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 Dynamically Adaptive Numerical Weather Prediction

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

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

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?

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 Motivation: Operational NWP is STATIC

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

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

Sponsored by the National Science Foundation

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

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

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Yes, Adaptive Radars (CASA) Yes, Adaptive Radars (CASA)

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

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?

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

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

Baseline/Pseudo-Operational Forecast Rainwater Mixing Ratio at 3 Hours (2 km Altitude)

Nest Ratio (  x) Domain Baseline (  x = 5 km) 3 (  x = 1.67 km) 5 (  x = 1 km) 7 (  x= 714 m) 9 (  x = 555 m ) A (1000 x 1000 km 2 ) 840 s B (690 x 690 km 2 ) C (280 x 280 km 2 ) D (180 x 180 km 2 ) E (90 x 90 km 2 ) Computer Time (s)

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)

Nest Ratio (  x) Domain Baseline (  x = 5 km) 3 (  x = 1.67 km) 5 (  x = 1 km) 7 (  x= 714 m) 9 (  x = 555 m ) A (1000 x 1000 km 2 ) 840 s B (690 x 690 km 2 ) 5333 s C (280 x 280 km 2 ) 5306 s D (180 x 180 km 2 ) 5359 s E (90 x 90 km 2 ) 5240 s Computer Time (s)

Nest Ratio (  x) Domain Baseline (  x = 5 km) 3 (  x = 1.67 km) 5 (  x = 1 km) 7 (  x= 714 m) 9 (  x = 555 m ) A (1000 x 1000 km 2 ) 840 s B (690 x 690 km 2 ) 5333 s C (280 x 280 km 2 ) 5306 s D (180 x 180 km 2 ) 5359 s E (90 x 90 km 2 ) 5240 s

Nest Ratio (  x) Domain Baseline (  x = 5 km) 3 (  x = 1.67 km) 5 (  x = 1 km) 7 (  x= 714 m) 9 (  x = 555 m ) A (1000 x 1000 km 2 ) 840 s B (690 x 690 km 2 ) 5333 s23,517 s46,766 s60,416 s C (280 x 280 km 2 ) 1799 s5306 s10,565 s31,387 s D (180 x 180 km 2 ) 1440 s2957 s5359 s14,752 s E (90 x 90 km 2 ) 1177 s1520 s2269 s5240 s Computer Time (s)

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

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

Domain D (180 x 180 km)

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

Summary Summary n Phase error was dominant relative to amplitude error n Nesting improves the total error of the baseline (5 km) forecast n 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) n An “optimal” nesting ratio was evident in some variables but not for others n Substantially large and expensive nested domains at fine grid spacing do not necessarily yield the “best” forecast

Future Work Future Work n Results reported here were based on a VERY simple experiment  important to establish baseline results n 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