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Short-Range Ensemble Prediction System at INM José A. García-Moya SMNT – INM 27th EWGLAM & 12th SRNWP Meetings Ljubljana, October 2005.

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Presentation on theme: "Short-Range Ensemble Prediction System at INM José A. García-Moya SMNT – INM 27th EWGLAM & 12th SRNWP Meetings Ljubljana, October 2005."— Presentation transcript:

1 Short-Range Ensemble Prediction System at INM José A. García-Moya SMNT – INM 27th EWGLAM & 12th SRNWP Meetings Ljubljana, October 2005

2 October 2005 27th EWGLAM & 12th SRNWP Meetings2 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 October 2005 27th EWGLAM & 12th SRNWP Meetings3

4 October 2005 27th EWGLAM & 12th SRNWP Meetings4 Errors in LAMs Due to the model formulation Multimodel techniques 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 Multiphysic Stochastic physic techniques

5 October 2005 27th EWGLAM & 12th SRNWP Meetings5 Multi-model Hirlam. HRM from DWD. MM5 UM Unified Model from UKMO.

6 October 2005 27th EWGLAM & 12th SRNWP Meetings6 Multi-Boundaries From different global deterministic models: ECMWF UM UKMO AVN NCEP GME DWD.

7 October 2005 27th EWGLAM & 12th SRNWP Meetings7 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.

8 October 2005 27th EWGLAM & 12th SRNWP Meetings8 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

9 October 2005 27th EWGLAM & 12th SRNWP Meetings9 Actual Ensemble II BCs / Model AVNECMWFGMEUM HirlamXXXX HrmXXXX MM5XXXX UMOOOX

10 October 2005 27th EWGLAM & 12th SRNWP Meetings10 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

11 October 2005 27th EWGLAM & 12th SRNWP Meetings11 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

12 October 2005 27th EWGLAM & 12th SRNWP Meetings12 Post-processing II

13 October 2005 27th EWGLAM & 12th SRNWP Meetings13 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

14 October 2005 27th EWGLAM & 12th SRNWP Meetings14 Monit 1: home

15 October 2005 27th EWGLAM & 12th SRNWP Meetings15 Monit 2: all models X bcs

16 October 2005 27th EWGLAM & 12th SRNWP Meetings16 Monit 5: All Prob 24h 2m T trend

17 October 2005 27th EWGLAM & 12th SRNWP Meetings17 Monit 7: Spread - Emean maps

18 October 2005 27th EWGLAM & 12th SRNWP Meetings18 Validation ECMWF operational analysis as reference. Verification software ~ ECMWF Metview + Local developments Deterministic scores Bias & Rms for each member Probabilistic ensemble scores Rank histograms ROC Spread skill 15 days of comparison (Aug, 17 to 31, 2005).

19 October 2005 27th EWGLAM & 12th SRNWP Meetings19

20 October 2005 27th EWGLAM & 12th SRNWP Meetings20 Rank histograms 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

21 October 2005 27th EWGLAM & 12th SRNWP Meetings21

22 October 2005 27th EWGLAM & 12th SRNWP Meetings22 Spread Skill Spread vs Ensemble Mean Error Z500 H+00 to H+72 T500 H+00 to H+72 Msl Pressure H+00 to H+72

23 October 2005 27th EWGLAM & 12th SRNWP Meetings23

24 October 2005 27th EWGLAM & 12th SRNWP Meetings24 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

25 October 2005 27th EWGLAM & 12th SRNWP Meetings25

26 October 2005 27th EWGLAM & 12th SRNWP Meetings26

27 October 2005 27th EWGLAM & 12th SRNWP Meetings27 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

28 October 2005 27th EWGLAM & 12th SRNWP Meetings28 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)

29 October 2005 27th EWGLAM & 12th SRNWP Meetings29 Thanks to… MetOffice Ken Mylne, Jorge Bornemann DWD Detlev Majewski, Michael Gertz ECMWF Metview Team

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