Meteorological experience from the Olympic Games of Torino 2006

Transcription

1 Meteorologcal experence from the lympc Games of Torno 6 ARPA PIEMTE th CM General Meetng Moscow, 6- eptember

2 ummary Multmodel general Theory Models & Varables Multmodel calculaton: case of precptaton Recommendatons ample of results Comments ther PPT used n ARPA Pemonte (now) References

3 Multmodel general Theory As suggested by the name, the Multmodel uperensemble method requres several model outputs, whch are weghted wth an adequate set of weghts calculated durng the so-called tranng perod. The smple Ensemble respectvely, s gven by ( ) method wth bas-corrected or based data ( ) () or () The conventonal uperensemble forecast (Krshnamurt et. al., ) constructed wth bas-corrected data s gven by a ( ) (3)

4 Multmodel general Theory As suggested by the name, the Multmodel uperensemble method requres several model outputs, whch are weghted wth an adequate set of weghts calculated durng the so-called tranng perod. The smple Ensemble respectvely, s gven by ( ) method wth bas-corrected or based data ( ) () or () The conventonal uperensemble forecast (Krshnamurt et. al., ) constructed wth bas-corrected data s gven by a ( ) (3) number of models

5 Multmodel general Theory As suggested by the name, the Multmodel uperensemble method requres several model outputs, whch are weghted wth an adequate set of weghts calculated durng the so-called tranng perod. The smple Ensemble respectvely, s gven by ( ) method wth bas-corrected or based data ( ) () or () The conventonal uperensemble forecast (Krshnamurt et. al., ) constructed wth bas-corrected data s gven by a ( ) (3) th model forecast

6 Multmodel general Theory As suggested by the name, the Multmodel uperensemble method requres several model outputs, whch are weghted wth an adequate set of weghts calculated durng the so-called tranng perod. The smple Ensemble respectvely, s gven by ( ) method wth bas-corrected or based data ( ) () or () The conventonal uperensemble forecast (Krshnamurt et. al., ) constructed wth bas-corrected data s gven by a ( ) (3) mean forecast

7 Multmodel general Theory As suggested by the name, the Multmodel uperensemble method requres several model outputs, whch are weghted wth an adequate set of weghts calculated durng the so-called tranng perod. The smple Ensemble respectvely, s gven by ( ) method wth bas-corrected or based data ( ) () or () The conventonal uperensemble forecast (Krshnamurt et. al., ) constructed wth bas-corrected data s gven by a ( ) (3) observaton mean

8 Multmodel general Theory As suggested by the name, the Multmodel uperensemble method requres several model outputs, whch are weghted wth an adequate set of weghts calculated durng the so-called tranng perod. The smple Ensemble respectvely, s gven by ( ) method wth bas-corrected or based data ( ) () or () The conventonal uperensemble forecast (Krshnamurt et. al., ) constructed wth bas-corrected data s gven by a ( ) (3) Multm. weghts

9 ( ) ( )( ) ( )( ) ( )( ) ( ) ( )( ) ( ) ( )( ) ( )( ) T T T T T T T T T a a M M M M M M L L M M M L a G ( ) T G The calculaton of the parameters a s gven by the mnmsaton of the mean square devaton by dervaton we obtan a set of equatons, where s the number of models nvolved: We then solve these equatons usng Gauss-Jordan method.

10 Models & Varables lympc Games peratonal Use CM-I7 (UTC UTC) CM-7 (UTC UTC) CM-EU (UTC UTC) ECMW-I (UTC UTC) CM-I7 (UTC UTC) CM-ME (UTC UTC) ECMW-I (UTC UTC) Tm, Rhm, Wm, Precptaton

11 Multmodel calculaton: case of precptaton model model model 3 model 4 model 5 model 6 model 7 model 8 nly models out of 8 gve some precptaton for the gven pont and forecast tme. Probably the best forecast should be no precptaton. uperensemble ever gves a result! a ( ) How to avod ths precptaton overestmaton?

12 alse alarm rato RC (Recever peratng Characterstc) Probablty of detecton (ht rate HRR) HRR AR

13 How we calculate the global RC of Multmodel: model model model 3 model 4 model 5 model 6 model 7 model 8 or each model n the tranng perod we calculate the HRR and AR AR AR AR 3 AR 4 AR 5 AR 6 AR 7 AR 8 HRR HRR HRR 3 HRR 4 HRR 5 HRR 6 HRR 7 HRR 8 Then we calculate the Multmodel AR and HRR by combnng the gven values wth respect to what models are forecastng now: AR MM AR (-AR ) ( - AR 3 ) ( - AR 4 ) AR 5 (-AR 6 ) ( - AR 7 ) ( - AR 8 ) HRR MM HRR (-HRR ) ( - HRR 3 ) ( - HRR 4 ) HRR 5 (-HRR 6 ) ( - HRR 7 ) ( - HRR 8 ) I AR MM >HRR MM Multmodel uperensemble s put to

14 Recommendatons Many tests have been performed n the years: dfferent combnaton of models (LAMs alone or LAMs and ECMW) dfferent numbers of models (UTC or/and UTC runs) dfferent nd of tranng perod (fxed or dynamcal) dfferent lengths of the tranng perod comparsons among uperensemble, PoorMenEnsemble and Kalman flter And the answers are:

15 Recommendatons ECMW model MUT be ncluded there s an added value up to 6/8 models the dynamcal tranng permts to tae nto account the seasonal varaton of the model performances the mnmum lengths (for the models and the area we use!!!) are: 9 days for Tm, Rhm and Wm 8 days for precptaton E wors better than PME and (concernng temperature) s bascally equvalent to Kalman flter appled to ECMW model. The advantage s that the same algorthm t can be appled to other varables (precptaton)

16 ample of results uperens ECMW ECMW CM-I7 CM-I7 uperens ECMW ECMW CM-I7 CM-I RT MEA QUARE ERRR ( C) RT MEA QUARE ERRR ( C) forecast tme (h) forecast tme (h) uperens ECMW ECMW CM-I7 CM-I7-7 m 7-5 m 5-35 m RME of Tm durng ebruary and March 6 n Pemonte regon RT MEA QUARE ERRR ( C) forecast tme (h)

17 ample of results uperens ECMW ECMW CM-I7 CM-I7 uperens ECMW ECMW CM-I7 CM-I RT MEA QUARE ERRR (%) RT MEA QUARE ERRR (%) forecast tme (h) forecast tme (h) uperens ECMW ECMW CM-I7 CM-I m 7-5 m 5-35 m RME of RHm durng ebruary and March 6 n Pemonte regon RT MEA QUARE ERRR (%) forecast tme (h)

18 ample of results uperens ECMW ECMW CM-I7 CM-I7 uperens ECMW ECMW CM-I7 CM-I7 6 6 RT MEA QUARE ERRR (m/s) RT MEA QUARE ERRR (m/s) forecast tme (h) forecast tme (h) uperens ECMW ECMW CM-I7 CM-I7-7 m 7-5 m 5-35 m RME of Wm durng ebruary and March 6 n Pemonte regon RT MEA QUARE ERRR (m/s) forecast tme (h)

19 ample of results bs uperens ECMW ECMW CM-I7 CM-I7 bs uperens ECMW ECMW CM-I7 CM-I7 4 9 DIURAL CYCLE ( C) DIURAL CYCLE (%) forecast tme (h) forecast tme (h) ss uperens ECMW ECMW CM-I7 CM-I7 Tm RHm 6 5 Wm Durnal cycle durng ebruary 6 n Pemonte regon between 7 and 5 m DIURAL CYCLE (m/s) forecast tme (h)

20 ample of results bs uperens ECMW ECMW CM-I7 CM-I7 bs uperens CM-I7 ECMW CM-I7 ECMW 4 9 DIURAL CYCLE ( C) DIURAL CYCLE (%) forecast tme (h) forecast tme (h) bs uperens ECMW ECMW CM-I7 CM-I7 Tm RHm 6 5 Wm Durnal cycle durng ebruary 6 n Pemonte regon between 5 and 35 m DIURAL CYCLE (m/s) forecast tme (h)

21 ample of results. uperens ECMW ECMW CM_I7 CM_I7. uperens ECMW ECMW CM_I7 CM_I7 Precptaton ndces for: verfcaton Multmodel uperensemble ECMW I UTC ECMW I UTC CM-I7 UTC CM-I7 UTC as a functon of the threshold (mm) for h/36h forecasts. Average values (left) and maxmum values (rght). BIA HRR uperens ECMW ECMW CM_I7 5 CM_I7 5 uperens ECMW ECMW CM_I7 5 CM_I7 5 HRR BIA uperens ECMW ECMW CM_I7 CM_I uperens ECMW ECMW CM_I7 5 CM_I Perod: eptember 6- August 8 Area: Pemonte AR AR

22 ample of results Precptaton ndces for: verfcaton Multmodel uperensemble ECMW I UTC ECMW I UTC CM-I7 UTC CM-I7 UTC as a functon of the season for h/36h forecasts. Average values, threshold mm (left) and maxmum values, threshold 35 mm (rght). Perod: eptember 6- August 8 Area: Pemonte BIA uperens ECMW ECMW CM_I7 CM_I DJ DJ uperens ECMW ECMW CM_I7 CM_I HRR DJ AR DJ DJ uperens ECMW ECMW CM_I7 CM_I DJ uperens ECMW ECMW CM_I7 CM_I7 3.5 BIA.5.5 DJ DJ HRR DJ DJ uperens ECMW ECMW CM_I7 CM_I AR DJ uperens ECMW ECMW CM_I7 CM_I DJ 8 8 8

23 Comments Temperature ver the plans the temperature forecasts are acceptable, but n the mountans, mean errors of up to 6/7 C occurred. Th e post-processng method enabled a consderable mprovement n the forecasts for all the consdered venues. The mprovement s partcularly evdent n the mountans, where the mean error was reduced to values below C, whle the RME was usually below C In general, the Kalman flter was slghtly less based, wth mean error values always close to zero, ndcatng that, n general, the values were nether overestmated nor underestmated. n the other hand, the Multmodel uperensemble, despte slghtly larger bas, reported lower values for the RME, ndcatng smaller errors than the Kalman flter

24 Comments ther varables (Wnd and Relatve Humdty) The DM of CM-I7 was useful for estmatng wnd gusts, but for the average wnd feld, uperensemble was more relable. The breeze condtons n the mountans were not seen at all by the models (and therefore by any nd of post-processng ) The DM underestmates RH n the valleys and over the plans, but wth the uperensemble technque we could correct ths trend

25 Comments Precptaton uperensemble error s more stable than Drect Model utput uperensemble gves better results than Ensemble and Poor- Man Ensemble Tranng perod: the longest the better (bascally for statstcs reasons, but for a larger area t should be easer) The RC correcton wors correctly

26 ther PPT used n ARPA Pemonte (now) The DM of CM-I mght contan a consderable amount of nose! Experenced forecasters nterpret output from a very hgh resoluton determnstc forecast n a probablstc way "hgh probablty of some heavy near Tcno and Toce valley ", not 8 mm of ran wll fall n x,y"

27 eghbourhood method (Thes et al., 5) appled to CM-I. orecasts wthn a neghbourhood n space & tme consttute a sample of the forecast at grd pont A, gven the hypothess that spatal and temporal uncertantes are ndependent. The radus, the shape and the tme extenson must be calbrated emprcally.

28 f course here we have a problem wth verfcaton eed for very hgh resoluton (n space and tme) observatons nly eye-ball verfcaton at the moment

29 References (uperensemble) Krshnamurt, T.., Kshtawal, C.M., Larow, T., Bachoch, D., Zhang, Z., Wllford, C.E., Gadgl,., urendran,., Mult-Model uperensemble orecasts or Weather And easonal Clmate. J. Clmate, 3, ,. Cane, C., and Mlell, M., "Multmodel uperensemble technque for weather parameter forecasts n Pemonte regon", at. Hazards Earth yst. c. (HE),, 65-73, Cane, D., and Mlell, M., "Comparson of CM models and Multmodel uperensemble outputs n Pemonte", CM ewsletter o. 9, 69-7, 9 Mlell, M., and Cane, D., "Use of Multmodel uperensemble technque for complex orography weather forecast", CM ewsletter o. 6, 46-5, 6 Cane, D., and Mlell, M., "Weather forecasts wth Multmodel uperensemble Technque n a complex orography regon", Meteorologsche Zetschrft, Vol. 5, o., 7-4, 6 Cane, D., and Mlell, M., "A new method for tm forecast n complex orography areas", CM ewsletter o. 5, 5-57, 5

30 References (eghbourhood Method) Thes,., Hense, A., and Damrath, U., Probablstc precptaton forecasts from a determnstc model: a pragmatc approach. Meteorologcal Applcatons,, 3, 57-68, 5 Kaufmann, P., Post-processng of the almo Precptaton wth the eghbourhood Method. CM ewsletter o. 7, 57-6, 7 Turco, M., and Mlell, M., "Towards peratonal Probablstc Precptaton orecast", CM ewsletter o. 9, 56-6, 9

31 Than you for the attenton and GD LUCK R YUR LYMPIC GAME!