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Improving WRF Wind Prediction with Subgrid Scale Drag Parameterization

Explore the issue of high-speed wind bias in the WRF model, proposing a new subgrid scale drag parameterization to address the overprediction bias in moderate to high winds. Detailed verification studies reveal the need for more contrast between land and water winds. The efficacy of different parameterization schemes and the impact on model performance are analyzed through multi-month evaluations. Proposed approach shows promise in reducing bias but requires further optimization for strong wind speeds and stability conditions.

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Improving WRF Wind Prediction with Subgrid Scale Drag Parameterization

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  1. Fixing WRF’s High Speed Wind Bias: A New Subgrid Scale Drag Parameterization and the Role of Detailed Verification Cliff Mass and Dave Ovens University of Washington

  2. Problems with WRF winds • WRF generally has a substantial overprediction bias for all but the lightest winds. • Not enough light winds. • Winds are generally too geostrophic over land. • Not enough contrast between winds over land and water. • This problem is evident virtually everywhere and appears to occur in all PBL schemes available with WRF.

  3. 10-m wind bias, 00 UTC, 24-h forecast, Jan 1-Feb 8, 2010

  4. 10-m wind bias, 12 UTC, 12-h forecast, Jan 1-Feb 8, 2010

  5. The Problem

  6. Insufficient Contrast Between Land and Water

  7. So What is the Problem? • As noted earlier, tried all available WRF PBL schemes…no magic bullet there. We are using the YSU scheme in most work. • Doesn’t improve going from 36 to 12 km resolution, 1.3 km somewhat better. • Inherent problem with all PBL schemes? • What about the roughness of subgrid terrain that we are not resolving?

  8. The 12-km grid versus terrain

  9. A new drag surface drag parameterization • Determine the subgrid terrain variance and make surface drag or roughness used in model dependent on it. • Consulting with Jimy Dudhia of NCAR came up with an approach—enhancing u* and only in the boundary layer scheme (YSU). • For our 12-km and 36-km runs used the variance of 1-km grid spacing terrain.

  10. 38 Different Experiments: Multi-month evaluation winter and summer

  11. Some Results for Experiment “71” • Ran the modeling system over a five-week test period (Jan 1- Feb 8, 2010)

  12. 10-m wind speed bias: Winter

  13. MAE 10m wind speed

  14. Case Study

  15. Old New

  16. An Issue • Our method appears to hurt slightly during strong wind speeds and near maximum temperatures in summer.

  17. Summer-0000 TC-Original

  18. With Sub-grid drag

  19. Summer

  20. Improvement? • Next step—could have the parameterizaton fade out for higher winds speeds and lower stability, possibility by depending on Richardson number. • Actually, this makes some sense…sometimes the atmosphere is well-mixed, and at these times variations in sub-grid roughness would be less important.

  21. The End

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