TGS Expands its Data Coverage for Offshore Wind

4C Offshore | Tom Russell
By: 25/08/2021 TGS
TGS, a global provider of energy data and intelligence, has expanded its coverage of numerical weather prediction (NWP) model data. The latest model covers a 400,000 square kilometers area off the East Coast US, extending from Massachusetts to North Carolina. It was selected to inform and enhance wind resource assessment in the New York Bight Proposed Sale Notice areas.

This wind energy model has been produced in collaboration with Vaisala, a global leader in weather, environmental, and industrial measurements, to create a higher resolution dataset than publicly available with coverage over the key offshore wind industry focus areas in the US coast.

These model results have been validated by publicly available measurements, including but not limited to Floating Lidar data, to increase confidence and improve data quality compared to other industry datasets. It will be further enhanced with proprietary measurements later this year.

Wind developers and other industry stakeholders can compare their assessment of New York Bight areas with wind conditions over existing leases in their portfolio using this expansive high-resolution volume. This capability facilitates a baseline for future validation and post-award analysis.

TGS will now include both US and Scotwind NWP models into a comprehensive atlas of public and proprietary wind data resources. Combined with TGS subsidiary 4COffshore’s market intelligence database, this provides a powerful resource enabling rapid, accurate evaluation of available data in existing leases, especially for future auctions.

Katja Akentieva, VP New Energy Solutions for Western Hemisphere at TGS, observes, “We are pleased to provide unique solutions and insights to the fast-growing offshore wind industry in the US and beyond, enabling our customers to reduce their commercial risk and improve their understanding of the energy potential in each lease block. Initial results reveal some interesting variations, spatial and temporal, which may impact various components of the wind farm design, construction and energy output.”