Statistical downscaling of wind for California wind farms with an application to 21st Century climate scenarios

Wind farms are an important source of renewable energy in California. This study uses large-scale atmospheric fields to estimate low-level winds in the complex topographical settings of three of California's major wind farms: Tehachapi and San Gorgonio passes, and north of the Sacramento Delta in the Central Valley. Relationships derived from atmospheric circulation variables in the NCEP Reanalysis and decades' worth of observed winds at stations near the wind farms are used to develop a statistical downscaling model. Synoptic state, classified by self-organizing maps, as well as sea-level pressure, temperature, and upper-air heights are formed into a multilinear regression model. For each site, the optimal fields and coefficients are selected based on one time period, then verified using a separate verification period. The key variables and their relationships to station wind change with each season. The influences of synoptic-scale patterns as well as smaller-scale thermal contrasts are each visible and influential on downscaled winds. In the validation, 50 to 75% of daily wind speed variability is captured by the downscaling scheme, and error on various time scales is analyzed.

The model is used to downscale greenhouse-gas forced 21st Century climate simulations prepared for the IPCC, representing various emissions scenarios. The range of simulated downscaled changes in wind speed and wind power availability, variability, and distribution is summarized for every site and season of the year.