Transforming Weather Data from Pixel to Latent Space

2025-03-09

AI Summary

Current weather modeling is constrained by pixel-space representations, yielding overly smooth outputs, supporting only single-pressure-variable subsets (PVS), and incurring prohibitive storage and computational costs. To address these limitations, we propose the Weather Latent Autoencoder (WLA), which maps high-dimensional meteorological data into a compact, disentangled, unified latent space—decoupling reconstruction from downstream forecasting tasks to improve both accuracy and spatial detail fidelity while enabling joint multi-PVS modeling. Our key innovations include a novel pressure-variable unified embedding module, enabling the first full-variable ERA5-latent dataset; and an integrated architecture combining channel-spatial attention, pressure-variable alignment, and differentiable vector quantization for end-to-end training and low-bit latent representation. Applied to 244.34 TB of ERA5 data, WLA achieves 0.43 TB storage (568× compression), outperforms pixel-space baselines on downstream forecasting tasks, and substantially reduces both storage and computational overhead.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Large Vision Models
• Natural Language Processing: (Large) Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The increasing impact of climate change and extreme weather events has spurred growing interest in deep learning for weather research. However, existing studies often rely on weather data in pixel space, which presents several challenges such as smooth outputs in model outputs, limited applicability to a single pressure-variable subset (PVS), and high data storage and computational costs. To address these challenges, we propose a novel Weather Latent Autoencoder (WLA) that transforms weather data from pixel space to latent space, enabling efficient weather task modeling. By decoupling weather reconstruction from downstream tasks, WLA improves the accuracy and sharpness of weather task model results. The incorporated Pressure-Variable Unified Module transforms multiple PVS into a unified representation, enhancing the adaptability of the model in multiple weather scenarios. Furthermore, weather tasks can be performed in a low-storage latent space of WLA rather than a high-storage pixel space, thus significantly reducing data storage and computational costs. Through extensive experimentation, we demonstrate its superior compression and reconstruction performance, enabling the creation of the ERA5-latent dataset with unified representations of multiple PVS from ERA5 data. The compressed full PVS in the ERA5-latent dataset reduces the original 244.34 TB of data to 0.43 TB. The downstream task further demonstrates that task models can apply to multiple PVS with low data costs in latent space and achieve superior performance compared to models in pixel space. Code, ERA5-latent data, and pre-trained models are available at https://anonymous.4open.science/r/Weather-Latent-Autoencoder-8467.

Problem

• Transforms weather data from pixel to latent space for efficiency.
• Improves weather model accuracy and reduces computational costs.
• Enables unified representation of multiple pressure-variable subsets.

Innovation

• Weather Latent Autoencoder transforms pixel to latent space
• Pressure-Variable Unified Module enhances multi-scenario adaptability
• Low-storage latent space reduces computational and storage costs