Enhancing operational wind downscaling capabilities over Canada: Application of a Conditional Wasserstein GAN methodology

TL;DR

This paper introduces an advanced wind downscaling method over Canada using a Conditional Wasserstein GAN with high-resolution covariates, significantly improving forecast accuracy and operational scalability for weather prediction.

Contribution

It extends the DownGAN framework by incorporating high-resolution static covariates and Frequency Separation techniques, enabling more accurate and scalable wind downscaling over large regions.

Findings

Reduced RMSE and LSD metrics demonstrate improved accuracy.

Effective integration of high-resolution covariates enhances model performance.

Operational scalability achieved for Canadian wind downscaling.

Abstract

Wind downscaling is essential for improving the spatial resolution of weather forecasts, particularly in operational Numerical Weather Prediction (NWP). This study advances wind downscaling by extending the DownGAN framework introduced by Annau et al.,to operational datasets from the Global Deterministic Prediction System (GDPS) and High-Resolution Deterministic Prediction System (HRDPS), covering the entire Canadian domain. We enhance the model by incorporating high-resolution static covariates, such as HRDPS-derived topography, into a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty, implemented using a UNET-based generator. Following the DownGAN framework, our methodology integrates low-resolution GDPS forecasts (15 km, 10-day horizon) and high-resolution HRDPS forecasts (2.5 km, 48-hour horizon) with Frequency Separation techniques adapted from computer vision. Through robust training and inference over the Canadian region, we demonstrate the operational scalability of our approach, achieving significant improvements in wind downscaling accuracy. Statistical validation highlights reductions in root mean square error (RMSE) and log spectral distance (LSD) metrics compared to the original DownGAN. High-resolution conditioning covariates and Frequency Separation strategies prove instrumental in enhancing model performance. This work underscores the potential for extending high-resolution wind forecasts beyond the 48-hour horizon, bridging the gap to the 10-day low resolution global forecast window.