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

2024-12-09

arXiv.org

AI Summary

To address the insufficient spatial resolution and limited forecast horizon in operational wind field downscaling over Canada, this paper proposes a deep generative method integrating multi-source data and physical constraints. Methodologically, it introduces a novel architecture combining a conditional Wasserstein GAN with gradient penalty (WGAN-GP) and a U-Net generator under a frequency-domain separation strategy, jointly downsampling GDPS (15 km) and HRDPS (2.5 km) forecasts while incorporating high-resolution static covariates—particularly terrain—to enforce physical consistency. Key contributions include: (i) extending the forecast horizon beyond HRDPS’s 48-hour limit to enable continuous 10-day generation of 2.5 km wind fields; (ii) achieving stable, nationwide operational deployment across Canada; and (iii) demonstrating statistically significant improvements over the DownGAN baseline in both RMSE and log-spectral distance, thereby validating the feasibility and practicality of long-horizon, high-fidelity wind field downscaling.

Technology Category

• Natural Language Processing: Generation
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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.

Problem

• Improving wind downscaling accuracy
• Extending high-resolution wind forecasts
• Reducing RMSE and LSD metrics

Innovation

• Conditional Wasserstein GAN
• UNET-based generator
• Frequency Separation techniques