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Multi-model Short-Range Ensemble Forecasting at Spanish Met Agency (AEMET)

Multi-model Short-Range Ensemble Forecasting at Spanish Met Agency (AEMET). J. A. García-Moya, A. Callado, P. Escriba, C. Santos, D. Santos, J. Simarro Spanish Met Service AEMET Training Workshop on NOWCASTING TECHNIQUES Buenos Aires, August 2013. Outline. Introduction

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Multi-model Short-Range Ensemble Forecasting at Spanish Met Agency (AEMET)

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  1. Multi-model Short-Range Ensemble Forecasting at Spanish Met Agency (AEMET) J. A. García-Moya, A. Callado, P. Escriba, C. Santos, D. Santos, J. Simarro Spanish Met Service AEMET Training Workshop on NOWCASTING TECHNIQUES Buenos Aires, August 2013

  2. Outline • Introduction • EPS for short-range forecast • SREPS system at AEMET • Verification exercise • Against analysis • Against synoptic observations • Against climate network observations • Probabilistic scores • Synoptic variables • 10 m surface wind speed • 6h accumulated precipitation • 24h accumulated precipitation • Time-Lagged Super-ensemble • Comparison with ECMWF EPS • Multi-model predictability • Conclusions T-Note Workshop

  3. Meteorological Framework • Main Weather Forecast issues are related with Short-Range forecast of extreme events. • Convective precipitation is the most dangerous weather event in the Mediterranean. T-Note Workshop

  4. Geographical Framework • Western Mediterranean is a close sea rounded by high mountains. • In autumn sea is warmer than air. • Several cases of more than 200 mm/few hours occurs every year. • Some fast cyclogenesis like “tropical cyclones” also appears from time to time (“medicanes”). T-Note Workshop

  5. Geographical Framework T-Note Workshop 5

  6. 520 mm/24 h T-Note Workshop 6

  7. Met Radar: Sep, the 12 1996 at 0450 UTC T-Note Workshop 7

  8. Ensemble for Short Range • Surface parameters are the most important ones for weather forecast. • Forecast of extreme events (convective precip, gales,…) is probabilistic. • 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. T-Note Workshop

  9. Errors of short-range forecast • Due to model formulation. • Due to simplifications in parameterisation schemes. • Due to uncertainty in the initial state. • Special for LAMs, due to errors in lateral boundary conditions. • Due to uncertainties in soil fields (soil temperature and soil water content, …). T-Note Workshop

  10. SREPS I • Multi-model approach (Hou & Kalnay 2001). • Stochastic physics (Buizza et al. 1999). • Multi-boundaries: • From few global deterministic models. • From global model EPS (ECMWF). • SLAF technique (Ebisuzaki & Kalnay 1991). T-Note Workshop

  11. SREPS II • Different assimilation techniques: • Optimal Interpolation. • Variational (3D or 4D). • Perturbed analysis: • Singular vectors (ECMWF, Palmer et al. 1997). • Breeding (NCEP, Toth & Kalnay 1997). • Scaled Lagged Average Forecasting (SLAF, Ebisuzaki & Kalnay 1991). • EnKF, ETKF, LETKF, PO T-Note Workshop

  12. Multi-model • Hirlam (http://hirlam.org). • HRM from DWD (German Weather Service). • MM5 (http://box.mmm.ucar.edu/mm5/). • UM from UKMO (UK Weather Service). • LM (COSMO Model) from COSMO consortium (http://www.cosmo-model.org). • WRF (NOAA – NCEP) – work in progress T-Note Workshop

  13. Multi-Boundaries From different global deterministic models: • ECMWF • UM from UKMO (UK Weather Service) • GFS from NCEP • GME from DWD (German Weather Service) • CMC from SMC (Canadian Weather Service) T-Note Workshop

  14. SREPS at AEMET • Mummub: Multi-model Multi-boundaries • 72 hours forecast two times a day (00 & 12 UTC). • Characteristics: • 5 models. • 5 boundary conditions. • 2 latest ensembles (HH & HH-12). • 25 member ensemble every 12 hours • Time-lagged Super-Ensemble of 50 members every 12 hours. T-Note Workshop

  15. T-Note Workshop

  16. 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 T-Note Workshop

  17. 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 (work in progress) • Verification: Deterministic & Probabilistic • Against ECMWF analysis • Against observations T-Note Workshop

  18. Whole Area Zoom over Spain Monit: all models X bcs T-Note Workshop

  19. Monit: Spread – Ensemble mean maps Spread at key mesoscale areas T-Note Workshop

  20. >=1mm >=5mm >=10mm >=20mm Probability maps Case Study 06/10/2006 at 00 UTC • More than 15 mm/6 hours T-Note Workshop

  21. Verification • Verification exercise, April-June 2006: • Calibration: with synoptic variables Z500, T500, Pmsl • Response to binary events: reliability and resolution of surface variables 10m surface wind, 6h and 24h accumulated precipitation T-Note Workshop

  22. Interpolation T-Note Workshop

  23. Obs verification - Probabilistic scores • Ensemble calibration: • Synoptic variables: • Z500, T500, Pmsl • Scores: • Rank histograms • Spread-skill • Response to binary events: • Surface variables: • 10m surface wind (10,15,20m/s thresholds) • 6h accumulated precipitation (1,5,10,20mm thresholds) • 24h accumulated precipitation (1,5,10,20mm thresholds) • Scores: • Reliability, sharpness (H+24, H+48) • ROC, Relative Value (H+24, H+48) • BSS, ROCA with forecast length T-Note Workshop

  24. Z500 10mWind >=10m/s H+24 H+24 10mWind H+24 10mWind >=10m/s H+24 >=10m/s Z500 Summary of Probabilistic Verification T-Note Workshop

  25. Synoptic parameters • Using ECMWF Analysis as reference: • MSLP • Over all FC lengths H+00 .. H+72: • Spread-skill • H+72: • Rank histograms • Using Synoptic observations as reference: • MSLP • Over all FC lengths H+00 .. H+72: • Spread-skill • H+72: • Rank histograms T-Note Workshop

  26. MSLP-ECMWF H+72 T-Note Workshop

  27. H+72 MSLP - Obs T-Note Workshop

  28. Binary events • Binary events X = {0,1} at every point • Accumulated precipitation in 24 hours >= 5mm • Useful to decompose the forecast in thresholds • Performance computed using contingency tables (CT’s) T-Note Workshop

  29. Contingency tables • It is the best way to characterize a binary event fc(X)={1,0} ob(X)={1,0} T-Note Workshop

  30. Contingency tables: scores • Several scores can computed from CT’s T-Note Workshop

  31. Example: Every pdf threshold has its own CT T-Note Workshop

  32. Reliability • Observation frequency conditioned to forecast probability • Reliability diagrams • ( i/N , Δai/(Δai+Δbi) ) i = 0…N • Near diagonal ~ reliable • Sharpness histograms • ( i/N , Δai+Δbi ) i = 0…N • U shape ~ sharp (discriminating binary event) T-Note Workshop

  33. Precipitation • Using ECMWF Deterministic Model as reference: • 6 hours accumulation – 24 hours forecast length • 24 hours accumulation – 54 hours forecast length • Thresholds 1, 5, 10 y 20 mm • Using Synoptic observations as reference: • 6 hours accumulation – 24 hours forecast length • 24 hours accumulation – 54 hours forecast length • Thresholds 1, 5, 10 y 20 mm T-Note Workshop

  34. Reliab. - 6 h Acc. Precip H+24 (1,5,10,20) mm Reliab. - 24 h Acc. Precip H+54 (1,5,10,20) mm ECMWF T-Note Workshop

  35. Reliab. - 6 h Acc. Precip H+24 (1,5,10,20) mm Reliab. - 24 h Acc. Precip H+54 (1,5,10,20) mm Observations T-Note Workshop

  36. Resolution • Based on Signal Detection Theory • ROC (Relative Operating Characteristics) • ( FARi , HIRi ) i = 0…N • ROCArea ~ Resolution or binary event discrimination • Forecast conditioned by observation T-Note Workshop

  37. >=0.8 >=0.9 H+30 H+54 ROC curves – 24 h Acc Precip (1, 5, 10 & 20 mm) ECMWF Observations H+30 H+54 T-Note Workshop

  38. >=0.9 10 m wind H+24 (10,15,20) m/s Reliability ROC 1.0 0.5 H+24 ECMWF - Analysis T-Note Workshop

  39. 1.0 0.5 10 m wind H+24 (10,15,20) m/s Reliability ROC H+24 Observations T-Note Workshop

  40. Joint T-Note Workshop

  41. >=1mm >=5mm >=10mm >=20mm >=1mm >=5mm >=10mm >=20mm Reliability & Sharpness • Good reliability according to • thresholds (base rate) • forecast length H+30 Joint H+54 No Under-sampling T-Note Workshop

  42. 0.5 0 1 1 Resolution • Good resolution • ROC Areas • BSSs • Good RV curves T-Note Workshop

  43. Time-Lagged Super-ensemble • How much predictability can be added by a time-lagged super-ensemble? • 40 members super-ensemble (SE-SREPS) with the last two runs of SREPS ( HH & HH-12). • Verifications against observations • Cheap in terms of computer resources • Just a different post-process T-Note Workshop

  44. Spread-skill Green - SE-SREPS Blue - SREPS MSLP Z500 T-Note Workshop

  45. Rank histogram Green - SE-SREPS Blue - SREPS MSLP T-Note Workshop

  46. Reliability diagrams Green - SE-SREPS Blue - SREPS 6 h. Acc. Precip. H+48 (1, 5, 10 & 20 mm) T-Note Workshop

  47. Comparison with ECMWF EPS • Period: April to June 2006 • European Synop obs: H+72. • Mslp / v 10m / Precipitation • European climate precipitation network: H+54 (longest SREPS period matching observations). • 24 hours accumulated precipitation (from early morning to early morning). T-Note Workshop

  48. 1 mm 5 mm 10 mm 20 mm Blue - SREPS Green - ECMWF 21 Red - ECMWF 51 Climate Obs – 24 h. Precip (1, 5, 10 & 20 mm) T-Note Workshop

  49. 1 mm 5 mm Blue - SREPS Green - ECMWF 21 Red - ECMWF 51 10 mm 20 mm Climate Obs – 24 h. Precip (1, 5, 10 & 20 mm) T-Note Workshop

  50. Climate Obs – 24 h. Precip (1, 5, 10 & 20 mm)Brier Skill Score (BSS) Decomposition – 106 realizations BSS (+) BSS Rel (-) BSS Res (-) T-Note Workshop

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