Prediction of local wind climatology from Met Office models: Virtual Met Mast techniques

Standen, J., Wilson, C., Vosper, S. and Clark, P. ORCID: https://orcid.org/0000-0003-1001-9226 (2017) Prediction of local wind climatology from Met Office models: Virtual Met Mast techniques. Wind Energy, 20 (3). pp. 411-430. ISSN 1099-1824 doi: 10.1002/we.2013

Abstract/Summary

The Met Office has developed the Virtual Met Mast™ (VMM) tool for assessing the feasibility of potential wind farm sites. It provides site-specific climatological wind information for both onshore and offshore locations. The VMM relies on existing data from past forecasts from regional-scale numerical weather prediction (NWP) models, to which corrections are applied to account for local site complexity. The techniques include corrections to account for the enhanced roughness lengths used in NWP models to represent drag due to sub-grid orography, and downscaling methods which predict local wind acceleration over small-scale terrain. The corrected NWP data are extended to cover long periods (decades) using a technique in which the data are related to alternative long-term datasets. For locations in the UK the VMM currently relies on operational mesoscale model forecast data at 4km horizontal resolution. Predictions have been verified against observations made at typical wind turbine hub heights at over 80 sites across the UK. In general the predictions compare well with the observations. The techniques provide an efficient method for screening potential wind resource sites. Examples of how the VMM techniques can be used to produce local wind maps are also presented.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/66218
Identification Number/DOI	10.1002/we.2013
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Uncontrolled Keywords	wind resource assessment; wind energy; numerical weather prediction; mesoscale modelling; wind climatology; UK
Publisher	Wiley
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