# WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data

arXiv: https://arxiv.org/abs/2109.08770 · HTML: https://mlsanity.com/paper/2109.08770/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [The Perception-Distortion Tradeoff](https://mlsanity.com/paper/1711.06077/) (2017)
- [SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models](https://mlsanity.com/paper/2104.14951/) (2021)
- [SRFlow: Learning the Super-Resolution Space with Normalizing Flow](https://mlsanity.com/paper/2006.14200/) (2020)
- [DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution](https://mlsanity.com/paper/1703.03126/) (2017)
- [Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network](https://mlsanity.com/paper/2005.10374/) (2020)

## Built on by (most cited first, 11 of 11)

- [Hard-Constrained Deep Learning for Climate Downscaling](https://mlsanity.com/paper/2208.05424/) (2022)
- [Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models](https://mlsanity.com/paper/2401.15469/) (2024)
- [Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers](https://mlsanity.com/paper/2207.11417/) (2022)
- [Contrastive Learning for Climate Model Bias Correction and Super-Resolution](https://mlsanity.com/paper/2211.07555/) (2022)
- [Generative Adversarial Models for Extreme Geospatial Downscaling](https://mlsanity.com/paper/2402.14049/) (2024)
- [PuYun: Medium-Range Global Weather Forecasting Using Large Kernel Attention Convolutional Networks](https://mlsanity.com/paper/2409.02123/) (2024)
- [Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification](https://mlsanity.com/paper/2211.16708/) (2022)
- [A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network](https://mlsanity.com/paper/2312.13212/) (2023)
- [Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data](https://mlsanity.com/paper/2401.16936/) (2024)
- [MeltwaterBench: Deep learning for spatiotemporal downscaling of surface meltwater](https://mlsanity.com/paper/2512.12142/) (2025)
- [SolarFlowRefiner: Refinement-Aware Flow Matching for Surface Solar Radiation Downscaling](https://mlsanity.com/paper/2609.22126/) (2026)

## Abstract (arXiv)

The transition to green energy grids depends on detailed wind and solar forecasts to optimize the siting and scheduling of renewable energy generation. Operational forecasts from numerical weather prediction models, however, only have a spatial resolution of 10 to 20-km, which leads to sub-optimal usage and development of renewable energy farms. Weather scientists have been developing super-resolution methods to increase the resolution, but often rely on simple interpolation techniques or computationally expensive differential equation-based models. Recently, machine learning-based models, specifically the physics-informed resolution-enhancing generative adversarial network (PhIREGAN), have outperformed traditional downscaling methods. We provide a thorough and extensible benchmark of leading deep learning-based super-resolution techniques, including the enhanced super-resolution generative adversarial network (ESRGAN) and an enhanced deep super-resolution (EDSR) network, on wind and solar data. We accompany the benchmark with a novel public, processed, and machine learning-ready dataset for benchmarking super-resolution methods on wind and solar data.