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Short-Range Ensemble Prediction System at INM José A. García-Moya & Carlos Santos SMNT – INM COSMO Meeting Zurich, September 2005.

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Presentation on theme: "Short-Range Ensemble Prediction System at INM José A. García-Moya & Carlos Santos SMNT – INM COSMO Meeting Zurich, September 2005."— Presentation transcript:

1 Short-Range Ensemble Prediction System at INM José A. García-Moya & Carlos Santos SMNT – INM COSMO Meeting Zurich, September 2005

2 September 2005COSMO Meeting2 Introduction Surface parameters are the most important ones for weather forecast. Forecast of extreme events (convective precip, gales,…) is probabilistic even for the short-range. Short Range Ensemble prediction can help to forecast these events. Forecast risk (Palmer, ECMWF Seminar 2002) is the goal for both Medium- and, also, “Short-Range Prediction” (quotation is mine).

3 September 2005COSMO Meeting3 Meteorological Framework Main Weather Forecast issues are related with Short-Range extreme events. Convective precipitation is the most dangerous weather event in Spain. Western Mediterranean is a quasi-closed sea rounded by high mountains. In autumn sea is warmer than air, then low troposphere is conditionally unstable most of the time. Several cases of more than 200 mm/few hours every year. Some fast cyclogenesis like “tropical cyclones” happen.

4 September 2005COSMO Meeting4 Ensemble for Short-Range Extreme weather events have a low predictability even in the Short Range (less than 72 hours). Convection is highly non-linear and it shows a chaotic behaviour. Then a probabilistic approach may help to improve the prediction of such phenomena.

5 September 2005COSMO Meeting5

6 September 2005COSMO Meeting6 Errors in LAMs Due to the model formulation Multimodel thecniques Due to uncertainties in the initial state Singular vectors, breeding Due to uncertainties at boundaries From different deterministic global models From a global ensemble Due to the parameterization schemes Mutiphysics Stochastic physic techniques

7 September 2005COSMO Meeting7 Multi-model Hirlam. HRM from DWD. MM5 UM Unified Model from UKMO.

8 September 2005COSMO Meeting8 Multi-Boundaries From different global deterministic models: ECMWF UM UKMO AVN NCEP GME DWD.

9 September 2005COSMO Meeting9 Ensemble 72 hours forecast four times a day (00, 06, 12 y 18 UTC). Characteristics: 4 models. 4 boundary conditions. 4 last ensembles (HH, HH-6, HH-12, HH-18). 16 member ensemble every 6 hours Time-lagged Super-Ensemble of 64 members every 6 hours.

10 September 2005COSMO Meeting10 Actual Ensemble 72 hours forecast once a day (00 UTC). Characteristics: 4 models. 4 boundary conditions. 13 (of 16 expected) member ensemble every 24 hours

11 September 2005COSMO Meeting11 Actual Ensemble II BCs / Model AVNECMWFGMEUM HirlamXXXX HrmXXXX MM5XXXX UMOOOX

12 September 2005COSMO Meeting12 Road Map 2003- 2004 Research to find best ensemble for the Short Range Jun 04 – Jun 05 Building Multimodel System Jun 05- Dec 05 Mummub n/16 members Daily run non- operational Mar 06Mummub 16/16 members Full operations Jun 06Mummub+4lag 64 members First try

13 September 2005COSMO Meeting13 Post-processing Integration areas 0.25 latxlon, 40 levels Interpolation to a common area ~ North Atlantic + Europe Grid 380x184, 0.25º Software Enhanced PC + Linux ECMWF Metview + Local developments Outputs Deterministic Ensemble probabilistic

14 September 2005COSMO Meeting14 Post-processing III

15 September 2005COSMO Meeting15 Post-processing II

16 September 2005COSMO Meeting16 Monitoring in real time Intranet web server Deterministic outputs Models X BCs tables Maps for each couple (model,BCs) Ensemble probabilistic outputs Probability maps: 6h accumulated precipitation, 10m wind speed, 24h 2m temperature trend Ensemble mean & Spread maps EPSgrams (not fully-operational) Verification

17 September 2005COSMO Meeting17 Monit 1: home

18 September 2005COSMO Meeting18 Monit 2: all models X bcs

19 September 2005COSMO Meeting19 Monit 3: one member Z500

20 September 2005COSMO Meeting20 Monit 4: one member 6h Acc Precip

21 September 2005COSMO Meeting21 Monit 5: All Prob 24h 2m T trend

22 September 2005COSMO Meeting22 Monit 6: Prob maps 24h 2m T trend

23 September 2005COSMO Meeting23 Monit 7: Spread - Emean maps

24 September 2005COSMO Meeting24 Monit 8: EPSgrams EPSgrams Not fully operational

25 September 2005COSMO Meeting25 Case study: Aug, 20, 2005 Prob. Map & RADAR 12-18Z

26 September 2005COSMO Meeting26 Case study: Aug, 20, 2005 Prob. Map & RADAR 00-24Z

27 September 2005COSMO Meeting27 Validation ECMWF operational analysis as reference. Verification software ~ ECMWF Metview + Local developments Deterministic scores Bias & Rms for each member Probabilistic ensemble scores Talagrand ROC Spread vs Ensemble mean error 15 days of comparison (Aug, 17 to 31, 2005).

28 September 2005COSMO Meeting28

29 September 2005COSMO Meeting29 Talagrand Diagrams Ensemble members ranked from smallest to greatest value. Percent of cases which verifying analysis falls in an interval. First interval, below smallest member. Last one, above greatest member. Z500, T500, Msl Pressure H+24, H+48

30 September 2005COSMO Meeting30

31 September 2005COSMO Meeting31 Spread vs Ensemble Mean Error Z500 H+00 to H+72 T500 H+00 to H+72 Msl Pressure H+00 to H+72

32 September 2005COSMO Meeting32

33 September 2005COSMO Meeting33 ROC Curves 10m Wind Speed Thresholds: 10m/s, 15m/s H+24, H+48 24h Accumulated Precipitation Thresholds: 1mm, 5mm, 10mm, 20mm H+24, H+48

34 September 2005COSMO Meeting34

35 September 2005COSMO Meeting35

36 September 2005COSMO Meeting36 Advantages: Better representation of model errors (SAMEX and DEMETER). Consistent set of perturbations of initial state and boundaries. Better results (SAMEX, DEMETER, Arribas et al., MWR 2005). Disadvantages: Difficult to implement operationally (four different models should be maintained operationally). Expensive in terms of human resources. No control experiment in the ensemble. Conclusions for Multimodel

37 September 2005COSMO Meeting37 Future 16 members full-operational Bias removal Calibration: Bayessian Model Averaging Verification against observations Time-lagged 64 members 4runs/day More Post processing software (targeting clustering)

38 September 2005COSMO Meeting38 Team García-Moya, J.A. Head, Pre-processing BCs, Hirlam Callado, A. UM Santos, C. Post-processing, Verification, Hirlam Santos, D. MM5 Simarro, J. HRM, Pre-processing BCs

39 September 2005COSMO Meeting39 Thanks to… MetOffice Ken Mylne, Jorge Bornemann DWD Detlev Majewski, Michael Gertz ECMWF Metview Team

40 September 2005COSMO Meeting40 Questions ?... j.garciamoya@inm.es csantos@inm.es


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