Presentación realizada para: 30th EWGLAM & 15th SRNWP Meetings celebrado del 6 al 9 de octubre de 2008 en Madrid.This project is partially supported by the Spanish Ministry of Education under research projects CGL2004-04095/CLI and CGL2005-05681

Callado, Alfons

García-Moya, José Antonio

Santos Burguete, Carlos

Santos Muñoz, Daniel

Simarro, Juan Pablo

English

Callado Pallarés, Alfons

Simarro Grande, Juan Pablo

Agencia Estatal de Meteorología

1Predictability of precipitation with Aemet multimodel SREPS:assessment using HR observations(yavtobos)CARLOS SANTOS, ALFONS CALLADO, JOSE A. GARCIA-MOYA, DANIEL SANTOS-MUÑOZ AND JUAN SIMARROPredictability Group   Spanish Meteorological Agency (AEMet)EWGLAM Madrid 2008/10/06-08 2Outline• Why Multimodel SREPS?• Verification issues• Performance results• Points: Spain & Europe• Up-scaled: Europe• Predictability of pcp• Concluding remarks3Why an EPS for SR?EWGLAM Madrid 2008/10/06-08 4AEMet Multimodel SREPS • Multimodel• 72 hours forecast• Twice a day (00,12 UTC)• 5x4 = 20 members• 0.25º5Verification issuesPooling vs stratification?SpainEuropeRain gauge networksScoring rulesVerification softwareVerification methodPeriodPrecipitationNew scoresECMWF recommendationsECMWF Metview + LocalConfidence intervalsFeature-orientedObservation pointsUpscalingPooling vs stratification?~90 days (Apr1 to Jun30 2006).Extreme events•Definition•Obs. Quality control24h accumulated precipitation forecast 06UTC-06UTC ag. observed 07UTC-07UTCHH+030 and HH+054Improvements & issuesStandard approachEWGLAM Madrid 2008/10/06-08 6INM pcp network• MultimodelEWGLAM Madrid 2008/10/06-08 7JointEWGLAM Madrid 2008/10/06-08 8Reliability & Sharpness• ReliabilityUnder-samplingOK9Networks Comparison00T+30,54T+30T+30T+30Pcp>5mmPcp>1,5,10,20mmPcp>1,5,10,20mmPcp>20mmBSSRVROCReliabilityEWGLAM Madrid 2008/10/06-08 10INM pcp network• MultimodelEWGLAM Madrid 2008/10/06-08 11Up-scaled HR observations• What is the truth? (yesterday talks)• Verification on points: Independency of realizations?• Up-scaling Europe HR data: a first simple approachavgp95p75p50p25p05EWGLAM Madrid 2008/10/06-08 12Extreme events• EPSs give explicit, quantitative and detailed information about uncertainty. They provide predictability information, and can especially help forecast extreme events.• BUT So far, no mature objective methods to verify extreme events only, or isolated case studies.• Following Group of experts in verification methods for extreme events• Confidence intervals on scores• New scores not sensitive to vanishing sample rates (very rare events) e.g. EDS• Feature-oriented, fuzzy verification methods (Ebert, Casati) might show a more realistic information about performance (by better representation of actual pcp), e.g. SAL decomposition13Predictability of pcpTested methods for probabilistic pcp forecasts:• Diffusive methods (Theis)• Hybrid ensembles (Jun Du)Near future improvements on Aemet SREPS pcp forecasts:• Non gaussian BMA (gaussian succesful for 10m wind speed)EWGLAM Madrid 2008/10/06-08 14Conclusions & [near] future• High performance of the AEMet short-range multi-model ensemble 24h probabilistic precipitation forecasts using HR pcp observations• On points: good reliability & resolution, independently on the different frequency of occurrence (base rate) on each network and threshold, thus overcoming different skill difficulties• Up-scaling: first simple approach, promising• Future improvements on verification methods• 1 year verification up-scaling HR pcp observations• Following Group of experts in extreme verification methods• Future plans to improve SREPS pcp forecasts• Promising non-gaussian BMA on acc pcp• Increase model resolution of individual members (currently ~ 0.25ºL40)EWGLAM Madrid 2008/10/06-08 15Aknowledgements• Eugenia Kalnay (Univ. Of Maryland), • Ken Mylne, Jorge Bornemann (MetOffice)• Detlev Majewski, Michael Gertz, Michael Denhard  (DWD)• Metview Team, Martin Leutbecher (ECMWF)• Chiara Marsigli, Ulrich Schättler (COSMO)• Olivier Talagrand (LMD)• We also like to thank many Met. Services for making their climate network precipitation observations available to us for verification (some of them not yet included): Arpa-Sim (Italy), DWD (Germany), EARS (Slovenia), HNMS (Greece), HMS (Hungary), KNMI (Netherlands), Lombardia (Italy), Météo-France (France), NIMH (Bulgaria), NMAP (Romania), SHMU (Slovakia), UKMO (UK), ZAMG (Austria).• This project is partially supported by the Spanish Ministry of Education under research projects CGL2004-04095/CLI and CGL2005-05681EWGLAM Madrid 2008/10/06-08 16Any HR pcp datasets are welcome!!!csantos@inm.esThank youEWGLAM Madrid 2008/10/06-08 17Extras(Bonus slides)EWGLAM Madrid 2008/10/06-08 18Team• José A. García-Moya.• Carlos Santos (Hirlam, verification & graphics, web server).• Daniel Santos (MM5, Bayesian Model Average).• Alfons Callado (UM & grib software).• Juan Simarro (HRM, LM and Vertical interpolation software).EWGLAM Madrid 2008/10/06-08 19References• Jolliffe, I. T., Stephenson, D.B., 2003: Forecast Verification: A Practitioner's Guide in Atmospheric Science, John Wiley and Sons, Chichester• Verification of ECMWF products in Member States and Co-operating States, Report 2005. ECMWF, A1-A15. • Hou  D., Kalnay E., & Droegemeier, K.K., 2001: Objective Verification of the SAMEX’98 Ensemble Forecasts. M.W.R., 129, 73-91.• Buizza, R., A. Hollingsworth, F. Lalaurette, and A. Ghelli, 1999: Probabilistic predictions of precipitation using the ECMWF Ensemble Prediction System. ECMWF• Stensrud D. J., H. E. Brooks, J. Du, M. S. Tracton, and E. Rogers, 1999: Using Ensembles for Short-Range Forecasting, M.W.R., 127, 433-446• Arribas A., Robertson K.B., & Mylne, K.R., 2005: Test of Poor Man’s Ensemble Prediction System. M.W.R., 133, 1825-1839EWGLAM Madrid 2008/10/06-08 20Links• WWRP/WGNE Joint Working Group on Verification, Forecast Verification - Issues, Methods and FAQhttp://www.bom.gov.au/bmrc/wefor/staff/eee/verif/verif_web_page.html• VERIFICATION SYSTEMS FOR LONG-RANGE FORECASTS NEW, Standard Verification System (SVS) for Long-range Forecasts (LRF)http://www.wmo.ch/web/www/DPS/verification_systems.html• ECMWF EPS Verification http://www.ecmwf.int/products/forecasts/d/charts/medium/verification/EWGLAM Madrid 2008/10/06-08 21• Predictability is flow dependent• Extreme weather events have a low predictability, uncertainties can grow critically even in the Short Range (less than 72 hours), • Convection is highly non-linear and it shows a chaotic behaviour. • Then a probabilistic apprach may help to improve the prediction of such phenomena.IntroductionEWGLAM Madrid 2008/10/06-08 22• Surface parameters are the most important ones for weather forecast.• Forecast of extreme events (convective precip, gales,…) is probabilistic.• Short Range Ensemble prediction can help to forecast these events.• Forecast risk (Palmer, ECMWF Seminar 2002) is the goal for both Medium- and, also, Short-Range Prediction.Ensemble for short rangeEWGLAM Madrid 2008/10/06-08 23• Main Weather Forecast issues are related with Short-Range extreme events.• Convective precipitation is the most dangerous weather event in Spain.• Western Mediterranean is a close sea rounded by high mountains, in autumn sea is warmer than air.• Several cases of more than 200 mm/few hours every year. Some fast cyclogenesis like “tropical cyclones”.Meteorological FrameworkEWGLAM Madrid 2008/10/06-08 24RV• We use a simple algorithm to compute acc pcp rank histograms avoiding “zero problems”• Over all those points with obs=0 and M of N fcs=0 the rank of the observation is not really zero (though it seems with some algorithms which plot a spurious overload of “zero ranks”)• In those cases, a random rank {0..M} can be assigned, which is the same that to add 1/M to all bins in {0,M}. Always under the assumption that the number of realizations is large enough• With this method more realistic rank histograms can be achievedEWGLAM Madrid 2008/10/06-08 25Reliability & Sharpness• Good reliability according to• thresholds (base rate)• forecast lengthUnder-samplingEWGLAM Madrid 2008/10/06-08 26Reliability & Sharpness• Good reliability according to• thresholds (base rate)• forecast lengthNoUnder-samplingView publication statsView publication stats

Predictability of precipitation with Aemet multimodel SREPS: assessment using HR observations (yavtobos)

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Predictability of precipitation with Aemet multimodel SREPS: assessment using HR observations (yavtobos)

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