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IRI Experience in Forecasting and Applications for the Mercosur Countries

IRI Experience in Forecasting and Applications for the Mercosur Countries. Purely Empirical (observational) approach. Mason & Goddard, 2001, Bull.Amer.Meteor.Soc. Prediction Systems: statistical vs. dynamical system. ADVANTAGES Based on actual, real-world

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IRI Experience in Forecasting and Applications for the Mercosur Countries

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  1. IRI Experience in Forecasting and Applications for the Mercosur Countries

  2. Purely Empirical (observational) approach Mason & Goddard, 2001, Bull.Amer.Meteor.Soc.

  3. Prediction Systems: statistical vs. dynamical system ADVANTAGES Based on actual, real-world observed data. Knowledge of physical processes not needed. Many climate relationships quasi-linear, quasi-Gaussian ------------------------------------ Uses proven laws of physics. Quality observational data not required (but helpful for val- idation). Can handle cases that have never occurred. DISADVANTAGES Depends on quality and length of observed data Does not fully account for climate change, or new climate situations. ------------------------------ Some physical laws must be abbreviated or statis- tically estimated, leading to errors and biases. Computer intensive. Stati- stical ------- Dyna- mical

  4. Dynamical Prediction System: 2-tiered vs. 1-tiered forecast system ADVANTAGES Two-way air-sea interaction, as in real world (required Where fluxes are as important as large scale ocean dynamics) ------------------------------------- More stable, reliable SST in the prediction; lack of drift that can appear in 1-tier system Reasonably effective for regions impacted most directly by ENSO DISADVANTAGES Model biases amplify (drift); flux corrections Computationally expensive ------------------------------ Flawed (1-way) physics, especially unacceptable in tropical Atlantic and Indian oceans (monsoon) 1-tier ------- 2-tier

  5. IRI DYNAMICAL CLIMATE FORECAST SYSTEM 2-tiered OCEAN ATMOSPHERE GLOBAL ATMOSPHERIC MODELS ECPC(Scripps) ECHAM4.5(MPI) CCM3.6(NCAR) NCEP(MRF9) NSIPP(NASA) COLA2 GFDL PERSISTED GLOBAL SST ANOMALY Persisted SST Ensembles 3 Mo. lead 10 POST PROCESSING MULTIMODEL ENSEMBLING 24 10 FORECAST SST TROP. PACIFIC: THREE (multi-models, dynamical and statistical) TROP. ATL, INDIAN (ONE statistical) EXTRATROPICAL (damped persistence) 10 Forecast SST Ensembles 3/6 Mo. lead 24 12 30 12 30 30

  6. Method of Forming 3 SST Predictions for Climate Predictions damped persistence -------------------------------------------------- 0.25 (1) NCEP CFS (2) LDEO (3) CPC Constr Analog IRI CCA CPTEC CCA -------------------------------------------------- damped persistence

  7. Method of Forming 4th SST Prediction for Climate Predictions (4)Anomaly persistence from most recently observed month (all oceans)

  8. Collaboration on Input to Forecast Production Sources of the Global Sea Surface Temperature Forecasts Tropical Pacific Tropical Atlantic Indian Ocean Extratropical Oceans NCEP Coupled CPTEC Statistical IRI Statistical Damped PersistenceLDEO Coupled Constr Analogue Atmospheric General Circulation Models Used in the IRI's Seasonal Forecasts, for Superensembles Name Where Model Was Developed Where Model Is Run NCEP MRF-9 NCEP, Washington, DC QDNR, Queensland, AustraliaECHAM 4.5 MPI, Hamburg, Germany IRI, Palisades, New YorkNSIPP NASA/GSFC, Greenbelt, MD NASA/GSFC, Greenbelt, MDCOLA COLA, Calverton, MD COLA, Calverton, MD ECPC SIO, La Jolla, CA SIO, La Jolla, CA CCM3.6 NCAR, Boulder, CO IRI, Palisades, New York GFDL GFDL, Princeton, NJ GFDL, Princeton, NJ

  9. Forecasts of the climate The tercile-based category system: Below, near, and above normal Probability: 33% 33% 33% Below| Near | Below| Near | Above Below| Near | | || ||| ||||.| || | | || | | | . | | | | | | | | | | Data: 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 Rainfall Amount (mm) (30 years of historical data for a particular location & season)

  10. Monthly issued probability forecasts of seasonal global precipitation and temperature Four lead times - example: JUL | Aug-Sep-Oct* Sep-Oct-Nov Oct-Nov-Dec Nov-Dec-Jan *probabilities of extreme (low and high) 15% issued also

  11. good skill very good skill OND 1997 OND 1998

  12. somewhat negative skill fairly good skill OND 1999 OND 2000

  13. Neutral (zero) skill good skill OND 2001 OND 2002

  14. neutral (zero) skill negative skill OND 2003 OND 2004

  15. Historical skill using actually observed SST Ensemble mean (10 members)

  16. Historical skill using actually observed SST Ensemble mean (24 members)

  17. OND

  18. JFM

  19. AMJ

  20. JAS

  21. Indonesia Australia/ Indonesia OND Precip 1950- 2000 North America Europe Tropical Americas South America

  22. Some Themes in Customer Demands: • Higher skills in existing products: • -Tighter range of possibilities • 2. More usable and easily understood product • 3. Expansion of repertoire of products • beyond seasonally averaged climate

  23. Specifically, users consistently request: More concrete forecast format Direct use of physical units (inches, degr C) Best guess quantity, with comparison to average More flexibility in probabilistic forecast format More temporal detail than 3 month averages Outlooks for single months, even at long lead Rainy season (or monsoon) onset time Weather features within season (e.g. dry spells) Chance of extreme event (storm, flood, drought) Climate change nowcast (the new “normal”)

  24. Almost all users want reliable probabilities (probabilities that would match observed frequencies of occurrence over a large sample of identical forecasts). Even if reliable, users for some applications are not impressed with slight changes from the climatological precipitation probabilities. They are interested in forecasts having probabilities that deviate strongly from the neutral, climatological probabilities.

  25. OND 1997

  26. Ranked Probability Skill Score (RPSS) 3 probprob RPSfcst =  (Fcsticat – Obsicat)2 icat=1 icat ranges from 1 (below normal) to 3 (above normal) icat is cumulative, from 1 to icat RPSS = 1 - (RPSfcst / RPSclim) RPSclim is RPS of forecasts of climatology (33%,33%,33%)

  27. Real-time Forecast Skill (from Goddard et al. 2003, BAMS, p1761)

  28. Real-time Forecast Skill (from Goddard et al. 2003, BAMS, p1761)

  29. Real-time Forecast Skill (from Goddard et al. 2003, BAMS, p1761

  30. 1998-2001 Reliability Diagram from Barnston et al. 2003, BAMS, p1783

  31. Reliability Diagram longer “AMIP” period from Goddard et al. 2003 (EGS-AGU-EUG)

  32. IRI Forecast Information Pages on the Web IRI Home Page: http://iri.columbia.edu/ ENSO Quick Look: http://iri.columbia.edu/climate/ENSO/currentinfo/QuickLook.html IRI Probabilistic ENSO Forecast: http://iri.columbia.edu/climate/ENSO/currentinfo/figure3.html Current Individual Numerical Model Climate Forecasts http://iri.columbia.edu/forecast/climate/prec03.html Individual Numerical Model Hindcast Skill Maps http://iri.columbia.edu/forecast/climate/skill/SkillMap.html Current Multi-model Climate Forecasts http://iri.columbia.edu/forecast/climate/multi_model2003.html

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