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Priority project « Advanced interpretation and verification of very high resolution models »

Priority project « Advanced interpretation and verification of very high resolution models ». Topics. Advanced postprocessing of weather parameters Verification of very high resolution models, incl. fuzzy verification methods Hydrological applications. 3. Hydrological applications.

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Priority project « Advanced interpretation and verification of very high resolution models »

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  1. Priority project « Advanced interpretation and verification of very high resolution models »

  2. Topics • Advanced postprocessing of weather parameters • Verification of very high resolution models, incl. fuzzy verification methods • Hydrological applications

  3. 3. Hydrological applications Hydrology (precipitation adaptation): Presentation by A. Mazur Snow parametrisation: Presentation by E. Machulskaya

  4. 1. Recognition of weather elements • Done last year: recognition of thunderstorms with the boosting algorithm: • Choice of predictors Perler, Kohli, Walser

  5. Kalman filtering of COSMO LEPS V. Stauch, poster outside

  6. 2. Verification of very high resolution models • Goals • 1-3 km scale (VHR) • Focus on precipitation • Is VHR (~2km) better than HR (~7km)? • Model intercomparison • Generate products related to the verification • Way to define the scores could depend on the application (value) • Use synop, (high resolution rainguage network), radar, evt. composition of all (gridded observations)

  7. Mesoscale model (5 km) 21 Mar 2004 Global model (100 km) 21 Mar 2004 Observed 24h rain Sydney Sydney RMS=13.0 RMS=4.6 Motivation Which rain forecast would you rather use? B. Ebert

  8. 7km 2km Motivation: precipitation pattern

  9. Fuzzy Verification F. Ament Verification on coarser scales than model scale: “Do not require a point wise match!“

  10. Expected behaviour of scores From Nigel Roberts (2005)

  11. Application of scores to a perfect forecast All scores should equal ! But, in fact, 5 out of 12 do not!

  12. Requested theoretical properties of scores • Avoid « leaking » scores • Use illustrative and understandable scores • Score should give a real information of the forecast quality on the different scales • Monotonic behavior concerning • scale (best values for large scales) • frequency of occurrence (best values for high frequencies of occurrence) • Represent some significant characteristics of the PDF (obs and forecast)

  13. Requested practical properties of scores • Agreement between subjective and objective judgment • Possible help in decision making • Correspond to the needs of the users • Should be able to provide a comparison between 2km and 7 km models (also global models) • Should not use a matching between prediction and observation because it would not allow the generation of univocal products

  14. Chosen scores • Our best candidates: • Upscaling and Fraction skill score • Corresponding products • Upscaling mean around a point / station • Fraction skill score probability to exceed some threshold in a neighbourhood

  15. good bad Fuzzy Verification: COSMO-DE – COSMO-EU JJA 2007, Verification against Swiss Radar Composite, 3 hourly accumulations - 90 58 33 20 7 = Upscaling Spatial scale (km) Difference COSMO-DE (2.8km) COSMO-EU (7km) - 90 58 33 20 7 = Fraction skill score Spatial scale (km) Threshold (mm/3h) Threshold (mm/3h) Threshold (mm/3h) COSMO-EU better COSMO-DE better

  16. good bad Fuzzy Verification COSMO-2 – COSMO-7 JJA 2007, Verification against Swiss Radar Composite, 3 hourly accumulations - 90 58 33 20 7 = Upscaling Spatial scale (km) Difference COSMO-2 (2.2km) COSMO-7 (7km) - 90 58 33 20 7 = Fraction skill score Spatial scale (km) Threshold (mm/3h) Threshold (mm/3h) Threshold (mm/3h) COSMO-7 better COSMO-2 better

  17. Monthly dependency cut-off 03h, accumulation 03h COSMO-2- COSMO-7 COSMO-DE - COSMO-EU June July August

  18. Quarterly summaries of „Fuzzy“-scores FSS Autumn 2007 U. Damrath

  19. Monthly summaries of „Fuzzy“-scores FSS July 2007

  20. Average number of stations in each area ( SON 2007) X Analysis of precipitation in boxes • We devised a verification methodology by aggregating observed and predicted precipitation in boxes of 1°x 1°(labelled boxes in the map) • The choice of the size and position of the areas has been performed according to different rules: • boxes have to be enough large in order to contain a high number of observation points (ranging from 20 to over 100, depending on location and period of time considered) • boxes have to be homogeneous as much as possible in terms of geographic-territorial characteristics M.-S. TesiniC. Cacciamani

  21. 90th percentile of “climatological” pdf Box 2 aut2007 19 mm/24 23 mm/24 25 mm/24

  22. Consideration on “day-by-day” behaviour • COSMO-I7 seems to be more realistic than ECMWF in reproducing the intra-box variability. • However, COSMO-I7 presents both a large number of false alarms and high “spikes”. On the other hand, ECMWF presents a greater number of missed alarms, especially for high thresholds. • According to most standard verification measures, COSMO-I7 forecast would have poor quality, but it might be very valuable to the forecaster since it provides information on the distribution and variability of the rain field over the considered region.

  23. Neighbourhood method P. Kaufmann • Cylindrical neighbourhood with fading zone • Settings at MeteoSwiss: • COSMO-7 (6.6 km): rxy= 5, rf= 5, rt=3 • COSMO-2 (2.2 km): rxy=10, rf=10, rt=1 • Effective radius: • COSMO-7: ~50 km • COSMO-2: ~35 km t y x

  24. 12 July: high probabilities match well with precipitation pattern Probability of 12 h sum above 35 mm 06 – 18 UTC 18 – 06 UTC 24 h sum 06 – 06 UTC next day

  25. 15 August: high probabilities match well precipitation pattern Probability of 12 h sum above 35 mm 06 – 18 UTC 18 – 06 UTC 24 h sum 06 – 06 UTC next day

  26. 17 July: completely missed event Probability of 12 h sum above 35 mm 06 – 18 UTC 18 – 06 UTC 24 h sum 06 – 06 UTC next day

  27. Conclusionson verification of very high resoution models • Results of Upscaling and Fraction skill score are reasonable. • Scores increase with box size, but it is difficult to extract optimal size by looking at one single model. • Overall better results for very high-res models • This benefits of very high-res models is rather to see in situations where precipitation variability is large: convection , orography, summer… • …and at scales of 30 to 50 km • Products can be generated • Regional means (not new) • Probability to exceed threshold in neighborhood • Or possibly the whole pdf?

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