Downscaling surface wind predictions from numerical weather prediction  models in complex terrain with WindNinja

Wagenbrenner, Natalie S.; Forthofer, Jason M.; Lamb, Brian K.; Shannon, Kyle S.; Butler, Bret W.

doi:https://doi.org/10.5194/acp-16-5229-2016

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.

https://doi.org/10.5194/acp-16-5229-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 16, issue 8

Research article

|

27 Apr 2016

Research article |

| 27 Apr 2016

Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja

Natalie S. Wagenbrenner, Jason M. Forthofer, Brian K. Lamb, Kyle S. Shannon, and Bret W. Butler

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Final revised paper (published on 27 Apr 2016)
Preprint (discussion started on 18 Jan 2016)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Minor revisions', Anonymous Referee #1, 21 Feb 2016
- AC1: 'Reply to Referee #1', Natalie Wagenbrenner, 14 Mar 2016
RC2: 'Review', Anonymous Referee #2, 23 Feb 2016
- AC2: 'Reply to Referee #2', Natalie Wagenbrenner, 14 Mar 2016

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Natalie Wagenbrenner on behalf of the Authors (26 Mar 2016) Author's response Manuscript

ED: Reconsider after major revisions (10 Apr 2016) by Timothy Garrett

AR by Natalie Wagenbrenner on behalf of the Authors (10 Apr 2016) Author's response Manuscript

ED: Publish as is (14 Apr 2016) by Timothy Garrett

AR by Natalie Wagenbrenner on behalf of the Authors (16 Apr 2016) Author's response Manuscript

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.