Reinstating Continuous Climate Patterns From Small and Discretized Data

Wind energy is a leading renewable energy source. It does not pollute the environment and reduces greenhouse gas emissions that contribute to global warming. However, current wind characterization is performed at a resolution insufficient for assessing renewable energy resources in different climate scenarios. In this paper, we advocate the use of generative deep models for wind field representation learning. In contrast to existing approaches, we formulate the generative model as an explicit function of the spatial coordinate, thereby learning a continuous representation of the wind field, which can extrapolate from discretized data with demonstrated generalizability. We extend the concept of conditional neural fields by encoding the local turbulent wind properties into latent variables. Such resolution enhancement enables essential localized analyses of renewable energy resources' long-term economic sustainability.