Removal of Systematic Model Bias on a Model Grid

Clifford F. Mass Department of Atmospheric Sciences, University of Washington, Seattle, Washington

Search for other papers by Clifford F. Mass in
Current site
Google Scholar
PubMed
Close
,
Jeffrey Baars Department of Atmospheric Sciences, University of Washington, Seattle, Washington

Search for other papers by Jeffrey Baars in
Current site
Google Scholar
PubMed
Close
,
Garrett Wedam Department of Atmospheric Sciences, University of Washington, Seattle, Washington

Search for other papers by Garrett Wedam in
Current site
Google Scholar
PubMed
Close
,
Eric Grimit Department of Atmospheric Sciences, University of Washington, Seattle, Washington

Search for other papers by Eric Grimit in
Current site
Google Scholar
PubMed
Close
, and
Richard Steed Department of Atmospheric Sciences, University of Washington, Seattle, Washington

Search for other papers by Richard Steed in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Virtually all numerical forecast models possess systematic biases. Although attempts to reduce such biases at individual stations using simple statistical corrections have met with some success, there is an acute need for bias reduction on the entire model grid. Such a method should be viable in complex terrain, for locations where gridded high-resolution analyses are not available, and where long climatological records or long-term model forecast grid archives do not exist. This paper describes a systematic bias removal scheme for forecast grids at the surface that is applicable to a wide range of regions and parameters.

Using observational data and model forecasts over the Pacific Northwest, a method was developed to reduce the biases in gridded 2-m temperature, 2-m dewpoint temperature, and 12-h precipitation forecasts. The method first estimates bias at observing locations using errors from forecasts that are similar to the current forecast. These observed biases are then used to estimate bias on the model grid by pairing model grid points with stations that have similar elevation and/or land-use characteristics.

Results show that this approach reduces bias substantially, particularly for periods when biases are large. Adaptations to weather regime changes are made within a short period, and the method essentially “shuts off” when model biases are small. With modest modifications, this approach can be extended to additional variables.

Corresponding author address: Prof. Clifford F. Mass, Dept. of Atmospheric Sciences, University of Washington, Box 351640, Seattle, WA 98195. Email: cliff@atmos.washington.edu

Abstract

Virtually all numerical forecast models possess systematic biases. Although attempts to reduce such biases at individual stations using simple statistical corrections have met with some success, there is an acute need for bias reduction on the entire model grid. Such a method should be viable in complex terrain, for locations where gridded high-resolution analyses are not available, and where long climatological records or long-term model forecast grid archives do not exist. This paper describes a systematic bias removal scheme for forecast grids at the surface that is applicable to a wide range of regions and parameters.

Using observational data and model forecasts over the Pacific Northwest, a method was developed to reduce the biases in gridded 2-m temperature, 2-m dewpoint temperature, and 12-h precipitation forecasts. The method first estimates bias at observing locations using errors from forecasts that are similar to the current forecast. These observed biases are then used to estimate bias on the model grid by pairing model grid points with stations that have similar elevation and/or land-use characteristics.

Results show that this approach reduces bias substantially, particularly for periods when biases are large. Adaptations to weather regime changes are made within a short period, and the method essentially “shuts off” when model biases are small. With modest modifications, this approach can be extended to additional variables.

Corresponding author address: Prof. Clifford F. Mass, Dept. of Atmospheric Sciences, University of Washington, Box 351640, Seattle, WA 98195. Email: cliff@atmos.washington.edu

Save
  • Baars, J. A., and Mass C. F. , 2005: Performance of National Weather Service forecasts compared to operational, consensus, and weighted model output statistics. Wea. Forecasting, 20 , 10341047.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Billam, P. J., cited. 2006: Math::Evol README and manual. [Available online at http://www.pjb.com.au/comp/evol.html.].

  • Cressman, G. P., 1959: An operational objective analysis system. Mon. Wea. Rev., 87 , 367374.

  • Dallavalle, J. P., and Glahn H. R. , 2005: Toward a gridded MOS system. Preprints, 21st Conf on Weather Analysis and Forecasting and 17th Conf. on Numerical Weather Prediction, Washington, DC, Amer. Meteor. Soc., 13B.2. [Available online at http://ams.confex.com/ams/pdfpapers/94998.pdf.].

  • Eckel, F. A., and Mass C. F. , 2005: Effective mesoscale, short-range ensemble forecasting. Wea. Forecasting, 20 , 328350.

  • Glahn, H. R., and Lowry D. A. , 1972: The use of model output statistics (MOS) in objective weather forecasting. J. Appl. Meteor., 11 , 12031211.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Glahn, H. R., and Ruth D. P. , 2003: The new digital forecast database of the National Weather Service. Bull. Amer. Meteor. Soc., 84 , 195201.

  • Homleid, M., 1995: Diurnal corrections of short-term surface temperature forecasts using the Kalman filter. Wea. Forecasting, 10 , 689707.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mass, C., and Coauthors, 2003: Regional environmental prediction over the Pacific Northwest. Bull. Amer. Meteor. Soc., 84 , 13531366.

  • Neilley, P., and Hanson K. A. , 2004: Are model output statistics still needed? Preprints, 20th Conf. on Weather Analysis and Forecasting and 16th Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc., 6.4. [Available online at http://ams.confex.com/ams/pdfpapers/73333.pdf.].

  • Ruth, D., 2002: Interactive forecast preparation—The future has come. Preprints, Interactive Symp. on AWIPS, Orlando, FL, Amer. Meteor. Soc., 3.1. [Available online at http://ams.confex.com/ams/pdfpapers/28371.pdf.].

  • Stensrud, D. J., and Skindlov J. , 1996: Gridpoint predictions of high temperature from a mesoscale model. Wea. Forecasting, 11 , 103110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and Yussouf N. , 2003: Short-range ensemble predictions of 2-m temperature and dewpoint temperature over New England. Mon. Wea. Rev., 131 , 25102524.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, L. J., and Vallée M. , 2002: The Canadian Updateable Model Output Statistics (UMOS) system: Design and development tests. Wea. Forecasting, 17 , 206222.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yussouf, N., and Stensrud D. J. , 2006: Prediction of near-surface variables at independent locations from a bias-corrected ensemble forecasting system. Mon. Wea. Rev., 134 , 34153424.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 455 119 12
PDF Downloads 393 108 13