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Ming Ji NOAA/OGP

NOAA Intra-Seasonal-to-Interannual Prediction Program Toward an Integrated, Requirement Based, and Products Driven R&D Program. Ming Ji NOAA/OGP. ISIP Requirements. NOAA Strategic Plans Improve long-range climate, weather and water predictions CCSP deliverables addressed by NOAA ISIP

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Ming Ji NOAA/OGP

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  1. NOAA Intra-Seasonal-to-Interannual Prediction ProgramToward an Integrated, Requirement Based, and Products Driven R&D Program Ming Ji NOAA/OGP

  2. ISIP Requirements • NOAA Strategic Plans • Improve long-range climate, weather and water predictions • CCSP deliverables addressed by NOAA ISIP • Climate Variability and Change • Global Water Cycle • ISIP Users and Customers • Operational Climate Prediction Centers • Climate and Water Service Providers • Place based R&D Applications (e.g., RISA) • Fed. Gov. - Improve f’cst skill and information content for policy and decision making

  3. ISIP Goals • Provide the Nation with a seamless suite of climate forecasts and application products to manage risks and opportunities of climate impacts due to intra-seasonal to interannual climate variations • The “Seamless Suite” of NWS forecast guidance (week-2 to interannual) • Multi-model ensemble based forecast system(s) • Regional/sectoral application products for decision support (resource and risk management) • International predictions, assessments and applications

  4. Seamless Suite of Forecasts Forecast Uncertainty Outlook Years Seasons Guidance Months Threats Assessments 2 Week Forecast Lead Time 1 Week Forecasts Days Watches Hours Warnings & Alert Coordination Minutes Benefits State/Local Planning Energy Health Reservoir Control Space Operation Agriculture Recreation Commerce Ecosystem Hydropower Protection of Life & Property Environment Fire Weather Flood Mitigation & Navigation Transportation Boundary Conditions Initial Conditions

  5. Major fires Agricultural production at 50%, blowing dust Health warning: Limit outdoor activities; expect brownouts major fisheries regime change likely Air quality alerts – 75% of days Swimming and Fishing prohibited Frequent floodings and Asian dust threats continue High danger of toxic CO2 releases Expect fisheries downturn; health threats African bacteria alerts New environmental forecast products will be feasible Possible Threats-Summer 2020: hot, dry and unhealthy

  6. Integrated ISIP Program Structure • Model Development (High-end centers: EMC, GFDL, GMAO, NCAR) • Experimental Prediction (Incl. Multi-model Ens. Fcst System Development) • Applications and Products Development • Research (e.g., process and predictability studies, diagnostics, data analysis, attribution, model development)

  7. NCEP/EMC

  8. Program Structure (2) • Model Development (Requirement for Environmental Modeling Program) • Experimental Prediction (Incl. Multi-model Forecast System Development) • Applications and Products Development • Research (e.g., process and predictability studies, diagnostics, data analysis, attribution, model development)

  9. IRI Example: Dynamical Multi-model Forecast System ATMOSPHERE SST HISTORICAL DATA Extended simulations Observations GLOBAL ATMOSPHERIC MODELS ECHAM4.5(MPI) NCEP (MRF9) CCM3.2(NCAR) NSIPP(NASA) COLA2.x PERSISTED GLOBAL SST Persisted SST Ensembles 3 Mo. lead POST PROCESSING -Multimodel Ensembling FORECAST SST TROP. PACIFIC (NCEP dynamical) TROP. ATL, INDIAN (statistical) EXTRATROPICAL (damped persistence) Forecast SST Ensembles 3/6 Mo. lead REGIONAL MODELS AGCM INITIAL CONDITIONS UPDATED ENSEMBLES (10+) WITH OBSERVED SST

  10. Extreme precipitation events associated with winter storms PDF of storms formed in the Gulf of Mexico (1949-1998) Red - El Nino winters Blue - La Nina winters Dotted - observations (Higgins et al) Solid - NSIPP model (9 member ensemble) Extreme values of the precipitation EOF corresponding to storms that form in the Gulf of Mexico during DJF. Extreme values are fit to the Gumbel Distribution based on daily winter maxima for the period 1949-1998. The abscissa is the maximum value of the associated principal component for a given winter. The model is seeing the same separation in extreme events in El Niño and La Niña years as nature

  11. Program Structure (4) • Model Development • Multi-model f’cst system and experimental prediction • Application Products Development • Research (CPPA) Integrated PACSGAPP research program • Process studies (e.g., NAME) • Regional Reanalysis and LDAS products • Routine attribution/forecast discussions with CPC • Land and hydrology model development for climate prediction and water resource applications • Climate model diagnostics consortium • ODASI consortium • Regional modeling consortium

  12. PDO ENSO Land/Soil AO/NAO Monsoons Patterns of Natural Climate Variability and Source of Predictability TAV Land/Soil

  13. Drought causes - Drought and climate variability Are the tropical oceans playing an important role? Evolution of tropical Pacific Sea Surface Temperatures Two key features: • Prolonged La Niña conditions 1998-2002 • Long-term warming of Indian Ocean-west Pacific “warm pool” 1998 El Niño La Niña onset 1999 2000 2001 2002 La Niña ends

  14. Challenges • Build an integrated ISIP R&D program • ISIP workshop (Aug. 2003) • Integrate science communities • Integration of PACS/GAPP - CPPA • Two-way communication between producers and users • ISIP workshop (Aug. 2003) • Support integrated climate obs. system • See ISIP products • Performance measures

  15. ISIP Products by Category • National Guidance Products • Attribution on all time scales • Nowcasting of Trends • Subseasonal probabilistic forecast of extremes • Global and regional reanalysis products • Best practice for climate forecasts • Regional/sectoral Application Products • Water resource availability • Fire weather • Agriculture applications • River forecasts, costal runoff • Other • Trainings and workshops • International applications (place based) • Information briefings for Fed. Gov. (White House, Congress) • Requirements for climate obs. system

  16. ISIP Products (FY03/04 and beyond) • Operational guidance • Realistic skill score – Estimate the limit of predictability • Operational ensemble subseasonal forecasts • Operational coupled model forecast sys. (NCEP) • Multi-model ensemble forecasts • Regional/sectoral applications • Agriculture (e.g., Freezing days) • Fire Weather • Drought forecast/LDAS • Hydrological f’cst (wk-2 to seasonal) • Other • NAME/NAME CPT (CLIVAR) • Diurnal cycle/conv. precip. over land • Information briefings (on demand), workshops, trainings • Regional Reanalysis

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