We propose another solution where the forecast and observed fields (for example, precipitation) are decomposed into a set of patterns or features. Pattern recognition techniques can be used to classify coherent structures in the fields (e.g., convective lines, cells, stratiform, and orographic precipitation features). The set of features found in the observed field could then be compared to the set of features found in the forecast field. The joint distribution of forecast and observed features could then be interrogated following the general verification framework developed by Murphy and Winkler (1987). One could also measure errors in displacement, amplitude, areal extent, orientation, etc. of the various types of events classified by this scheme. We also plan to explore techniques developed by other researchers, such as using wavelet transforms to partition the forecast and observed fields into components covering the range of spatial scales (Briggs and Levine, 1997). This allows one to look at the skill of a forecast versus spatial scale, or the contribution by each spatial scale to a given measure of skill. Another method involves examining the statistical structure of the horizontal variability of precipitation as a function of scale in both the observed and forecast fields (Zepeda-Arce et al, 2000). This allows the determination of whether or not the model is capturing the spatial variability of the observed precipitation field. We plan to use the NCEP 22km Eta Model as well as a parallel version of the Eta Model using the Kain-Fritsch convective parameterization in order to develop these techniques, although the verification methods will be applicable to any model.
Supplementary URL: http://www.nssl.noaa.gov/etakf/verf/