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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. Problems with WRF winds. WRF generally has a substantial overprediction bias for all but the lightest winds.
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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
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.
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?
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.
38 Different Experiments: Multi-month evaluation winter and summer
Some Results for Experiment “71” • Ran the modeling system over a five-week test period (Jan 1- Feb 8, 2010)
Old New
An Issue • Our method appears to hurt slightly during strong wind speeds and near maximum temperatures in summer.
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.