Spatial Forecast Verification: Image Warping

• In response to a growing need for more informative forecast verification in the face of gridded verification sets, many new methods have been proposed. While widely varying in their approaches, the new methods generally fall into two ma- jor categories of filter and displacement, each of which can be further subdivided. One of the displacement approaches, a field deformation approach known as image warping, will be demonstrated here. Results for spatial verification of the spatial forecast verification Inter-Comparison Project test cases are shown. An initial look at space-time verification using the image warp is also discussed, with an applic- ation to NCAR and NCEP 4-km WRF models cases from the 2005 NSSL/SPC Spring Program. The approach is found to be very useful for obtaining guidance about forecast performance. Both diagnostic and summary score information can be gleaned. Initial findings for the space-time approach show that while the NCEP model has better initial scores, the NCAR models require drastically less deform- ation to achieve a much higher reduction in error. This is most likely a result of the NCEP model’s highly over forecasting low-intensity precipitation spatially.

Ämnesord

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

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