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Atmosphere 2017, 8(1), 24; doi:10.3390/atmos8010024

Mountain Waves in High Resolution Forecast Models: Automated Diagnostics of Wave Severity and Impact on Surface Winds

Met Office, FitzRoy Rd., Exeter EX1 3PB, UK
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Academic Editor: Vanda Grubišić
Received: 30 November 2016 / Revised: 6 January 2017 / Accepted: 16 January 2017 / Published: 23 January 2017
(This article belongs to the Special Issue Atmospheric Gravity Waves)
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Abstract

An automated method producing a diagnostic of the severity of lee waves and their impacts on surface winds as represented in output from a high resolution linear numerical model (3D velocities over mountains (3DVOM)) covering several areas of the U.K. is discussed. Lee waves involving turbulent rotor activity or downslope windstorms represent a hazard to aviation and ground transport, and summary information of this kind is highly valuable as an efficient ‘heads-up’ for forecasters, for automated products or to feed into impact models. Automated diagnosis of lee wave surface effects presents a particular challenge due to the complexity of turbulent zones in the lee of irregular terrain. The method proposed quantifies modelled wind perturbations relative to those that would occur in the absence of lee waves for a given background wind, and diagnoses using it are found to be quite consistent between cases and for different ranges of U.K. hills. A recent upgrade of the operational U.K. limited area model, the U.K. Variable Resolution Model (UKV) used for general forecasting at the Met Office means that it now resolves lee waves, and its performance is here demonstrated using comparisons with aircraft- and surface-based observations and the linear model. In the future, automated diagnostics may be adapted to use its output to routinely produce contiguous mesoscale maps of lee wave activity and surface impacts over the whole U.K. View Full-Text
Keywords: lee waves; high resolution; numerical weather prediction; turbulence; rotor; aviation; diagnostic; automation lee waves; high resolution; numerical weather prediction; turbulence; rotor; aviation; diagnostic; automation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Sheridan, P.; Vosper, S.; Brown, P. Mountain Waves in High Resolution Forecast Models: Automated Diagnostics of Wave Severity and Impact on Surface Winds. Atmosphere 2017, 8, 24.

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