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Dynamically Adaptive Numerical Weather Prediction

Dynamically Adaptive Numerical Weather Prediction. Kelvin K. Droegemeier and Yunheng Wang Center for Analysis and Prediction of Storms and School of Meteorology University of Oklahoma 19 th Conference on Numerical Weather Prediction 4 June 2009.

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Dynamically Adaptive Numerical Weather Prediction

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  1. Dynamically Adaptive Numerical Weather Prediction Kelvin K. Droegemeier and Yunheng WangCenter for Analysis and Prediction of Storms and School of MeteorologyUniversity of Oklahoma19th Conference on Numerical Weather Prediction4 June 2009

  2. Hero Experimental Forecast(1 km grid spacing WRF, radar data assimilated) Why Use the Same Grid Spacing Everywhere?

  3. We Know How to Automatically and Continuously Nest Grids Skamarock et al. (1994)

  4. But Suppose this is NWP, not Simulation! QUESTION Given a fixed amount of computing resource (e.g., total CPU time in a partition of nodes) What choice of model parameters (e.g., grid spacing, domain size, number of nested grids, physics options) produces the “best” forecast for the situation at hand?

  5. Motivation: Operational NWP is STATIC Observing Systems Do Not Sample the Atmosphere When/Where Needs are Greatest Forecast Models Run Largely on Fixed Schedules in Fixed Domains Cyberinfrastructure is Virtually Static

  6. But Weather is DYNAMIC: Local, High-Impact, Heterogeneous and Rapidly Evolving Rain and Snow Fog Rain and Snow Snow and Freezing Rain Intense Turbulence Severe Thunderstorms

  7. A Fundamental Research Question • Can we produce more accurate forecasts if we adapt our technologies and approaches to the weather as it occurs? • People, even animals adapt/respond: Why don’t our resources???

  8. Sponsored by the National Science Foundation

  9. Making it Happen In a User-Centered Framework Where Everything Can Mutually Interact • Adaptive weather tools • Adaptive sensors • Adaptive cyberinfrastructure

  10. Sample Problem Scenario in Adaptation Storms Forming Forecast Model Data Mining On-Demand Grid Computing Streaming Observations

  11. LEAD Portal Home Page http://portal.leadproject.org

  12. Yes, Adaptive Radars (CASA)

  13. Some Questions in Adaptive Systems • When is adaptation useful and can the costs and benefits of adaptation be quantified? • What types of adaptation are possible and most effective and how can they be chosen and combined? • How is adaptivity triggered/controlled? • How can one deal with loss of resources or less than ideal availability to achieve the required adaptation? • What negative consequences exist to adaptation? • In the context of NWP, how might adaptation impact predictability? • More in the extended abstract…

  14. QUESTION Given a fixed amount of computing resource (e.g., total CPU time in a partition of nodes) What choice of model parameters (e.g., grid spacing, domain size, number of nested grids, physics options) produces the “best” forecast for the situation at hand?

  15. Experiment Design • Idealized isolated supercell simulation using WRF 3.0 • Warm-rain microphysics, no terrain, radiation or surface physics • 3-hour, 5 km baseline or pseudo-operational forecast • 3-hour, 250 m “nature” run • Fixed amount of computing time for key experiments • Vary only the domain size and grid spacing of a single two-way grid nested within the 5 km forecast • Nested grids are all launched at t = 0 • Use mean square error ( = phase + amplitude error) to assess forecast quality relative to “nature” run

  16. Domain A (1000 x 1000 km) Baseline Run (5 km) Nature Runs (250 m)

  17. Baseline/Pseudo-Operational ForecastRainwater Mixing Ratio at 3 Hours (2 km Altitude)

  18. Domain A (1000 x 1000 km) (Baseline and Nature Runs) Domain B (690 x 690 km) Domain C (280 x 280 km) Domain D (180 x 180 km) Domain E (90 x 90 km)

  19. Computer Time (s)

  20. Outer (5 km) Domain MSE at 3 Hours Surface Rainwater Mixing Ratio (g/kg) [Baseline = 0.025 g/kg] Vertical Velocity (m/s) at 4 km Altitude [Baseline = 0.31 m/s]

  21. Total Nested Domain MSE at 3 Hours Surface Rainwater Mixing Ratio (g/kg) Vertical Velocity (m/s) at 4 km Altitude

  22. Domain D (180 x 180 km)

  23. Smallest Nested Domain MSE at 3 Hours Surface Rainwater Mixing Ratio (g/kg) Vertical Velocity (m/s) at 4 km Altitude

  24. Summary • Phase error was dominant relative to amplitude error • Nesting improves the total error of the baseline (5 km) forecast • For a fixed amount of computing time, total error across an entire nested grid is a minimum for the largest domain and coarsest grid spacing (or smallest nesting ratio) • An “optimal” nesting ratio was evident in some variables but not for others • Substantially large and expensive nested domains at fine grid spacing do not necessarily yield the “best” forecast

  25. Future Work • Results reported here were based on a VERY simple experiment  important to establish baseline results • Ongoing experiments are more realistic • Greater aerial coverage of storms • Real data cases • Multiple nested grids launched at various times and repositioned • Other computational constraints (e.g., turnaround time) • Other adaptation strategies (ensembles, rapid updates in time) • More sophisticated error and value measures • Additional testing as part of the NOAA Hazardous Weather Test Bed

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