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Massimo Milelli, Daniele Cane ARPA Piemonte

The INTERREG IIIB Project AMPHORE: A pplication des M éthodologies de P révisions H ydrometeorologiques O rientées aux R isques E nvironnementaux. Massimo Milelli, Daniele Cane ARPA Piemonte. massimo.milelli@arpa.piemonte.it daniele.cane@torino2006.it. The AMPHORE partners.

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Massimo Milelli, Daniele Cane ARPA Piemonte

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  1. The INTERREG IIIB Project AMPHORE: Application des Méthodologies de Prévisions Hydrometeorologiques Orientées aux Risques Environnementaux Massimo Milelli, Daniele Cane ARPA Piemonte massimo.milelli@arpa.piemonte.it daniele.cane@torino2006.it 2nd Workshop on Short Range EPS Bologna, 7-8 April 2005

  2. The AMPHORE partners • ARPA Piemonte (I) • ARPA - SIM EMILIA ROMAGNA (I) • CIMA (I) • ARPACAL (ARPA Calabria) (I) • ARPAL (ARPA Liguria) (I) • DPC (Dipartimento Protezione Civile) (I) • Université J. Fourier- Grenoble (F) • Météo-France (F) • Fundació Bosch i Gimpera (E) • Universitat de les Illes Balears (E) 2nd Workshop on Short Range EPS Bologna, 7-8 April 2005

  3. Goals of the project • Optimization of the alert systems for natural hazards due to intense precipitation events • Increasing of the synergy and of the cooperation between the scientific community and the local administrators • Creation of new formation opportunities for young people concerning natural hazards prevention themes • Improving of the precipitation forecast with the help of probabilistic techniques 2nd Workshop on Short Range EPS Bologna, 7-8 April 2005

  4. observation mean number of models mean forecast i thmodel forecast Multim. weights Multimodel Theory As suggested by the name, the Multimodel SuperEnsemble method requires several model outputs, which are weighted with an adequate set of weights calculated during the so-called training period. The simple ensemble method with bias-corrected or biased data respectively, is given by (1) or (2) The conventional superensemble forecast (Krishnamurti et. al., 2000) constructed with bias-corrected data is given by (3) 2nd Workshop on Short Range EPS Bologna, 7-8 April 2005

  5. The calculation of the parameters ai is given by the minimisation of the mean square deviation by derivation we obtain a set of N equations, where N is the number of models involved: We then solve these equations using Gauss-Jordan method. 2nd Workshop on Short Range EPS Bologna, 7-8 April 2005

  6. Model setup Considered variables: • Total Precipitation • T2m Time interval: 3h Models involved: • Lami (IT) (7 km) • Aladin (FR) (10 km) • Rams (IT) (20 km) • MM5 (ES) (20 km) • Bolam (IT) (20 km) • ECMWF (UK) (40 km) Independency of the models ! Different numerics, physics, initialization, domain, assimilation… 2nd Workshop on Short Range EPS Bologna, 7-8 April 2005

  7. Test events definition Poor-man Ensemble: Piedmont, 25 November 2002 (analysis 2002112500, forecast +36h) Gard, 8 September 2002 (analysis 2002090800, forecast +36h) Montserrat, 9 June 2000 (analysis 2000060900, forecast +36h) Reno, 7 November 2003 (analysis 2003110700, forecast +36h) + SuperEnsemble test cases SuperEnsemble Training: August to November 2004 (skipping the dates of the events): Cambrils, 6 September (Spain) Calabria, 12 November (Italy) Gard, 2 November 2004 (France) Piedmont, 15 September (Italy) 2nd Workshop on Short Range EPS Bologna, 7-8 April 2005

  8. Work performed • 3 models (LAMs) • 4 models (3 LAMs + ECMWF) • 6 models (LAMs) • 8 models (LAMs and ECMWF) • Different training length: 90/180/365 days (only precipitation) • Different training length: 60/120 days (only temperature) • Fixed training • Variable training 2nd Workshop on Short Range EPS Bologna, 7-8 April 2005

  9. A Toce M B Alta Dora Baltea Sesia Bassa Dora B. I C Pianura Settentrionale Orco Bassa Dora R. Sangone L D Pianura Meridionale Colline Piemontesi H Alta DoraRiparia Po G Scrivia Belbo - Orba E F Varaita Stura di Demonte Alto Tanaro The Method • We evaluate the model performances with respect to our regional high resolution network. • We applied Multimodel SuperEnsemble technique on • 2m temperature forecasts, compared with the measurements of 201 stations, divided in altitude classes (<700 m, 700-1500 m, >1500 m). • Precipitation over warning areas • 11 warning areas over Piedmont and Aosta • Valley. • For each of themmeanandmaximum • precipitationvalues are considered • Forecast times: 12-36 h. 2nd Workshop on Short Range EPS Bologna, 7-8 April 2005

  10. The verification of the precipitation results was made using several indices: Bias score (frequency bias) Equitable threat score (Gilbert skill score) where ROC (Relative operating characteristic), composed by False alarm ratio Probability of detection (hit rate) 2nd Workshop on Short Range EPS Bologna, 7-8 April 2005

  11. Sample results: precipitation 8 models, fixed training (180 days), fall 2004 forecast, average over the whole region Mean values 2nd Workshop on Short Range EPS Bologna, 7-8 April 2005

  12. Sample results: precipitation 8 models, fixed training (180 days), fall 2004 forecast, average over the whole region Maximum values 2nd Workshop on Short Range EPS Bologna, 7-8 April 2005

  13. Sample results: temperature 4 models, variable training (90 days) May 2004 forecast, 40 selected stations in the Olympic Area 2nd Workshop on Short Range EPS Bologna, 7-8 April 2005

  14. Conclusions • The Multimodel technique is now implemented and has been tested for the first time on limited area models in the Alpine area with high-resolution non-GTS weather station measurements • Multimodel SuperEnsemble permits a strong improvement of precipitation forecasts on warning areas, both in mean and maximum values • SuperEnsemble BIAS and ETS for precipitation events are better than those using a simple Ensemble. The same for Hit Rate and False Alarm Rate indices (not shown here, trust it !) • The Multimodel improves the temperature forecasts in high mountains locations, both in bias and RMSE and its performances are similar to those from Kalman filter (again not shown here) • The use of different runs of the same models improves SuperEnsemble performances 2nd Workshop on Short Range EPS Bologna, 7-8 April 2005

  15. References • Kalman R. E., Journal of Basic Engineering, 82 (Series D): 35-45, 1960 • Krishnamurti T.N. et al., Science, 285, 1548-1550, 1999 • Krishnamurti, T. N. et al.,J. Climate, 13, 4196-4216, 2000 • COSMO Newsletter, 3, 2003 • COSMO Newsletter, 5, 2005 Acknowledgements We wish to thank the Deutscher Wetterdienst and MeteoSwiss for providing the models outputs for the preliminary research work and the our partners for the realization of the project AMPHORE. 2nd Workshop on Short Range EPS Bologna, 7-8 April 2005

  16. You are welcome to the XX Olympic Winter Games (10-26 February) and to the IX Paralympic Winter Games (10-19 March) of Torino 2006 2nd Workshop on Short Range EPS Bologna, 7-8 April 2005

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