forecast development at the iri michael k. tippett
TRANSCRIPT
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Forecast development at the IRI
• Michael K. Tippett
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Our approachVisionValues
Past, present and future strategies
Outline
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VisionProvide global climate forecasts for societal benefit
ValuesHigh-quality climate forecast information/ingredients
In-housePartners
Transform forecast information into useful productsMatch user systems with scientific capabilities
Products informed by research (R2O/O2R)PhysicalSocial
Provide benefit via product content as well as process“Best practices”
Approach to forecast development
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One that is used.Fits into a user’s decision system
Hard to convince users to change their systemsEasier to get them to add inputs
Available.Data library
Trusted.Verification
What is a good forecast product?
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Forecast verification
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Forecast inputs/ingredientsForecast modelsObservational data
ProductsCategorical probabilitiesPDFs
MethodologiesCombination/calibration of forecast inputsProduct delivery
Strategic elements
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Carbon
Ocean
AtmosphereLand
Chemistry
Ice
Differing classes of forecast models
http://www.cmmap.org
Two classes of forecast models:• Ocean-atmosphere coupled
models: Initial state of climate system is prescribed
• Atmosphere-only models: Future SST is prescribed
Coupled processes
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The very beginning
Forecast ingredients:• Prescribed SST AGCMs (not coupled)Products• Issued seasonally• 3-month averages• Near-surface temperature and precipitation• Tercile probabilities• No digital dataMethodology• Basis for RCOF (subjective)• Manual map production
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ATB (After Tony Barnston)
Forecast ingredients:• Prescribed SST AGCMs (not coupled)Products• Issued monthly• 3-month averages• Near-surface temperature and precipitation• Tercile probabilities• Digital data availableMethodology• Objective estimation of probabilities• Automated map production (CRED)
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Present
Forecast ingredients:• Prescribed AGCMs• CFSv2 (coupled)Products• Issued monthly• 3-month averages• Near-surface temperature and precipitation• Maps of tercile probabilities• Full PDFs• Digital data via Data LibraryMethodology• Objective estimation of probabilities• Automated map production• More realistic estimates of uncertainty
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Flexible forecast format maproom
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Forecast inputsMore coupled modelsNMME
ProductsAdditional quantities, time-scalesLeverage emerging research
MethodologiesMore agile, able to adapt to changing inputsLeverage emerging research
Future
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Forecast and monitoring of regional extremes
Observations
Forecasts
Verification timeFo
reca
st le
ad (d
ays)
Monitor and forecastregional indices e.g.:• Rainfall• Severe weather• Fire
Motivated by IRFC collaboration
0-lead
45-lead