1. Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Fuzzy Verification toolbox: definitions and results Felix Ament MeteoSwiss, Switzerland

2. 2 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch Which rain forecast would you rather use? Mesoscale model (5 km) 21 Mar 2004 Sydney Global model (100 km) 21 Mar 2004 Sydney Motivation for new scores Observed 24h rain RMS=13.0 RMS=4.6

3. 3 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch Fine scale verification: Fuzzy Methods observation forecast xxx xx xxx x XX XX XX xX X X x x x Intensity Scale Evaluate box statistics (Choose a threshold to define event and non-event) define scales of interest consider statistics at these scales for verification General Recipe “… do not evaluate a point by point match!”  score depends on spatial scale and intensity

4. 4 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch A Fuzzy Verification Toolbox Ebert, E.E., 2007: Fuzzy verification of high resolution gridded forecasts: A review and proposed framework. Meteorol. Appls., submitted. Toolbox available at http://www.bom.gov.au/bmrc/wefor/staff/eee/fuzzy_verification.ziphttp://www.bom.gov.au/bmrc/wefor/staff/eee/fuzzy_verification.zip Fuzzy methodDecision model for useful forecast Upscaling (Zepeda-Arce et al. 2000; Weygandt et al. 2004)Resembles obs when averaged to coarser scales Anywhere in window (Damrath 2004), 50% coveragePredicts event over minimum fraction of region Fuzzy logic (Damrath 2004), Joint probability (Ebert 2002)More correct than incorrect Multi-event contingency table (Atger 2001)Predicts at least one event close to observed event Intensity-scale (Casati et al. 2004)Lower error than random arrangement of obs Fractions skill score (Roberts and Lean 2005)Similar frequency of forecast and observed events Practically perfect hindcast (Brooks et al. 1998)Resembles forecast based on perfect knowledge of observations Pragmatic (Theis et al. 2005)Can distinguish events and non-events CSRR (Germann and Zawadzki 2004)High probability of matching observed value Area-related RMSE (Rezacova et al. 2005)Similar intensity distribution as observed

5. 5 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch Applying fuzzy scores Fuzzy scores provide a wealth of information, but the results seems to be contrasting their interpretation is sometimes difficult contain too many numbers goodpoor

6. 6 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch Application versus testbed Know the scoresForecast error is unknown !? ?! Scores are unknownKnow the forecast error Application Testbed

7. 7 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch A Fuzzy Verification testbed 1.00 0.90 1.00 0.70 1.00 0.50 0.90 0.50 0.90 0.40 0.50 0.90 0.300.40 0.50 0.90 Perturbation Generator Analyzer Fuzzy Verification Toolbox Virtual truth (Radar data, model data, synthetic field) Realizations of virtual erroneous model forecasts Realizations of verification results Assessment of sensitivity (mean) [reliability (STD)] Two ingredients: 1.Reference fields: Hourly radar derived rain fields, August 2005 flood event, 19 time stamps (Frei et al., 2005) 2.Perturbations:  next slide

8. 8 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch Perturbations PerturbationType of forecast errorAlgorithm PERFECTNo error – perfect forecast!- XSHIFTHorizontal translation Horizontal translation (10 grid points) BROWNIANNo small scale skill Random exchange of neighboring points (Brownian motion) LS_NOISEWrong large scale forcing Multiplication with a disturbance factor generated by large scale 2d Gaussian kernels. SMOOTH High horizontal diffusion (or coarse scale model) Moving window arithmetic average DRIZZLE Overestimation of low intensity precipitation Moving Window filter setting each point below average point to the mean value

9. 9 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch Perfect forecast All scores should equal ! But, in fact, 5 out of 12 do not!

10. 10 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch Effect of „Leaking“ Scores observation forecast Problem: Some methods assume no skill at scales below window size! p obs =0.5 p forecast =0.5 Assuming random ordering within window yesno yes0.25 no0.25 An example: Joint probability method Forecast OBS Not perfect!

11. 11 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch Expected response to perturbations XSHIFTBROWNIANLS_NOISESMOOTHDRIZZLE Sensitivity: expected (=0.0); not expected (=1.0) Contrast := mean( ) – mean( ) Summary in terms of contrast: low high intensity coarse fine spatial scale

12. 12 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch Summary real Up- scaling Any- where in Window 50% cover- age Fuzzy Logig Joint Prob. Multi event cont. tab. Intensity Scale Fraction Skill Score Prag- matic Appr. Practic. Perf. Hindcast CSSR Area related RMSE Leaking Scores XSHIFT BROWNIANSMOOTH LS_NOISEDRIZZLE Contrast STD good Leaking scores show an overall poor performance “Intensity scale” and “Practically Perfect Hindcast” perform in general well, but … Many score have problem to detect large scale noise (LS_NOISE); “Upscaling” and “50% coverage” are beneficial in this respect Leaking scores show an overall poor performance “Intensity scale” and “Practically Perfect Hindcast” perform in general well, but … Many score have problem to detect large scale noise (LS_NOISE); “Upscaling” and “50% coverage” are beneficial in this respect

13. 13 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch Spatial detection versus filtering  x=25km   x=10km   x=5km  Horizontal translation (XSHIFT) with variable displacement  x “Intensity scale” method can detect spatial scale of perturbation All other methods like the “Fraction Skill score” just filter small scale errors

14. 14 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch Redundancy of scores Correlation (%) of resulting scores between all score for all thresholds, window sizes – averaged over all types of perturbation:  Groups of scores: UP, YN, MC, FB, PP FZ, JP FB, PP, (IS)

15. 15 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch August 2005 flood event Precipitation sum 18.8.-23.8.2005: Mean: 106.2mm Mean: 43.2mm Mean: 73.1mm Mean: 62.8mm (Hourly radar data calibrated using rain gauges (Frei et al., 2005))

16. 16 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch Fuzzy Verification of August 2005 flood Based on 3 hourly accumulations during August 2005 flood period (18.8.-23.8.2005) bad good Intensity threshold (mm/3h) Scale (7km gridpoints) COSMO-7 COSMO-2

17. 17 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch Fuzzy Verification of August 2005 flood COSMO-7 better COSMO-2 better neutral Difference of Fuzzy Scores Intensity threshold (mm/3h) Scale (7km gridpoints)

18. 18 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch D-PHASE RADAR Operational phase (June until November 2007) is running 33 atmospheric models take part … … and store there output in a common format in one data archive Demonstration of Probabilistic Hydrological and Atmospheric Simulation of flood Events in the Alpine region Standard verification (see Poster) Let’s apply the fuzzy toolbox Models: COSMO -2, -7, -DE, -EU Period: August 2007 Lead times: most recent forecast starting at forecast hour +03. Observations: Swiss Radar data aggregated on each model grid To be verified: 3h accumulation of precip.

19. 19 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch D-PHASE: August 2007 Intensity Scale score (preliminary), 3h accumulation COSMO-7 COSMO-2 COSMO-DE COSMO-EU

20. 20 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch Conclusions Fuzzy Verification score are a promising framework for verification of high resolution precipitation forecasts. The testbed is a useful tool to evaluate the wealth of scores (not necessarily fuzzy ones): Not all scores indicate a perfect forecast by perfect scores (Leaking scores). The “intensity scale” method is able to detect the specific scale of an spatial error. MeteoSwiss goes for: Upscaling, Intensity scale, Fraction skill score ( and Pracitically perfect hindcast) methods. First long term application for D-PHASE has just started.

21. 21 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch Summary ideal Up- scaling Any- where in Window 50% cover- age Fuzzy Logig Joint Prob. Multi event cont. tab. Intensity Scale Fraction Skill Score Prag- matic Appr. Practic. Perf. Hindcast CSSR Area related RMSE Leaking Scores XSHIFT BROWNIANSMOOTH LS_NOISEDRIZZLE Contrast STD good

22. 22 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch D-PHASE: August 2007

23. 23 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch D-PHASE: August 2007

24. 24 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch D-PHASE: August 2007 – cosmoch7

25. 25 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch D-PHASE: August 2007 – Cosmoch2

26. 26 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch D-PHASE: August 2007 - LME

27. 27 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch D-PHASE: August 2007 - LMK

28. 28 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch August 2005 flood event Precipitation sum 18.8.-23.8.2005: Mean: 73.1mm Mean: 62.8mm Mean: 106.2mm Mean: 43.2mm 7

29. 29 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch August 2005 flood event Fuzzy Verification (hourly accumulations): COSMO-7 COSMO-2

30. 30 Fuzzy Verification toolbox Felix.Ament@meteoswiss.ch August 2005 flood event Fuzzy Verification COSMO-2 – COSMO-7: Suprisingly, small differences However, COSMO2 seems to be slightly better slightly better at