The Potential for Self-Organizing Maps to Identify Model Error Structures

Walter C. Kolczynski Jr. Department of Meteorology, Naval Postgraduate School, Monterey, California

Search for other papers by Walter C. Kolczynski Jr. in
Current site
Google Scholar
PubMed
Close
and
Joshua P. Hacker Department of Meteorology, Naval Postgraduate School, Monterey, California

Search for other papers by Joshua P. Hacker in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

An important aspect of numerical weather model improvement is the identification of deficient areas of the model, particularly deficiencies that are flow dependent or otherwise vary in time or space. Here the authors introduce the use of self-organizing maps (SOMs) and analysis increments from data assimilation to identify model deficiencies. Systematic increments reveal time- and space-dependent systematic errors, while SOMs provide a method for categorizing forecasts or increment patterns. The SOMs can be either used for direct analysis or used to produce composites of other fields. This study uses the forecasts and increments of 2-m temperature and dry column mass perturbation μ over a 4-week period to demonstrate the potential of this technique. Results demonstrate the potential of this technique for identifying spatially varying systematic model errors.

Corresponding author address: Walter C. Kolczynski Jr., Department of Meteorology, Naval Postgraduate School, 253 Root Hall, Monterey, CA 93943. E-mail: walter.kolczynski@gmail.com

Abstract

An important aspect of numerical weather model improvement is the identification of deficient areas of the model, particularly deficiencies that are flow dependent or otherwise vary in time or space. Here the authors introduce the use of self-organizing maps (SOMs) and analysis increments from data assimilation to identify model deficiencies. Systematic increments reveal time- and space-dependent systematic errors, while SOMs provide a method for categorizing forecasts or increment patterns. The SOMs can be either used for direct analysis or used to produce composites of other fields. This study uses the forecasts and increments of 2-m temperature and dry column mass perturbation μ over a 4-week period to demonstrate the potential of this technique. Results demonstrate the potential of this technique for identifying spatially varying systematic model errors.

Corresponding author address: Walter C. Kolczynski Jr., Department of Meteorology, Naval Postgraduate School, 253 Root Hall, Monterey, CA 93943. E-mail: walter.kolczynski@gmail.com
Save
  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 28842903, doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., 2003: A local least squares framework for ensemble filtering. Mon. Wea. Rev., 131, 634642, doi:10.1175/1520-0493(2003)131<0634:ALLSFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., 2009: Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus, 61A, 7283, doi:10.1111/j.1600-0870.2008.00361.x.

    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 12831296, doi:10.1175/2009BAMS2618.1.

    • Search Google Scholar
    • Export Citation
  • Cavazos, T., A. Comrie, and D. Liverman, 2002: Intraseasonal variability associated with wet monsoons in southeast Arizona. J. Climate, 15, 24772490, doi:10.1175/1520-0442(2002)015<2477:IVAWWM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chang, F.-J., L.-C. Chang, H.-S. Kao, and G.-R. Wu, 2010: Assessing the effort of meteorological variables for evaporation estimation by self-organizing map neural network. J. Hydrol., 384, 118129, doi:10.1016/j.jhydrol.2010.01.016.

    • Search Google Scholar
    • Export Citation
  • Crane, R., and B. Hewitson, 2003: Clustering and upscaling of station precipitation records to regional patterns using self-organizing maps (SOMs). Climate Res., 25, 95107, doi:10.3354/cr025095.

    • Search Google Scholar
    • Export Citation
  • Danforth, C. M., and E. Kalnay, 2008: Using singular value decomposition to parameterize state-dependent model errors. J. Atmos. Sci., 65, 14671478, doi:10.1175/2007JAS2419.1.

    • Search Google Scholar
    • Export Citation
  • Development Testbed Center, cited 2013: Reference configuration WRFv3.3.1 ARW PS:4.1.1.1.2.1.1. [Available online at http://www.dtcenter.org/config/v3.3.1/ARW_PS4.1.1.1.2.1.1/index.php.]

  • Fassnacht, S. R., and J. E. Derry, 2010: Defining similar regions of snow in the Colorado River Basin using self-organizing maps. Water Resour. Res., 46, W04507, doi:10.1029/2009WR007835.

    • Search Google Scholar
    • Export Citation
  • Gaspari, G., and S. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723757, doi:10.1002/qj.49712555417.

    • Search Google Scholar
    • Export Citation
  • Hacker, J. P., and W. M. Angevine, 2013: Ensemble data assimilation to characterize surface-layer errors in numerical weather prediction models. Mon. Wea. Rev., 141, 1804–1821, doi:10.1175/MWR-D-12-00280.1.

    • Search Google Scholar
    • Export Citation
  • Hewitson, B. C., and R. G. Crane, 2002: Self-organizing maps: Applications to synoptic climatology. Climate Res., 22, 1326, doi:10.3354/cr022013.

    • Search Google Scholar
    • Export Citation
  • Hogan, T., T. Rosmond, and R. Gelaro, 1991: The NOGAPS forecast model: A technical description. Tech. Doc. AD-A247-216, Navy Operational Global Atmospheric Prediction System, 218 pp.

  • Johnson, N. C., S. B. Feldstein, and B. Tremblay, 2008: The continuum of Northern Hemisphere teleconnection patterns and a description of the NAO shift with the use of self-organizing maps. J. Climate, 21, 63546371, doi:10.1175/2008JCLI2380.1.

    • Search Google Scholar
    • Export Citation
  • Kohonen, T., 1982: Self-organized formation of topologically correct feature maps. Biol. Cybern., 43, 5969, doi:10.1007/BF00337288.

  • Liu, Y., and R. H. Weisberg, 2011: A review of self-organizing map applications in meteorology and oceanography. Potential Forecast Skill of Ensemble Prediction and Spread and Skill Distributions of the ECMWF Ensemble Prediction System, J. I. Mwasiagi, Ed., InTech, 253–272.

  • Malmgren, B. A., and A. Winter, 1999: Climate zonation in Puerto Rico based on principal components analysis and an artificial neural network. J. Climate, 12, 977985, doi:10.1175/1520-0442(1999)012<0977:CZIPRB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343360, doi:10.1175/BAMS-87-3-343.

    • Search Google Scholar
    • Export Citation
  • Michaelides, S. C., F. Liassidou, and C. N. Schizas, 2007: Synoptic classification and establishment of analogues with artificial neural networks. Pure Appl. Geophys., 164, 13471364, doi:10.1007/s00024-007-0222-7.

    • Search Google Scholar
    • Export Citation
  • NCAR Command Language, 2013: Version 6.1.2. Boulder, CO, UCAR/NCAR/CISL/VETS, doi:10.5065/D6WD3XH5.

  • Rodwell, M. J., and T. N. Palmer, 2007: Using numerical weather prediction to assess climate models. Quart. J. Roy. Meteor. Soc., 133, 129146, doi:10.1002/qj.23.

    • Search Google Scholar
    • Export Citation
  • Schuenemann, K. C., and J. J. Cassano, 2009: Changes in synoptic weather patterns and Greenland precipitation in the 20th and 21st centuries: 1. Evaluation of late 20th century simulations from IPCC models. J. Geophys. Res., 114, D20113, doi:10.1029/2009JD011705.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., J. B. Klemp, D. O. Gill, D. M. Barker, M. G. Duda, W. Wang, and J. G. Powers, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note TN-475+STR, NCAR, 125 pp.

  • Tian, B., M. A. Shaikh, M. R. Azimi-Sadjadi, T. H. Vonder Haar, and D. L. Reinke, 1999: A study of cloud classification with neural networks using spectral and textural features. IEEE Trans. Neural Networks, 10, 138151, doi:10.1109/72.737500.

    • Search Google Scholar
    • Export Citation
  • Walder, P., and I. MacLaren, 2000: Neural network based methods for cloud classification on AVHRR images. Int. J. Remote Sens., 21, 16931708, doi:10.1080/014311600209977.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 238 55 4
PDF Downloads 173 42 2