Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Priority project « Advanced interpretation and verification of very high resolution models »

2 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch Topics 1.Advanced postprocessing of weather parameters 2.Verification of very high resolution models, incl. fuzzy verification methods 3.Hydrological applications

3 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch 3. Hydrological applications Hydrology (precipitation adaptation): Presentation by A. Mazur Snow parametrisation: Presentation by E. Machulskaya

4 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch 1. Recognition of weather elements Done last year: recognition of thunderstorms with the boosting algorithm: Choice of predictors Perler, Kohli, Walser

5 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch 1.Kalman filtering of COSMO LEPS V. Stauch, poster outside

6 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch 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 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]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 Observed 24h rain RMS=13.0 RMS=4.6 B. Ebert

8 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch Motivation: precipitation pattern 7km 2km

9 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch Fuzzy Verification F. Ament Verification on coarser scales than model scale: “Do not require a point wise match!“ XX XX XX xX X X x MethodRaw DataFuzzyficationScoreExample result Upscaling Average Equitable threat score Fraction Skill Score (Roberts and Lean, 2005) Fractional coverage Skill score with reference to worst forecast XX XX XX xX X X x

10 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch Expected behaviour of scores From Nigel Roberts (2005)

11 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch Application of scores to a perfect forecast All scores should equal ! But, in fact, 5 out of 12 do not!

12 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch Requested theoretical properties of scores J Avoid « leaking » scores J Use illustrative and understandable scores J Score should give a real information of the forecast quality on the different scales J Monotonic behavior concerning scale (best values for large scales) frequency of occurrence (best values for high frequencies of occurrence) J Represent some significant characteristics of the PDF (obs and forecast)

13 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch Requested practical properties of scores J Agreement between subjective and objective judgment J Possible help in decision making J Correspond to the needs of the users J Should be able to provide a comparison between 2km and 7 km models (also global models) J Should not use a matching between prediction and observation because it would not allow the generation of univocal products

14 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch 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 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch Spatial scale (km) Fuzzy Verification: COSMO-DE – COSMO-EU good bad Threshold (mm/3h) - = Fraction skill score Upscaling = - COSMO-EU (7km)COSMO-DE (2.8km) Difference COSMO-EU better COSMO-DE better JJA 2007, Verification against Swiss Radar Composite, 3 hourly accumulations

16 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch Spatial scale (km) Fuzzy Verification COSMO-2 – COSMO good bad Threshold (mm/3h) - = Fraction skill score Upscaling = - COSMO-7 (7km)COSMO-2 (2.2km) Difference COSMO-7 better COSMO-2 better JJA 2007, Verification against Swiss Radar Composite, 3 hourly accumulations

17 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch Monthly dependency cut-off 03h, accumulation 03h COSMO-DE - COSMO-EU June COSMO-2 - COSMO-7 July August

18 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch Quarterly summaries of „Fuzzy“-scores FSS Autumn 2007 U. Damrath

19 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch Monthly summaries of „Fuzzy“-scores FSS July 2007

20 Analysis of precipitation in boxes Average number of stations in each area ( SON 2007) X 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. Tesini C. Cacciamani

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

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 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch Neighbourhood method P. Kaufmann Cylindrical neighbourhood with fading zone Settings at MeteoSwiss: COSMO-7 (6.6 km): r xy = 5, r f = 5, r t =3 COSMO-2 (2.2 km): r xy =10, r f =10, r t =1 Effective radius: COSMO-7: ~50 km COSMO-2: ~35 km x y t

24 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch 12 July: high probabilities match well with precipitation pattern 24 h sum 06 – 06 UTC next day Probability of 12 h sum above 35 mm 06 – 18 UTC 18 – 06 UTC

25 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch 15 August: high probabilities match well precipitation pattern 24 h sum 06 – 06 UTC next day Probability of 12 h sum above 35 mm 06 – 18 UTC 18 – 06 UTC

26 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch 17 July: completely missed event 24 h sum 06 – 06 UTC next day Probability of 12 h sum above 35 mm 06 – 18 UTC 18 – 06 UTC

27 COSMO General meeting ¦ Cracow, September 2008 Pierre.Eckert[at]meteoswiss.ch Conclusions on 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?