MODE (Method for Object-based Diagnostic Evaluation)

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Gridded <strong>for</strong>ecast example: Summary Locations: Forecast objects are • Too far North (except B) • Too far West (except C) Precipitation intensity: • Median intensity is too large B f C f D f Forecast A f POD = 0.27 FAR = 0.75 CSI = 0.34 C o B o D o Observed A o • Extreme (0.90 th) intensity is too small Size: • Forecasts C and D are too small • Forecast B is somewhat too large Matching: • Two small observed objects were not matched
Example: Summary across many <strong>for</strong>ecasts Does precipitation intensity vary between Forecast and Observed objects? Median 0.90 th Quantile S = Stage 4 precip (Observed); W = WRF Forecast
Page 1 and 2: MODE (Method for Object-based Diagn
Page 3 and 4: MODE Object Identification 2 parame
Page 5 and 6: Fuzzy logic mergers Centroid distan
Page 7 and 8: Fuzzy logic mergers orientation
Page 9 and 10: Fuzzy logic matches Overlap Size ra
Page 11 and 12: MODE Strengths and Weaknesses Stre
Page 13 and 14: June 1, 2005 ARW 2-km ARW 4-km Disp
Page 18 and 19: Application examples: Distribution
Page 20 and 21: Quilt plots for different models
Page 22 and 23: Percent of single objects matched 1
Page 24: 2005 0513 2005 0601
Page 30 and 31: 14 12 hit 10 8 6 4 2 false alm miss
Page 33: 1 June 2005 9 Cases

MODE (Method for Object-based Diagnostic Evaluation)

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