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Object-Based Diagnostic Evaluation Method (MODE): Analyzing Perturbed Fake Forecasts

This study explores the Method for Object-based Diagnostic Evaluation (MODE) using test cases that include geometric and perturbed fake forecasts. Results highlight key metrics such as median intensity, percentiles, and centroid distances for matched pairs. By examining varying degrees of perturbation across multiple points, this research aims to identify optimal performance under simulated conditions. Additional insights are provided on area ratios and rainfall totals for each forecasted object, contributing to improved evaluation of forecast accuracy.

dawn-henson
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Object-Based Diagnostic Evaluation Method (MODE): Analyzing Perturbed Fake Forecasts

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  1. Method for Object-based Diagnostic Evaluation (MODE) Fake forecasts

  2. Test case results for MODE • geometric cases • mode quantity A • quantity B • perturbed fake cases • mode quantity A • quantity B • percentile of intensity • median of max • MET – mode_analysis results • centroid distance for matched pairs • percentile intensity for forecast & observed objects

  3. 1 2 3 4 5 Geometric THE WINNER THE WINNER THE WINNER

  4. 1 2 3 4 5 Geometric THE WINNER THE WINNER

  5. 1 2 3 4 5 Geometric THE WINNER

  6. Perturbed fake cases • 3 pts right, 5 pts down • 6 pts right, 10 pts down • 12 pts right, 20 pts down • 24 pts right, 40 pts down • 48 pts right, 80 pts down • 12 pts right, 20 pts down, times 1.5 • 12 pts right, 20 pts down, minus 0.05”

  7. MODE objects

  8. MODE objects

  9. MODE objects

  10. MODE objects

  11. MODE objects

  12. MODE objects

  13. MODE objects

  14. Perturbed Fake

  15. Perturbed Fake

  16. Perturbed Fake cases

  17. Perturbed Fake cases

  18. would like to have… • rainfall total for each object • area ratio defined as fcst area/obs area • not just smaller area/larger area

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