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Intensity-scale verification technique

Intensity-scale verification technique. B. Casati , G. Ross and D.B. Stephenson (2004) “A New intensity-scale verification approach for the verification of spatial precipitation forecasts”, Meteorol Appl, vol 11, 141-154 pp.

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Intensity-scale verification technique

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  1. Intensity-scale verification technique B. Casati, G. Ross and D.B. Stephenson (2004) “A New intensity-scale verification approach for the verification of spatial precipitation forecasts”, Meteorol Appl, vol 11, 141-154 pp Evaluate the forecast skill as a function of the precipitation intensity and the spatial scale of the error NOTE: scale = single band spatial filter features of different scales  feedback on different physical processes and model parameterizations In the neighborhood based (fuzzy) verification, the scale is the neighborhood size (low band pass filter): as the scale increases the exact positioning requirements are more and more relaxed

  2. Nimrod case study: intense storm displaced Gridded data, square domain with dimension 2n It can be applied to any meteorological field … however, it was specifically designed for spatial precipitation forecasts …

  3. Intensity: threshold to obtain binary images (categorical approach) Binary Analysis Binary Error Image u=1mm/h 1 0 -1 Binary Forecast

  4. mean (1280 km) Scale l=8 (640 km) Scale l=7 (320 km) 1 0 -1 Scale l=6 (160 km) Scale l=5 (80 km) Scale l=4 (40 km) Scale l=3 (20 km) Scale l=2 (10 km) Scale l=1 (5 km) Scale: wavelet decomposition of the binary images

  5. SSu,l 1 0 -1 -2 -3 -4 Intensity-scale skill score For each threshold and scale component: skill score associated to the MSE of binary images ( = HSS) Skill versus random chance, equally partitioned across the scales

  6. Links with categorical verification Binary Analysis Binary Rec.Forecast Overlapping

  7. Strenghts Categorical approach  robust and resistant Wavelets  cope with spatially discontinuous fields characterized by the presence of few sparse non-zero features  suitable for spatial precipitation forecasts Weaknesses need gridded data on a square domain with dimension 2n : work in progress …

  8. Intensity-scale verification technique summary • evaluate the skill as function of precipitation intensity and spatial scale of the error it is capable of isolating specific IS errors (e.g. displaced storm  negative minimum in skill for 160 km scale) in general small scales have negative skill (small scale displacements) and large scales have positive skill • bridges categorical approaches (joint distribution) and scale verification approaches (physical properties) • is suitable for spatial precipitation forecasts: wavelets (discontinuities and features) + categorical approach (robust and resistant)

  9. Spring 2005, 13 May case

  10. Intensity-Scale Skill Score

  11. Confidence intervals, p-values Under-sampling Energy and bias on each scale Random chance can be partitioned across the scales in proportion of magnitude and number of events characterizing the scale … Future work THANK YOU !

  12. Intense storm displaced Skill scale (km) 1 0 -1 -2 -3 -4 640 320 160 80 40 20 10 5 0 1/16 ¼ ½ 1 2 4 8 16 32 threshold = 1mm/h threshold (mm/h) Intensity-scale verification technique Casati et al. (2004), Met App, vol. 11 The intensity-scale verification approach measures the skill as function of precipitation intensity and spatial scale of the error intensity: thresholdbinary images (categorical approach) scale: 2D Wavelets decomposition of binary images For each threshold and scale: skill score associated to the MSE of binary images = Heidke Skill Score

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