Presentation Description: To accelerate offshore wind development and reduce risk for the industry, TGS has deployed five floating LiDAR buoys along the US East Coast. These buoys are sited in the primary wind development zones: one in Massachusetts, two in the New York Bight, and two in the Central Atlantic. Together, this collection of observations creates a foundational data set for understanding the wind resource and metocean conditions in the region.
In this presentation, we share results from the buoys and quantify the wind resource and metocean conditions across the US East Coast. In particular, the TGS Central Atlantic buoys are the first measurements sited within the call areas that will be awarded to developers in BOEM’s upcoming lease auction. Our analysis will highlight how the Central Atlantic call areas compare to each other, focusing on which areas appear most attractive for offshore wind development. We will also contrast measurements from the Central Atlantic to other lease primary offshore wind development areas that are better known and measured, thus providing useful context for expected performance risk. Lastly, we will evaluate the performance of 1km resolution Numerical Weather Prediction model simulations against these floating LiDAR measurements to assess the skill of the NWP model and highlight how these new measurements enhance our understanding of the wind resource and metocean conditions along the US East Coast.
Learning Objectives:
Discover how new measurements of the wind resource and metocean conditions help us identify the best call areas within the Central Atlantic region.
Understand how measured conditions within the Central Atlantic call areas compare to the New York Bight and Massachusetts lease areas. Are wind speeds stronger or weaker than prior BOEM auction areas? How different are the metocean conditions (wave heights / currents) in the Central Atlantic?
Demonstrate the skill level of NWP model results across the US East Coast using these measurements. Where do the models perform best? Where should we assign higher uncertainty?