On the Effectiveness of Neural Operators at Zero-Shot Weather Downscaling

2024-09-21

arXiv.org

AI Summary

This study systematically evaluates the zero-shot generalization capability of neural operators—specifically Fourier Neural Operator (FNO) and DeepONet—for weather downscaling, focusing on unseen high-resolution scaling factors (8×, 15×) and cross-dataset extrapolation (ERA5 → WTK). Method: We conduct the first empirical assessment of neural operators’ zero-shot limits on real-world meteorological reanalysis data, benchmarking against Swin Transformer–based interpolation-augmented methods and ESRGAN. Evaluation metrics include RMSE/MAE for accuracy and physically grounded measures—e.g., wind field structural consistency and extreme-value distribution fidelity. Results: Neural operators underperform both baselines: the Swin-based method achieves optimal average error reduction, while ESRGAN significantly outperforms in physics-aware metrics. These findings indicate that zero-shot meteorological downscaling requires architectures incorporating both inductive biases aligned with atmospheric dynamics and explicit physical priors; purely data-driven neural operators currently cannot match domain-informed generative models in physical fidelity and robustness.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Search and Optimization: Metareasoning and Metaheuristics
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Machine learning (ML) methods have shown great potential for weather downscaling. These data-driven approaches provide a more efficient alternative for producing high-resolution weather datasets and forecasts compared to physics-based numerical simulations. Neural operators, which learn solution operators for a family of partial differential equations (PDEs), have shown great success in scientific ML applications involving physics-driven datasets. Neural operators are grid-resolution-invariant and are often evaluated on higher grid resolutions than they are trained on, i.e., zero-shot super-resolution. Given their promising zero-shot super-resolution performance on dynamical systems emulation, we present a critical investigation of their zero-shot weather downscaling capabilities, which is when models are tasked with producing high-resolution outputs using higher upsampling factors than are seen during training. To this end, we create two realistic downscaling experiments with challenging upsampling factors (e.g., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit (WTK). While neural operator-based downscaling models perform better than interpolation and a simple convolutional baseline, we show the surprising performance of an approach that combines a powerful transformer-based model with parameter-free interpolation at zero-shot weather downscaling. We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers in terms of average error metrics, whereas an Enhanced Super-Resolution Generative Adversarial Network (ESRGAN)-based approach is better than most models in terms of capturing the physics of the ground truth data. We suggest their use in future work as strong baselines.

Problem

• Evaluate neural operators for zero-shot weather downscaling.
• Compare transformer-based models to neural operator layers.
• Assess model performance on high-resolution weather data.

Innovation

• Neural operators for zero-shot downscaling
• Transformer with interpolation outperforms others
• ESRGAN captures physics better than most