Winds, Clouds, and Rain Over the Central and Northern California Offshore Wind Lease Areas

To achieve the goal of 15 GW of U.S. offshore wind capacity by 2035, multiple leases have been awarded for floating offshore wind farms over the central and northern California region. This region is particularly challenging to do site characterization and model assessment of these lease areas due to the harsh marine environment and scarcity of observations. To address this need, the U.S. Department of Energy’s Wind Energy Technology Office (WETO) funded the Observationally driven Resource Assessment with CoupLEd models (ORACLE) project, with efforts underway to characterize the weather and climate scales over these wind lease areas. To this extent, an extensive multi-year database has been generated that includes near-surface winds, cloud and precipitation properties derived from satellite sensors, output from reanalysis models, and forecasts from an operational weather model. The near-surface winds were derived from Synthetic Aperature Radar (SAR) and scatterometers onboard polar-orbiting satellites. Sea surface temperatures (SST) were derived from radiometers onboard polar orbiting and geostationary satellites. The cloud top heights, cloud thickness, cloud fraction, and integrated water content were derived from radiometers onboard geostationary satellites, while the rain rates are part of the Integrated Multi-satellitE Retrievals for GPM (IMERG) database. These observational datasets varied in temporal (daily to hourly), and spatial (~1km to 20 km) resolutions, as well as historic availability. To complement this observational database, the output from the European Center for Medium-range Weather Forecasting (ERA5) reanalysis model, and output from the High-Resolution Rapid Refresh (HRRR) model also has been aggregated. In this talk, we will present the database and showcase its utility in characterizing the winds, clouds, and rain in the offshore lease areas. The historic duration and spatial resolution of the database allows us to fully capture the diurnal and annual cycle of winds, meteorology, and clouds. Currently, efforts are underway to utilize the database to address the ability of HRRR model in forecasting the hub-height winds during different weather, cloud, and precipitation conditions.