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Explore the progress and remaining challenges in addressing wind forecast biases using the WRF model, focusing on key parameters like wind speed and direction. Discover the latest research findings and solutions to optimize wind predictions for more accurate weather forecasting applications.
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WRF Model Physics: Problems and Progress Cliff Mass and Dave Ovens University of Washington
For Several Basic Parameters We Have Made Substantial Progress During the Past Ten Years With the Transition from MM5 to WRF and Physics Improvements
MM5 PrecipBias for24-h90% and 160% lines are contoured with dashed and solid lines For entire Winter season
But some parameters are still a problem: wind speed and wind direction • WRF generally has a substantial overprediction bias, with winds being too geostrophic. • Not enough contrast between winds over land and water. • This problem is evident virtually everywhere
Dealing With Surface Wind Biases • A consistent error in WRF, both here in the Northwest and elsewhere, is the tendency for: • a positive wind speed bias • winds that are excessively geostrophic. • Also noticed that there was insufficient contrast between winds over the water and land (land winds too large). • A number of examples were discussed at the NW Weather Workshop and the last consortium meeting.
Dealing with surface wind biases • Last year we experimented with all available planetary boundary layer schemes (including a number of new ones) and also tried varying the number of vertical levels. • None solved this problem. • Earlier this year we started running at 1.3 km grid spacing over western WA and the problem seems to get much better.
Dealing with Wind Biases • This led to a hypothesis that the problem is that the model is not resolving subgrid scale roughness elements at the surface at 12 and even 4-km resolution. • Early experiments in increasing u*, which is related to surface drag were very suggestive—it decreased the wind and directional biases significantly. • This was good enough that we added it to the real-time system on April 14th.
Optimizing the Approach • An alternative, and perhaps more straightforward, way of doing the same thing is to increase the surface roughness length (z0), and others have played with this approach (like NCEP, who has never published anything on it). • Following the hypothesis, it made sense to make the increase in roughness dependent on the variance of the subgrid scale terrain. • More variance of terrain—more roughness.
New Surface Drag Approach • During the past few months we have completed an extensive series of experiments (view them at: http://www.atmos.washington.edu/~ovens/windbias/) with various surface drag approaches. • Narrowing this down substantially, but here is one of the best, with z0 dependent on surface terrain variance over land using 1-km terrain data base.
LSM Change • The Noah LSM in the WRF 3.1.1 and 3.2 codes has a strong cold bias in max temp over the elevated terrain of the Intermountain West. • Turning off the Noah LSM and switching to the simpler 5-layer thermal diffusion scheme (as was used in our MM5 runs) improves the surface and 2-m temperatures greatly. • This change will, however, introduce about a 1°F higher dewpoint temperature bias.
