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Weather type dependant fuzzy verification of precipitation

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  1. Weather type dependant fuzzy verification of precipitation Tanja Weusthoff COSMO General Meeting, Offenbach, 07.-11.09.2009

  2. Fuzzy Verification fcst obs • „multi-scale, multi-intensity approach“ • „Fuzzy verification toolbox“ of B. Ebert • Two methods • Upscaling (UP) • Fraction Skill Score (FSS) • present output scale dependent • standard setting: 3h accumulations, Jun-Nov 2007, vs Swiss radar data COSMO-2 (2.2km): leadtimes 03-06 COSMO-7 (6.6km): leadtimes 03-06,06-09,09-12,12-15 increasing box size increasing threshold

  3. … Upscaling (UP) (Atger, 2001) 1. Principle: Define box around region of interest and calculate the average of observation and forecast data within this box. 2. Contingency Table Event if Rave ≥ threshold No-Event if Rave < threshold Rave observation 3. Equitable Threat Score (ETS) forecast Q: Which fraction of observed yes - events was correctly forecast?

  4. … Fraction Skill Score (FSS) 1. Principle: Define box around region of interest and determine the fraction pj and oj of grid points with rain rates above a given threshold. (Roberts and Lean, 2005) 2. Probabilities 0 < pj < 1 = fraction of fcst grid points > threshold 0 < oj < 1 = fraction of obs grid points > threshold 3. Skill Score for Probabilities Q: On which spatial scales does the forecast resemble the observation? FBS worst  no colocation of non-zero fractions

  5. … Fraction Skill Score (FSS) 4. Useful Scales (Roberts and Lean, 2005) useful scales are marked in bold in the graphics

  6. good bad Upscaling and Fractions Skill Score - = Fractions skill score COSMO-2 COSMO-7 Difference - = Upscaling COSMO-2 better COSMO-7 better

  7. abs(median) / 0.5(q95-q05) COSMO-2 better D I F F E R E N C E S COSMO-7 better [ 10 , Inf ] q(50%) [ 5 , 10 [ q(5%) [ 2 , 5 [ q(95%) ¦ COSMO-2 – COSMO-7 ¦ [ 1 , 2 [ [ 0 , 1 [ 5% 95% COSMO-2 vs. COSMO-7, bootstrapping Values = Score of COSMO-2 • Size of numbers = abs(Median) / 0.5(q95-q05) • measure for significance of differences

  8. COSMO-2 better D I F F E R E N C E S COSMO-7 better Sensitivities Only 00 and 12 UTC model runs COSMO-2 & COSMO-7: leadtimes 3-6,6-9,9-12,12-15  absolute values of COSMO-2 slightly lower, but still the same pattern of differences

  9. Weather type verification: COSMO-2 Upscaling Window size: 3gp, 6.6 km Window size: 27gp, 60 km Fractions Skill Score Window size: 3gp, 6.6 km Window size: 27gp, 60 km Threshold [mm/3h] 34 7 # cases 4 7 5 5 7 45 31 23 15

  10. Smallest spatial scale [gridpoints] where the forecast has been useful regarding to FSS „useful scales“ definition. COSMO-2, gridpoints* 2.2 km COSMO-7, gridpoints* 6.6 km # cases - 7 4 + 23 + 5 7 45 5 31 - 15 34 7 16 14 10 7 5 2 0.5 <0.1 16 14 10 7 5 2 0.4 <0.1 % obs gridpts >= thresh (whole period)

  11. 45 34 31 23 15 7 7 7 4 5 5 Frequency of Weather Classes, June – November 2007

  12. COSMO-2 better D I F F E R E N C E S COSMO-2 (wc) better D I F F E R E N C E S COSMO-7 better COSMO-2 (all) better Northerly Winds (NE,N) 11 days COSMO-2 (wc) vs.COSMO-2 (all) COSMO-2 vs.COSMO-7  COSMO-2 in northerly wind situations clearly worse than over whole period.

  13. COSMO-2 better D I F F E R E N C E S COSMO-2 (wc) better D I F F E R E N C E S COSMO-7 better COSMO-2 (all) better Southerly Winds (SE,S) 12 days COSMO-2 (wc) vs.COSMO-2 (all) COSMO-2 vs.COSMO-7  COSMO-2 in southerly wind situations clearly better than over whole period.

  14. COSMO-2 better D I F F E R E N C E S COSMO-2 (wc) better D I F F E R E N C E S COSMO-7 better COSMO-2 (all) better Northwesterly winds (NW) 23 days COSMO-2 (wc) vs.COSMO-2 (all) COSMO-2 vs.COSMO-7  COSMO-2 in nothwesterly wind situations at large thresholds clearly better than over whole period.

  15. COSMO-2 better D I F F E R E N C E S COSMO-2 (wc) better D I F F E R E N C E S COSMO-7 better COSMO-2 (all) better Flat (F) 15 days COSMO-2 (wc) vs.COSMO-2 (all) COSMO-2 vs.COSMO-7  COSMO-2 in flat pressure situations clearly worse than over whole period.

  16. Summary & Outlook • Fuzzy verification of the 6 months period in 2007 has shown that COSMO-2 generally performed better than COSMO-7 on nearly all scales • part of this superiority is caused by the higher update frequency, but using same model runs still shows the same pattern of differences • the results for different weather types show large variations • best results were found for southerly winds and winds from Northwest and West, • Northeasterly winds as well as Flat pressure situations lead to worse perfomance of both models • operational Fuzzy verification is about to start, including Upscaling and Fraction Skill Score