Massimo Milelli, Daniele Cane

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Use of Multi-Model Super-Ensemble Technique for complex orography weather forecast. Massimo Milelli, Daniele Cane. VII COSMO General Meeting Zuerich, September 20-23 2005. Overview. Multimodel Ensemble and SuperEnsemble Theory Upgrade of previous results: Precipitation forecast

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Use of Multi-Model Super-Ensemble

Technique for complex

orography weather forecast

Massimo Milelli, Daniele Cane

VII COSMO General Meeting

Zuerich, September 20-23 2005

Overview

• Multimodel Ensemble and SuperEnsemble Theory
• Precipitation forecast
• Temperature forecast
• New Multimodel results:
• RH forecast
• Wind intensity forecast
• Conclusions

Multimodel Theory

number of models

i thmodel forecast

mean forecast

Multim. weights

observation mean

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)

We then solve these equations using Gauss-Jordan method.

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:

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

• 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 and 36-60 h.

Precipitation

We use the following operational runs of the 0.0625° resolution version of LM (00 and 12 UTC runs)

Local Area Model Italy

(UGM, ARPA-SIM, ARPA Piemonte) (nud00, nud12)

Lokal Modell

(Deutscher Wetterdienst) (lkd00, lkd12)

aLpine Model

(MeteoSwiss) (alm00, alm12)

Training: 180 days (dynamic)

Forecast: from July 2004 to March 2005

Stations: 102

Method: mean and maximum values over warning areas

The verification of the results has been made using the usual indices:

Bias score (frequency bias)

Equitable threat score (Gilbert skill score)

where

12-36 h

Mean

Maximum

36-60 h

Mean

Maximum

Temperature

We use the following operational runs of the 0.0625° resolution version of LM (00 and 12 UTC runs)

Local Area Model Italy

(UGM, ARPA-SIM, ARPA Piemonte) (nud00, nud12)

Lokal Modell

(Deutscher Wetterdienst) (lkd00, lkd12)

aLpine Model

(MeteoSwiss) (alm00, alm12)

Training: 90 days (dynamic)

Forecast: March 2005

Stations: 53 (h<700m), 34 (700m<h<1500m), 15 (h>1500m)

Method: bilinear interpolation horizontally, linear vertically (using Z)

700 m < h < 1500 m

h < 700 m

h > 1500 m

h < 700 m

700 m < h < 1500 m

h > 1500 m

Relative Humidity

We use the following operational runs of the 0.0625° resolution version of LM (00 and 12 UTC runs)

Local Area Model Italy

(UGM, ARPA-SIM, ARPA Piemonte) (nud00, nud12)

Lokal Modell

(Deutscher Wetterdienst) (lkd00, lkd12)

aLpine Model

(MeteoSwiss) (alm00, alm12)

Training: 90 days (dynamic)

Forecast: March 2005

Stations: 53 (h<700m), 34 (700m<h<1500m), 15 (h>1500m)

Method: bilinear interpolation horizontally, linear vertically (using Z)

h < 700 m

700 m < h < 1500 m

h > 1500 m

Wind Intensity

We use the following operational runs of the 0.0625° resolution version of LM (00 and 12 UTC runs)

Local Area Model Italy

(UGM, ARPA-SIM, ARPA Piemonte) (nud00, nud12)

plus

ECMWF (ecm00, ecm12)

Training: 90 days (dynamic)

Forecast: March 2005

Stations: 53 (h<700m), 34 (700m<h<1500m), 15 (h>1500m)

Method: nearest gridpoint

h < 700 m

700 m < h < 1500 m

h > 1500 m

Conclusions

Proposals for the continuation of the work

• Multimodel Ensemble and SuperEnsemble permit a strong improvement of all the considered variables with respect to direct model output.
• In particular, SuperEnsemble is always superior to Ensemble, except for mean precipitation over warning areas and for ETS in general.
• Use of Multimodel SuperEnsemble on really different models in the framework of the Amphore project (Interreg IIIB): LAMI, Aladin, MM5, Bolam, ECMWF (ongoing)
• Extension to other areas and/or variables (observation=ECMWF analysis ?):
• Geopotential
• MSLP
• Tracking of cyclones (original purpose)
• Study of a “confidence bar” in the forecast of any variable by the introduction of the MultiModel for maximum, mean and minimum values over predefined areas (analogous to precipitation)
• Vertical profiles

References

Acknowledgements

• Krishnamurti T.N. et al., Science, 285, 1548-1550, 1999
• Krishnamurti, T. N. et al.,J. Climate, 13, 4196-4216, 2000
• Cane, D., Milelli, M., COSMO Newsletter no. 5 (2005)
• Cane, D., Milelli, M., Meteorologische Zeitschrift, under review

We wish to thank the Deutscher Wetterdienst and MeteoSwiss for providing the models outputs for this research work.