Influence of Large-Scale Climate on Offshore Wind-Farm Scale Environment: A Deep Learning Approach

Characterization of the spatiotemporal variability of the local wind

farm scale environment is necessary for better developing offshore

wind energy infrastructure. However, while such variability is

strongly influenced by small-scale processes such as coastal low level

jets (CLLJ), mesoscale fronts, and internal boundary layers, given the

complex multiscale nature of the climate system, large scale climate

phenomena such as marine heatwaves and atmospheric blocking and modes

of large-scale climate variability such as El Nino Southern

Oscillation, Pacific Decadal Oscillation and North Atlantic

Oscillation are likely to contribute to the variability of the local

environment as well. Such influences could be through changing

land-ocean temperature contrast, changing boundary-layer

stratification, etc.,---aspects of the local environment that in turn

condition smaller scale processes. Since high resolution simulations

are computationally expensive and since high resolution data is

sparse, we investigate the use of deep-learning to increase the signal

to noise ratio and facilitate the examination of large-scale climate

influences on the local environment. We present preliminary results

from a study that uses a reanalysis-driven, regional, high resolution

Weather Research Forecast (WRF) simulation as reference and develops

generative models--- conditional versions of Generative Adversarial

Networks (cGANs) and Variational AutoEncoders (cVAEs)---to augment

wind farm scale data conditioned on large-scale climate data.