This repository contains deep learning tools for enhancing the spatial resolution of wind data. The software is developed in Python using the TensorFlow deep learning package. Models for diversity super-resolution is provided. Included in the package are pretrained models with example code/data to perform the super-resolution as well as tools of training models for different enhancement- or data-types.
The super-resolution is an inherently ill-conditioned problem, with multiple high-resolution fields plausibly mapping to the same coarse field. Considitional GANs provide a framework for generating a distribution of high-resolution realizations from a given low-resolution input. Stochastic estimation is used to inform the network of the expected degree and location of sub-grid diversity. The package includes a pretrained network to generate distributions of 10x-enhanced fields of wind data.
Preprint version (open access)
@article{hassanaly2022adversarial,
title={Adversarial sampling of unknown and high-dimensional conditional distributions},
author={Hassanaly, Malik and Glaws, Andrew and Stengel, Karen and King, Ryan N.},
journal={Journal of Computational Physics},
pages={110853},
volume={450},
year={2022},
publisher={Elsevier}
}