Use of Multimodel SuperEnsemble Technique for Mountain-area weather forecast in the Olympic Area of Torino 2006

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3 Use of Multimodel SuperEnsemble Technique for Mountain-area weather forecast in the Olympic Area of Torino 2006 Daniele Cane, Massimo Milelli ICAM-MAP, Zadar, May 23 rd -27 th, 2005 Dr. Daniele Cane Organising Committee for the XX Olympic Winter Games Environmental Protection Agency ARPA Piemonte

4 Overview Multimodel Ensemble and SuperEnsemble Theory temperature precipitation winds humidity 4

5 The XX Olympic Winter Games The XX Olympic Winter Games will be held in Torino, Italy, on ebruary 0-26, 2006 The IX Paralympic Winter Games will be held in Torino on March 0-9, The Olympic mountain venues are set in middle-, high- and very high-mountain places in the Susa and Chisone Valleys in Piedmont (up to 2600 m above sea level) 5

6 Model performances in the Olympic Area Weather forecasts are strongly dependent on the complex geography and orography of these valleys. Direct model outputs, even from high-resolution limited area models, show many strong systematic and random errors in the forecast, compared to the values observed by our high-density non-gts networ. We present some results of the Multimodel SuperEnsemble technique (Krishnamurti et al. 2000) applied on both global circulation models and on non-hydrostatic limited-area models. The Multimodel SuperEnsemble technique taes into account various model outputs, weighted by parameters calculated in a training period. 6

7 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 S O + ( ) i i i () or (2) S O + ( ) i O i The conventional SuperEnsemble forecast (Krishnamurti et. al., 2000) constructed with bias-corrected data is given by S O + i a i ( ) i i (3) number iobservation Multim. th mean model forecast of weights models mean 7

8 8 Multimodel Theory The calculation of the parameters a i is given by the minimisation of the mean square deviation by derivation we obtain a set of equations, where is the number of models involved: ( ) 2 T S O G 0 a i G ( ) ( )( ) ( )( ) ( )( ) ( ) ( )( ) ( ) ( )( ) ( )( ) T T T T T T T T T O O O O a a Μ Μ Μ Μ Μ Μ Λ Λ Μ Ο Μ Μ Λ We then solve these equations using Gauss-Jordan method.

9 Multimodel Results Previous wor In a previous wor (Cane and Milelli, 2005) we applied the Multimodel technique on the operational runs of the 625 resolution version of the limited-area model LM (00 2 UTC runs), over the warning areas of Piedmont region. Local Area Model Italy (UGM, ARPA-SIM, ARPA Piemonte) Loal Modell (Deutscher Wetterdienst) alpine Model (MeteoSwiss) This was one of the first implementations of Multimodel technique on limited-area models (in this case of 625 resolution) and we obtained a strong improvement in temperature forecasts in Piedmont region. 9

10 Multimodel Results This wor In this wor we focus on the Olympic Area where the XX Olympic Winter Games and the IX Paralympic Winter Games will be held on The Multimodel technique is applied also on the operational 2 UTC run of the ECMW IS global model. We used the data from the very dense Piedmontese non-gts networ for the calculation of the weights in the training period and for validation purposes. 0

11 Multimodel Results This wor We applied Multimodel SuperEnsemble technique on 2m temperature forecasts, compared with the measurements of 0 middle-mountain stations (700 m < height < 500) and 2 high-mountain stations (height > 500 m). Average and maximum precipitation over the complete area wind relative humidity

12 Multimodel Results Temperature Models used for Multimodel: ECMW, LAMI, LM-DWD, Swiss LM. Each model is interpolated at the station location correcting for station elevation. orecast period: ebruary 2004 (Olympic period). Training period: instead of using a fixed long training period, as in Krishnamurti et al., 2000, we preferred to use a dynamic short training period to tae into account the seasonal variation of model performances. or each forecast day, forecast time and station we considered the 90 days before as training period, we calculated the forecast and observation means and the Multimodel weights and then we obtained Ensemble and SuperEnsemble forecast. Validation: here we present global Root Mean Square Error and the Mean Error (or bias) of Multimodel SuperEnsemble, Ensemble and of the four models. 2

13 Multimodel Results Temperature A) B) SuperEns Ensemble ld00 nud00 alm00 ecm2 SuperEns Ensemble ld00 nud00 alm00 ecm2 MEA ERROR ( C) RMSE ( C) RMSE ( C) MEA ERROR ( C) Temperature results. Root Mean Square Error (top) and Mean Error (bottom) of forecast vs observations for SuperEnsemble (SuperEns), Ensemble, German LM (ld00), Italian LM (nud00), Swiss LM (alm00) and ECMW IS (ecm2). A) 700 m < station height < 500 m B) station height > 500 m. 3

14 Multimodel Results Precipitation Models used for Multimodel: ECMW, LAMI, LM-DWD, Swiss LM. Average and maximum values were also calculated for each model, taing into account the grid points covering the given area. orecast period: year 2004 (for statistical significance). Training period: fixed training period of 80 days before the forecast period. or each forecast time we calculated the forecast and observation means and the Multimodel weights, and then we obtained Ensemble and SuperEnsemble forecast for each forecast day. Validation: ormalized Bias and Equitable Threat Score (ETS). 4

15 Multimodel Results Precipitation AVERAGE SuperEns Ens alm00 ld00 nud00 ecm2 MAXIMUM SuperEns Ens alm00 ld00 nud00 ecm2 BIAS BIAS ETS 0.3 ETS precipitation (mm) precipitation (mm) Precipitation results. ormalized Bias (top) and Equitable Threat Score (bottom) of forecast vs observations for SuperEnsemble (SuperEns), Ensemble, German LM (ld00), Italian LM (nud00), Swiss LM (alm00) and ECMW IS (ecm2). 5

16 Multimodel Results Wind (preliminary results) Models used for Multimodel: LAMI and ECMW IS (00 and 2 UTC operational runs). earest grid point wind of the given model to the given station. orecast period: March weather stations from Piedmontese non-gts networ (over the whole Piedmontese region). Training period: dynamic 90 days training period before the forecast day, for each forecast time and station. Validation: Root Mean Square Error and Mean Error (or bias) of Multimodel SuperEnsemble, Ensemble and of the two models. 6

17 Multimodel Results Wind (preliminary results) 53 stations h < 700 m 35 stations 700 m< h < 500 m 5 stations h > 500 m SuperEns Ensemble nud00 ecm2 nud2 ecm00 SuperEns Ensemble nud00 ecm2 nud2 ecm00 SuperEns Ensemble nud00 ecm2 nud2 ecm MEA ERROR (m/s) MEA ERROR (m/s) MEA ERROR (m/s) SuperEns Ensemble nud00 ecm2 nud2 ecm00 SuperEns Ensemble nud00 ecm2 nud2 ecm00 SuperEns Ensemble nud00 ecm2 nud2 ecm00 ROOT MEA SQUARE ERROR (m/s) ROOT MEA SQUARE ERROR (m/s) ROOT MEA SQUARE ERROR (m/s) Wind speed preliminary results. Mean Error (top) and Root Mean Square Error (bottom) of forecast vs observations for SuperEnsemble (SuperEns), Ensemble, Italian LM (nud00 and nud2) and ECMW IS (ecm00 and ecm2). 7

18 Multimodel Results Relative humidity (preliminary results) Models used for Multimodel: ECMW, LAMI, LM-DWD, Swiss LM. (00 and 2 UTC operational runs). Each model is interpolated at the station location correcting for station elevation. orecast period: March weather stations from Piedmontese non-gts networ (over the whole Piedmontese region). Training period: dynamic 90 days training period before the forecast day, for each forecast time and station. Validation: Root Mean Square Error and Mean Error (or bias) of Multimodel SuperEnsemble, Ensemble and of the four models. 8

19 Multimodel Results Relative humidity (preliminary results) 53 stations h < 700 m 35 stations 700 m< h < 500 m 5 stations h > 500 m SuperEns Ensemble nud00 ld00 alm00 ecm2 nud2 ld2 alm2 ecm00 SuperEns Ensemble nud00 ld00 alm00 ecm2 nud2 ld2 alm2 ecm00 SuperEns Ensemble nud00 ld00 alm00 ecm2 nud2 ld2 alm2 ecm MEA ERROR (%) MEA ERROR (%) MEA ERROR (%) SuperEns Ensemble nud00 ld00 alm00 ecm2 nud2 ld2 alm2 ecm00 SuperEns Ensemble nud00 ld00 alm00 ecm2 nud2 ld2 alm2 ecm00 SuperEns Ensemble nud00 ld00 alm00 ecm2 nud2 ld2 alm2 ecm00 ROOT MEA SQUARE ERROR (%) ROOT MEA SQUARE ERROR (%) Relative humidity preliminary results. Mean Error (top) and Root Mean Square Error (bottom) of forecast vs observations for SuperEnsemble (SuperEns), Ensemble, German LM (ld00 and l2), Italian LM (nud00 and nud2), Swiss LM (alm00 and alm2) and ECMW IS (ecm00 and ecm2). ROOT MEA SQUARE ERROR (%) 9

20 Multimodel Ensemble and SuperEnsemble technique are applied for the first time on three versions of the LM limited area model together with the ECMW IS global model in the Area of the XX Olympic Winter Games. Multimodel SuperEnsemble gives a good improvement of temperature forecast, with BIAS and RMSE reduction. We obtained an improvement also in average precipitation forecast, while in maximum precipitation forecast Multimodel SuperEnsemble gives results comparable with the best model. Good preliminary results from extension of Multimodel technique to the whole Piedmont region, as far as other parameters are concerned. In the framewor of the Interreg IIIB-Medocc project Amphore the Multimodel technique will be applied on really different operational models: LAMI, Aladin, MM5, RAMS, Bolam, ECMW. 20

21 Cane, D., Milelli, M., 2005: COSMO ewsletter, submitted. Krishnamurti T.. et al., 999: Science, 285, Krishnamurti, T.. et al., 2000: J. Climate, 3, Acnowledgement We wish to than the Deutscher Wetterdienst and MeteoSwiss for providing the model outputs for this research wor. Thans to Dr. Elena Oberto for very useful discussion about the Multimodel bias reduction in cases of very low forecast precipitation. 2

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