Attention-Enhanced Convolutional Autoencoder and Structured Delay Embeddings for Weather Prediction

2025-11-16

AI Summary

Short-term weather forecasting poses a fundamental challenge due to the high-dimensional, chaotic nature of atmospheric dynamics. Method: This paper proposes an efficient reduced-order modeling (ROM) framework that combines an attention-enhanced convolutional autoencoder (based on ResNet) for nonlinear spatial dimensionality reduction with linear operator learning in a structured time-delay embedding space. Contribution/Results: A key insight is that projection error—not dynamical approximation error—dominates prediction error, indicating that linear models suffice to capture strong temporal correlations when operating in an appropriately constructed embedding space. Evaluated on ERA5 reanalysis data, the framework achieves significantly higher accuracy than conventional ROM methods for in-distribution short-term forecasts, while maintaining computational efficiency. It thus establishes a new paradigm for interpretable, lightweight modeling of chaotic meteorological systems.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Weather prediction is a quintessential problem involving the forecasting of a complex, nonlinear, and chaotic high-dimensional dynamical system. This work introduces an efficient reduced-order modeling (ROM) framework for short-range weather prediction and investigates fundamental questions in dimensionality reduction and reduced order modeling of such systems. Unlike recent AI-driven models, which require extensive computational resources, our framework prioritizes efficiency while achieving reasonable accuracy. Specifically, a ResNet-based convolutional autoencoder augmented by block attention modules is developed to reduce the dimensionality of high-dimensional weather data. Subsequently, a linear operator is learned in the time-delayed embedding of the latent space to efficiently capture the dynamics. Using the ERA5 reanalysis dataset, we demonstrate that this framework performs well in-distribution as evidenced by effectively predicting weather patterns within training data periods. We also identify important limitations in generalizing to future states, particularly in maintaining prediction accuracy beyond the training window. Our analysis reveals that weather systems exhibit strong temporal correlations that can be effectively captured through linear operations in an appropriately constructed embedding space, and that projection error rather than inference error is the main bottleneck. These findings shed light on some key challenges in reduced-order modeling of chaotic systems and point toward opportunities for hybrid approaches that combine efficient reduced-order models as baselines with more sophisticated AI architectures, particularly for applications in long-term climate modeling where computational efficiency is paramount.

Problem

• Develops efficient reduced-order model for short-range weather prediction
• Uses attention-enhanced autoencoder to reduce weather data dimensionality
• Analyzes limitations in generalizing predictions beyond training periods

Innovation

• ResNet-based autoencoder with attention for data reduction
• Linear operator in time-delayed latent space captures dynamics
• Hybrid approach combines efficient ROM with AI architectures