Using AWS-WeatherBug observations and the WRF-ARW model for wind energy applications

There is a growing interest in the economic potential of building wind farms both in the US and abroad. A challenge is to determine sites that have wind power capacity from a meteorological perspective, and are economically viable. To adequately assess the power potential for various wind farm configurations in regions already identified as likely sites an average expected wind energy production and its diurnal variability have to be estimated. We discuss how a combination of extensive datasets of real-time surface observations, such as those from the network of WeatherBug stations operated by AWS Convergence Technologies, and high-resolution mesoscale numerical weather prediction models, (e.g., WRF-ARW), can be used for such analysis. Surface and near-surface observations from numerous locations typically include temperature, humidity, wind speed and direction, as well as information on damaging wind gusts. In contrast, a weather model can provide complete coverage at the proper scale (horizontally and vertically) for the domain of interest and therefore capture local variations in wind power capacity.

For a selected geographical region in the northeastern United States, we evaluate and compare trends in near-surface fields and their distributions using AWS surface observations and WRF-ARW initialized based on NAM and RUC analyses. We also assimilate AWS surface observations using three-dimensional variational methods (WRF-3DVAR) when horizontal resolution is one to four km, and consider cases where complex orography creates a significant challenge to the assessment of available wind power. We will also discuss sensitivity studies we performed to analyze the impact of surface data assimilation on near-surface wind modeling at high temporal and spatial resolution.