Analyzing Self-Organizing Maps of Modeled U.S. Coastal Wind Regimes with a Comparison to Observations

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Abstract Wind offshore of the northeastern United States is a vast and plentiful resource. However, wind variability needs accounting for when planning, installing, and operating offshore wind farms. Therefore, increased knowledge of four general areas becomes vital: 1) common coastal wind regimes and their impact on wind energy production, 2) common regime transitions, 3) how near-surface wind shear varies between wind shear regimes, and 4) whether numerical forecast model skill is regime dependent. A self-organizing map (SOM) clusters hub-height (80-m) wind data from the High-Resolution Rapid Refresh (HRRR) model covering the northeast coast to address areas 1–3. The SOM identifies three general wind pattern types: unidirectional flow, confluent/diffluent flow, and cyclonic/anticyclonic flow. The strongest mean HRRR wind speeds offshore of New York are associated with a low-pressure system near Maine (12 m s−1) and wintertime-springtime westerlies (11 m s−1) while the weakest winds are associated with a nearby high (≤ 3 m s−1) and a diffluence zone (4 m s−1). Using a separate SOM trained on 10–80-m wind differences, warm air advection over cooler northern waters typically leads to lower-level stabilization, and thus increased shear. Regarding 4), modeled winds are compared to buoy lidar observations for each SOM regime. As in observations, HRRR monthly-averaged wind speed decreased in the summer. HRRR generally under-forecasts wind speed near the buoys, although monthly-averaged bias decreased over two years from 1.4 m s−1 to 0.1–0.2 m s−1. Greater bias occurred for regimes representing nearby pressure systems, indicating HRRR skill can be regime-dependent.