Articles | Volume 16, issue 8
https://doi.org/10.5194/acp-16-5229-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-16-5229-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja
Natalie S. Wagenbrenner
CORRESPONDING AUTHOR
US Forest Service, Rocky Mountain Research Station,
Missoula Fire Sciences Laboratory, 5775 W Highway 10, Missoula, MT 59808,
USA
Jason M. Forthofer
US Forest Service, Rocky Mountain Research Station,
Missoula Fire Sciences Laboratory, 5775 W Highway 10, Missoula, MT 59808,
USA
Brian K. Lamb
Laboratory for Atmospheric Research, Department of Civil
and Environmental Engineering, Washington State University, Pullman, WA
99164, USA
Kyle S. Shannon
US Forest Service, Rocky Mountain Research Station,
Missoula Fire Sciences Laboratory, 5775 W Highway 10, Missoula, MT 59808,
USA
Bret W. Butler
US Forest Service, Rocky Mountain Research Station,
Missoula Fire Sciences Laboratory, 5775 W Highway 10, Missoula, MT 59808,
USA
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Latest update: 13 Dec 2024
Short summary
We investigated the ability of WindNinja to improve wind predictions in complex terrain. Predictions are compared with surface observations from a tall, isolated mountain. Results show that WindNinja is capable of capturing important local-scale flow features induced by mechanical and thermal effects of the underlying terrain and incorporating those terrain-driven flow features into coarse-scale weather forecasts in order to improve near-surface wind predictions in complex terrain.
We investigated the ability of WindNinja to improve wind predictions in complex terrain....
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