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    To justify an offshore wind project’s economic viability, an accurate preconstruction energy yield estimate is required. Unfortunately, the behavior of the wind in a marine/coastal environment is complex, and often not well measured, modeled, nor understood; thus significant preconstruction energy yield uncertainties may be introduced when estimating a local wind resource and a turbine’s available power. In part, such uncertainties contribute to the chronic industry challenge known as wind farm underperformance bias, in which operational energy yield is less than preconstruction expected energy yield. The consequence of underperformance bias is noteworthy, as an inaccurate expectation of available wind and turbine power may cause sub-optimal wind farm layouts, thus further delay the offshore wind cost-competitiveness. Hampton University Lidar Lab focus on research that reduces atmospheric-related energy yield uncertainties that contribute to sub-optimal offshore wind farm performance. To address these uncertainties research is divided into 4 themes: “Research-2-Operation” (R2O), “Measurements-2-Models” (M2M), and “Doppler Lidar Uncertainty” (DLU). The R2O team collects high spatial and temporal resolution atmospheric measurements offshore to develop methodologies that more accurately characterize the wind resource and available turbine power. The goal is to elucidate how unique coastal meteorological conditions influence offshore turbine performance. The M2M group works closely with R2O, using measurements to validate and improve model estimates of the offshore wind resource. Finally, DLU works to quantify Doppler wind lidar measurement uncertainty and provide insight on appropriate measurement strategies in an offshore environment.