1. Multi-model Short-Range Ensemble Forecasting at Spanish Met Institute
J. A. García-Moya, A. Callado, P. Escriba, C. Santos, D. Santos, J. Simarro
NWP – Spanish Met Service AEMET
10th COSMO General Meeting
Cracovia – September 2008

2. SREPS at AEMET Multi-model Multi-boundaries.
72 hours forecast two times a day.
Characteristics:
5 limited area models.
4 boundary and initial conditions
Resolution: 0.25º, 40 levels.
20 member ensemble every 12 hours.
Time-lagged Super-Ensemble of 40 members every 12 hours.
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3. Multi-model Hirlam (http://hirlam.org).
HRM from DWD (German Weather Service).
MM5 (
UM from UKMO (UK Weather Service).
COSMO from COSMO consortium
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4. Multi-Boundaries Different global deterministic models: ECMWF
UM from UKMO (UK Weather Service)
GFS from NCEP
GME from DWD (German Weather Service)
CMC from Canadian Weather Service
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5. COSMO area is the same as HRM area
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6. Tasks Implementation of LAM models Pre-process Post-process
HIRLAM, HRM, MM5, UM, COSMO
each with 5 global models
Pre-process
get global data and prepare for each LAM
Post-process
probabilistic products, BMA
intranet web server
Verification
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7. Tasks COSMO implementation Vertical interpolation tool Crayx1 machine
5 global models
Vertical interpolation tool
interfacing LAM and global models
example: GFS isobaric to hybrid levels
int2lm tool can be used
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8. Meteorological Framework
Main Weather Forecast issues are related with Short-Range forecast of extreme events.
Convective precipitation is the most dangerous weather event in Spain.
Several cases of more than 200 mm/few hours occurs every year.
Verification exercises are focused in precipitation using dense gauge network
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9. 520 mm/24 h
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10. Monitoring in real time
Intranet web server
Deterministic outputs
Maps for each member
Probability maps
6h accumulated precipitation
10 m wind speed
24 h 2 m temperature trend
Ensemble mean and spread maps
EPSgrams
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11. Monitoring
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12. Deterministic product
Whole Area
Zoom over Spain
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13. How will be tomorrow compared with today?
Probabilistic product
How will be tomorrow compared with today?
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14. Spread at key mesoscale areas
Ensemble mean and spread
Spread at key mesoscale areas
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15. Bias&RMSE Z500 Z500 Bias & RMSE Implementation error for this member
Despite the error the Ensemble Mean has very good verification!
Good performance of COSMO members
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16. Probabilistic Verification
H+24
10mWind
>=10m/s
H+24
Z500
10mWind
>=10m/s
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17. Verification exercise
24h accumulated precipitation
forecast 06UTC-06UTC against observed 07UTC-07UTC
Checked in HH+030 and HH+054
90 days (Apr1 to Jun ).
Different rain gauge networks as references:
AEMET precipitation network (pnw)
MeteoFrance, DWD, UKMO
Joint pnw (many countries)
Verification method
Interpolation to observation points
Verification software
ECMWF Metview + Local developments
Performance scores
ECMWF recommendations
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18. Joint
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19. Reliability & Sharpness
>=1mm
>=20mm
>=10mm
>=5mm
H+30
Joint
H+54
Good reliability according to
thresholds (base rate)
forecast length
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20. Resolution Good resolution Good RV curves ROC Areas BSSs 1 0.5 1
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21. Conclusions
A Multi-model-Multi-boundaries Short Range Ensemble Prediction System (MMSREPS) has been develop at the INM-Spain.
We show here a 3 months verification, 24 hour of accumulated precipitation, dense net of gauge observations.
Verification shows quite good results
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22. References
(2001) Hou D., Kalnay E., & Droegemeier, K.K.: Objective Verification of the SAMEX’98 Ensemble Forecasts. M.W.R., 129,
(1999) Buizza R., Miller, M., & Palmer, T.N.: Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Q. J. R. Meteorol. Soc., 125,
(1997) Toth Z., & Kalnay E.: Ensemble Forecasting at NCEP and the breeding method. M.W.R., 125,
(1997) Palmer T.N., Barkmeier J., Buizza R., & Petroliagis T.: The ECMWF Ensmble Prediction System. Meteorol. Appl., 4,
(2004) Palmer, T.N., et al.: Development of a European multi-model ensemble system for seasonal to inter-annual prediction (DEMETER). B.A.M.S., 85,
(2005) Arribas A., Robertson K.B., & Mylne, K.R.: Test of Poor Man’s Ensemble Prediction System. M.W.R., 133,
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23. Acknowledgements Eugenia Kalnay (Univ. Of Maryland)
Ken Mylne, Jorge Bornemann (MetOffice)
Detlev Majewski, Michael Gertz (DWD)
Metview Team, Martin Leutbecher (ECMWF)
Chiara Marsigli, Ulrich Schättler (COSMO)
Olivier Talagrand (LMD)
We also like to thank many Met. Services for making their climate network precipitation observations available to us for verification (some of them not yet included): Arpa-Sim-Bolonia (Italy), DWD (Germany), EARS (Slovenia), HNMS (Greece), HMS (Hungary), KNMI (Netherlands), Lombardia (Italy), Météo-France (France), NIMH (Bulgaria), NMAP (Romania), SHMU (Slovakia), UKMO (UK), ZAMG (Austria), Cerdeña (Italy), Marche (Italy), Trento (Italy), Venezia (Italy), FMI (Finland), IMP (Portugal)
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24. Thank you for your attention
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