Forecast development at the IRI Michael K. Tippett.
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Transcript of Forecast development at the IRI Michael K. Tippett.
Forecast development at the IRI
• Michael K. Tippett
Our approachVisionValues
Past, present and future strategies
Outline
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
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?
Forecast verification
Forecast inputs/ingredientsForecast modelsObservational data
ProductsCategorical probabilitiesPDFs
MethodologiesCombination/calibration of forecast inputsProduct delivery
Strategic elements
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
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
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)
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
Flexible forecast format maproom
Forecast inputsMore coupled modelsNMME
ProductsAdditional quantities, time-scalesLeverage emerging research
MethodologiesMore agile, able to adapt to changing inputsLeverage emerging research
Future
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