Satellite winds as a tool for offshore wind resource assessment: The Great Lakes Wind Atlas

• We present the first full observational wind atlas for the Great Lakes.
• We describe a method to integrate SAR and QuikSCAT wind speeds.
• We describe a method to integrate satellite and in situ wind observations.
• We present methods for correction of wind speeds for the ice season.
• Wind prediction errors are reduced by integrating different data sets.

This work presents a new observational wind atlas for the Great Lakes, and proposes a methodology to combine in situ and satellite wind observations for offshore wind resource assessment. Efficient wind energy projects rely on accurate wind resource estimates, which are complex to obtain offshore due to the temporal and spatial sparseness of observations, and the potential for temporal data gaps introduced by the formation of ice during winter months, especially in freshwater lakes. For this study, in situ observations from 70 coastal stations and 20 buoys provide diurnal, seasonal, and interannual wind variability information, with time series that range from 3 to 11 years in duration. Remotely-sensed equivalent neutral winds provide spatial information on the wind climate. NASA QuikSCAT winds are temporally consistent at a 25 km resolution. ESA Synthetic Aperture Radar winds are temporally sparse but at a resolution of 500 m. As an initial step, each data set is processed independently to create a map of 90 m wind speeds. Buoy data are corrected for ice season gaps using ratios of the mean and mean cubed of the Weibull distribution, and reference temporally-complete time series from the North American Regional Reanalysis. Generalized wind climates are obtained for each buoy and coastal site with the wind model WAsP, and combined into a single wind speed estimate for the Great Lakes region. The method of classes is used to account for the temporal sparseness in the SAR data set and combine all scenes into one wind speed map. QuikSCAT winds undergo a seasonal correction due to lack of data during the cold season that is based on its ratio relative to buoy time series. All processing steps reduce the biases of the individual maps relative to the buoy observed wind climates. The remote sensing maps are combined by using QuikSCAT to scale the magnitude of the SAR map. Finally, the in situ predicted wind speeds are incorporated. The mean spatial bias of the final map when compared to buoy time series is 0.1 ms− 1 and the RMSE 0.3 ms− 1, which represents an uncertainty reduction of 50% relative to using only SAR, and of 40% to using only SAR and QuikSCAT without in situ observations.