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2nd Workshop on Short Range EPS

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Title: 2nd Workshop on Short Range EPS


1
The INTERREG IIIB Project AMPHORE Application
des Méthodologies de Prévisions
Hydrometeorologiques Orientées aux Risques
Environnementaux
Massimo Milelli, Daniele Cane ARPA Piemonte
massimo.milelli_at_arpa.piemonte.it daniele.cane_at_tori
no2006.it
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)

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

4
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)
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.
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
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)

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

9
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 (lt700 m, 700-1500 m, gt1500 m).
  • Precipitation over warning areas
  • 11 warning areas over Piedmont and Aosta
  • Valley.
  • For each of them mean and maximum
  • precipitation values are considered
  • Forecast times 12-36 h.

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)
11
Sample results precipitation
8 models, fixed training (180 days), fall 2004
forecast, average over the whole region
Mean values
12
Sample results precipitation
8 models, fixed training (180 days), fall 2004
forecast, average over the whole region
Maximum values
13
Sample results temperature
4 models, variable training (90 days)
May 2004 forecast, 40 selected stations in the
Olympic Area
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

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.
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
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