Elements of a Science Infusion Strategy for Hydrologic Ensemble Prediction

1. Elements of a Science Infusion Strategy for Hydrologic Ensemble Prediction John Schaake, Pedro Restrepo and D.J. Seo NCEP Seminar March 15, 2005

2. NOAA’s Strategic Goals(2005 – 2010 Strategic Plan) • Understand climate variabilityand change to enhance society’s ability to plan and respond • Serve society’s needs for weather and water information

3. NOAA’sClimate Mission Outcomes • A predictive understanding of the global climate system on the scales of weeks to decades with quantified uncertainties sufficient for making informed and reasoned decisions • Climate-sensitive sectors and the climate-literate public effectively incorporating NOAA’s climate products into their plans and decisions

4. NOAA’s Weather and Water Mission Outcomes • Reduced loss of life, injury and damage to the economy • Better, quicker and more valuableweather and water information to support improved decisions • Increased customer satisfaction with weather and water information and services

5. Predictions and Projections Objectives (NOAA Climate Program Plan FY 07 - 11: C. Koblinsky) Develop a predictive understanding of the global climate system on timescales of weeks to decades with quantified uncertainties sufficient for making informed decisions - Improve intraseasonal and interannual climate predictions to enable regional and national managers to better plan for the impacts of climate variability and change - Provide improved regional, national, and international climate assessments and projections to support policy decisions with objective information. Desired End-State: A seamless suite of forecasts (e.g. outlooks and projections) on intraseasonal, seasonal, interannual, and multi-decadal timescales and applications using ensembles of multiple climate models in support of the mission outcome “a predictive understanding of the global climate system”: FY07: Provide regional resolution forecasts to decision makers through increased computer and model capacity. FY11: Provide a broader suite of climate forecast products and services through development of Earth System Model.

6. What is Needed to Meet NOAA’s Strategic Goals Weather and Climate Forecasts Hydrologic Ensemble Prediction System Observations Products and Services

7. Elements of a Hydrologic Ensemble Prediction System Weather and Climate Forecasts Single-value and ensemble forecasts Product Properties Emerging Capabilities Reliable hydrologic products Products and Services

8. Elements of a Hydrologic Ensemble Prediction System Weather and Climate Forecasts Single-value and ensemble forecasts Atmospheric Ensemble Pre-Processor Reliable hydrologic inputs Hydrologic Requirements AHPS PreProcessor Science Strategy Reliable hydrologic products Products and Services

9. Elements of a Hydrologic Ensemble Prediction System Weather and Climate Forecasts Single-value and ensemble forecasts Reliable hydrologic inputs Hydrologic Ensemble Processor Hydrological Models (& Regulation) Land Data Assimilator Obs Ensemble forecasts Ensemble initial conditions Automatic Data Assimilation is Essential Requirement of this System Reliable hydrologic products Products and Services

10. Elements of a Hydrologic Ensemble Prediction System Weather and Climate Forecasts Single-value and ensemble forecasts Ensemble forecasts Product Generator Reliable hydrologic products Products and Services

11. Elements of a Hydrologic Ensemble Prediction System Weather and Climate Forecasts Single-value and ensemble forecasts Atmospheric Ensemble Pre-Processor Reliable hydrologic inputs Land Data Assimilator Hydrologic Ensemble Processor Hydrological Models Obs Ensemble forecasts Ensemble initial conditions Product Generator Integration Reliable hydrologic products Products and Services

12. Verification • Must be able to measure performance of every element in the system • Need probabilistic measures • Must be useful to forecasters and model developers

13. HEPEX Goal • HEPEX aims to bring the international hydrological and meteorological communities together todemonstrate how to produce reliable hydrological ensemble forecasts that can be used with confidence bythe emergency management and water resources sectors to make decisions that have important consequences for the economy, for public health and safety.

14. Some HEPEX science questions… What are the requirements of ensemble weather forecast for support of hydrologic prediction? Do meteorological forecast account for important meteorological and climate uncertainties? What is the role of operational forecasters? What are the sources of uncertainty in hydrologic predictions? Kristie Franz

15. Focus of Proposed THORPEX/HEPEX Hydrologic Application Project THORPEX/HEPEX collaboration Feedback from the hydrologic applications community THORPEX ensembles Kristie Franz

16. Thank You

17. Properties of Existing Products • Heterogeniety • Assessment • Model Bias • Skill and reliability of probabilities • Scale dependency Implications Problems Examples Examples Examples Return

18. A lot happens inside a grid box Tom Hammil, CDC Rocky Mountains Approximate size of one grid box in NCEP ensemble system Denver Next Source: accessmaps.com

19. Questions • Can we accurately forecast the evolution of the pdf of the grid-box average weather? (focus mostly on this) • How do we downscale from a grid-box average to a particular river basin or sub-area? Return Tom Hammil, CDC

20. Typical problems with current generation ensemble forecasts • Would like to maximize pdf sharpness subject to calibration. But: • Ensemble forecasts are biased • Ensemble mean different (systematic model error; improve the model or post-process to correct errors) • Ensemble spread less than it ought to be (better initial conditions, higher-res forecasts, incorporating stochastic effects). Tom Hammil, CDC Next

21. Ensemble forecasts: where are we today? • Generating initial conditions: Each center has adopted their own approximate way of sampling from initial condition pdf. • Breeding (NCEP) • Singular vector (ECMWF) • Perturbed observation (Canada) • Stochastic-dynamic ensemble work just beginning (e.g., Buizza et al. 1999) • Many attempts to post-process ensemble forecasts to provide reliable probability forecasts. Tom Hammil, CDC Return

22. Ensemble Temperature Forecast Next Return

23. Ensemble Precipitation Forecast Next Return

24. Ensemble Streamflow Forecast Next Return

25. NWSRFSForecast Input Requirements • Ensemble inputs for ESP • For each RFC sub-basin and time step • For all lead times 1hr to 1yr • Ensemble inputs include: • Precipitation • Temperature • Potential evaporation • Freezing level • Verification Return

26. Emerging Capabilities - Weather and Climate Ensemble Forecast Applications • Medium range global ensembles (~2wks) (NCEP GFS) • Short range regional ensembles (~2days) (NCEP SREF) • Long range coupled ocean – land –atmosphere ensembles (~6mos) (CFS) • Regional climate models • Multi-model ensembles (e.g. TIGGE & IRI) • Future MOS applications Return

27. Immediate AHPS Ensemble Precipitation Goals • Create short & medium term precipitation ensembles for input to hydrologic models at basin scale. • Use existing HPC deterministic forecasts (after modification by RFC HAS forecasters) (i.e. Maintain role of human forecaster for short-term forecasts). Add confidence Factor? • Use GFS fixed ensemble mean forecasts up to 14 days (to extend lead time and improve preprocessor parameters) Next Return

28. AHPS PreProcessorPerformance Objectives • Preserve skill of the single-value forecasts (at all space and time scales) • Remove forecast biases • Produce reliable probabilities • Account for space/time scale dependency • Simple, efficient and robust Next Return

29. Next Return

30. Precipitation Forecasts and ObservationsCalifornia – January Day 1 Return

31. AHPS PreProcessorScience Strategy • Develop basic capability using existing “single-value” forecasts and observations • Apply to specific RFC Sub-basin areas using limited RFC and HPC archives of QPF • Expand to gridded regions – include mult-scale properties • Develop general Bayesian approach to using GFS ensemble forecasts • Other approaches (e.g. analogs)? Next Return

32. Posterior Prior (local climatology) Likelihood function (relates local scale to GCM scale) 1/8th degree scale variable GCM-scale variable Bayesian Merging of Information Bayes Theorem Eric Wood, Princeton Return

33. NCEP Global Ensemble Forecasts Next Return

34. Cumulative Distributions of Adjusted Ensemble Members Return

35. Uncertainty Analysis of Global Ensemble Precipitation Forecasts Return

36. Effect of Spatial Scale on 24hr Forecast Skill (July – 5 locations) Return

37. Ensemble Challenges • Maintain spatial and temporal relationships across very large areas rainy + cold clear + warm snowing cloudy + hot Irrational outcomes Next Return

38. Short-term Ensemble Prototype Smith River Mad River Salmon River Van Duzen River American River (11 basins) Navarro River Next Return

39. Include forecaster skill in short-term inputs (QPF, temperature, etc.) Forecasters add value to short-term QPF. HPC adds value to models RFC adds value to HPC Ensemble Challenges Next Return

40. Include forecaster guidance of hydrologic model operation Hydrologic models require on-going tuning Forecasters commonly adjust or influence raw model output Ensemble Challenges Return