Open AccessThe Potential for Self-Organizing Maps to Identify Model Error Structures
Author(s) -
Walter C. Kolczynski,
Joshua P. Hacker
Publication year - 2014
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/mwr-d-13-00189.1
Subject(s) - computer science , perturbation (astronomy) , data assimilation , identification (biology) , systematic error , data mining , balanced flow , environmental science , meteorology , statistics , mathematics , geography , botany , physics , quantum mechanics , biology
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.