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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- Pyros: a raster–vector spatial simulation model for predicting wildland surface fire spread and growth D. Voltolina et al. 10.1071/WF22142
- Performance of operational fire spread models in California A. Cardil et al. 10.1071/WF22128
- Comparison of Spring Wind Gusts in the Eastern Part of the Tibetan Plateau and along the Coast: The Role of Turbulence X. Zhou et al. 10.3390/rs15143655
- The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.1) model enables fast dynamic downscaling to the hectometer scale D. Reynolds et al. 10.5194/gmd-16-5049-2023
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- A fast-response model of turbulence and passive scalar transport in row-organized canopies L. Ulmer et al. 10.1016/j.agrformet.2024.109919
- An Evaluation of Advanced Tools for Distributed Wind Turbine Performance Estimation R. Poudel et al. 10.1088/1742-6596/1452/1/012017
- A data-driven multi-model methodology with deep feature selection for short-term wind forecasting C. Feng et al. 10.1016/j.apenergy.2017.01.043
- A Data-Driven Fire Spread Simulator: Validation in Vall-llobrega's Fire O. Rios et al. 10.3389/fmech.2019.00008
- Efficient Simulation of Temperature Evolution of Overhead Transmission Lines Based on Analytical Solution and NWP R. Yao et al. 10.1109/TPWRD.2017.2751563
- Merging computational fluid dynamics and machine learning to reveal animal migration strategies S. Olivetti et al. 10.1111/2041-210X.13604
- Research challenges and needs for the deployment of wind energy in hilly and mountainous regions A. Clifton et al. 10.5194/wes-7-2231-2022
- Performance improvements to modern hydrological models via lookup table optimizations C. Marsh et al. 10.1016/j.envsoft.2021.105018
- Downscaling of surface wind forecasts using convolutional neural networks F. Dupuy et al. 10.5194/npg-30-553-2023
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Latest update: 21 Nov 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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