input datasets are based on spheroid-shaped (i.e., elliptical) Earth models, while LAMs generally assume a perfectly sphere-shaped Earth. This distinction is often neglected during preprocessing, when input data are remapped to LAM domains, leading to geolocation discrepancies that can exceed 20 km at midlatitudes.

A variety of terrestrial (topography and land use) input dataset configurations is employed to explore the impact of Earth model assumptions on a series of 1-km LAM simulations over Colorado. For the same terrestrial datasets, the ~20-km geolocation discrepancy between spheroidal-versus-spherical Earth models over the domain leads to simulated differences in near-surface and midtropospheric air temperature, humidity, and wind speed that are larger and more widespread than those due to using different topography and land use datasets altogether but not changing the Earth model. Simulated differences are caused by the shift of static fields with respect to boundary conditions, and altered Coriolis forcing and topographic gradients.

The sensitivity of high-resolution LAM simulations to Earth model assumptions emphasizes the importance for users to ensure terrestrial and meteorological input data are consistently mapped during preprocessing (i.e., datasets share a common geographic coordinate system before remapping to the LAM domain). Concurrently, the modeling community should update preprocessing systems to make sure input data are correctly mapped for all global and limited-area simulation domains.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Andrew J. Monaghan, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000. E-mail: monaghan@ucar.edu

Abstract

There is growing use of limited-area models (LAMs) for high-resolution (<10 km) applications, for which consistent mapping of input terrestrial and meteorological datasets is critical for accurate simulations. The geographic coordinate systems of most input datasets are based on spheroid-shaped (i.e., elliptical) Earth models, while LAMs generally assume a perfectly sphere-shaped Earth. This distinction is often neglected during preprocessing, when input data are remapped to LAM domains, leading to geolocation discrepancies that can exceed 20 km at midlatitudes.

A variety of terrestrial (topography and land use) input dataset configurations is employed to explore the impact of Earth model assumptions on a series of 1-km LAM simulations over Colorado. For the same terrestrial datasets, the ~20-km geolocation discrepancy between spheroidal-versus-spherical Earth models over the domain leads to simulated differences in near-surface and midtropospheric air temperature, humidity, and wind speed that are larger and more widespread than those due to using different topography and land use datasets altogether but not changing the Earth model. Simulated differences are caused by the shift of static fields with respect to boundary conditions, and altered Coriolis forcing and topographic gradients.

The sensitivity of high-resolution LAM simulations to Earth model assumptions emphasizes the importance for users to ensure terrestrial and meteorological input data are consistently mapped during preprocessing (i.e., datasets share a common geographic coordinate system before remapping to the LAM domain). Concurrently, the modeling community should update preprocessing systems to make sure input data are correctly mapped for all global and limited-area simulation domains.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Andrew J. Monaghan, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000. E-mail: monaghan@ucar.edu
Save
  • Bugayevskiy, L. M., and J. P. Snyder, 1995: Map Projections: A Reference Manual. Taylor & Francis Inc., 333 pp.

  • 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, 569586.

    • Search Google Scholar
    • Export Citation
  • Chen, F., and Coauthors, 2011: The integrated WRF/urban modeling system: Development, evaluation, and applications to urban environmental problems. Int. J. Climatol., 31, 273288, doi:10.1002/joc.2158.

    • Search Google Scholar
    • Export Citation
  • David, C. H., D. J. Gochis, D. R. Maidment, W. Yu, D. N. Yates, and Z.-L. Yang, 2009: Using NHDPlus as the land base for the Noah-distributed Model. Trans. GIS, 13, 363377.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 30773107.

    • Search Google Scholar
    • Export Citation
  • Farr, T. G., and Coauthors, 2007: The Shuttle Radar Topography Mission. Rev. Geophys., 45, RG2004, doi:10.1029/2005RG000183.

  • Fry, J., and Coauthors, 2011: Completion of the 2006 National Land Cover Database for the conterminous United States. Photogramm. Eng. Remote Sens., 77, 858864.

    • Search Google Scholar
    • Export Citation
  • Gesch, D., and S. Greenlee, cited 1996: GTOPO30 documentation. [Available online at http://webgis.wr.usgs.gov/globalgis/gtopo30/gtopo30.htm.]

  • Hahmann, A. N., D. Rostkier-Edelstein, T. T. Warner, F. Vandenberghe, Y. Liu, R. Babarsky, and S. P. Swerdlin, 2010: A reanalysis system for the generation of mesoscale climatographies. J. Appl. Meteor. Climatol., 49, 954972.

    • Search Google Scholar
    • Export Citation
  • Hedgley, D. R., 1976: An exact transformation from geocentric to geodetic coordinates for nonzero altitudes. NASA-TR-458, 17 pp.

  • Im, U., and Coauthors, 2010: Study of a winter PM episode in Istanbul using the high resolution WRF/CMAQ modeling system. Atmos. Environ., 44, 30853094.

    • Search Google Scholar
    • Export Citation
  • Janjic, Z. I., 2002: Nonsingular Implementation of the Mellor–Yamada level 2.5 scheme in the NCEP Meso model. NCEP Office Note 437, 61 pp.

  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181.

  • Lu, W., S. Zhong, J. J. Charney, X. Bian, and S. Liu, 2012: WRF simulation over complex terrain during a southern California wildfire event. J. Geophys. Res., 117, D05125, doi:10.1029/2011JD017004.

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

  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long wave. J. Geophys. Res., 102 (D14), 16 66316 682.

    • Search Google Scholar
    • Export Citation
  • Monaghan, A. J., K. MacMillan, S. M. Moore, P. S. Mead, M. H. Hayden, and R. J. Eisen, 2012: A regional climatography of West Nile, Uganda, to support human plague modeling. J. Appl. Meteor. Climatol., 51, 12011221.

    • Search Google Scholar
    • Export Citation
  • Pearson, F., 1990: Map Projections: Theory and Applications. CRC Press, Inc., 372 pp.

  • Rasmussen, R., and Coauthors, 2011: High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado: A process study of current and warmer climate. J. Climate, 24, 30153048.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and J. B. Klemp, 2008: A time-split nonhydrostatic atmospheric model for research and NWP applications. J. Comput. Phys., 227, 34653485.

    • Search Google Scholar
    • Export Citation
  • Taylor, A., cited 2012: Which datum do you use for the output from your models? [Available online at http://www.arl.noaa.gov/faq_geodatums.php.]

  • Thompson, G., R. M. Rasmussen, and K. Manning, 2004: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea. Rev., 132, 519542.

    • Search Google Scholar
    • Export Citation
  • Trier, S. B., M. A. LeMone, F. Chen, and K. W. Manning, 2011: Effects of surface heat and moisture exchange on ARW-WRF warm-season precipitation forecasts over the central United States. Wea. Forecasting, 26, 325.

    • Search Google Scholar
    • Export Citation
  • Warner, T. T., 2011: Quality assurance in atmospheric modeling. Bull. Amer. Meteor. Soc., 92, 16011610.

  • Warner, T. T., R. A. Peterson, and R. E. Treadon, 1997: A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical weather prediction. Bull. Amer. Meteor. Soc., 78, 25992617.

    • Search Google Scholar
    • Export Citation
  • WRF Development Team, 2012: ARW version 3 modeling system user’s guide. National Center for Atmospheric Research, 371 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/user_guide_V3.3/ARWUsersGuideV3.pdf.]

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 968 268 51
PDF Downloads 940 252 36