Temporal Bridges for Spatial Resolution: Enhancing Climate Data Super-Resolution with Bidirectional Alignment

2026-08-06

AI Summary

This work addresses the limitations of existing climate data super-resolution methods, which often focus solely on single-frame spatial information while neglecting temporal dependencies and exhibiting high sensitivity to noise, thereby compromising reconstruction accuracy. To overcome these issues, we propose a temporally enhanced bidirectional alignment framework that, for the first time in climate super-resolution, incorporates a bidirectional temporal alignment mechanism. By employing paired latent-space mappings, our approach unifies spatiotemporal representations and effectively suppresses noise, enabling the exploitation of implicit temporal correlations. Departing from conventional strategies such as optical flow—ill-suited for climate data—our method leverages deep networks for end-to-end optimization, integrating forward–backward alignment with a super-resolution module. Extensive experiments on large-scale real-world climate datasets demonstrate that the proposed framework significantly improves both fine-detail recovery and spatiotemporal consistency.

Abstract

High-resolution climate data is crucial for meteorological predictions and for informing decision support across diverse domains. However, the acquisition of such high-resolution climate information is often prohibitively costly, necessitating the development of data-driven meteorological prediction models. These models aim to generate fine-grained climate data from low-resolution inputs, a process termed climate data super-resolution (SR). Nevertheless, recent advancements in deep learning for climate data SR have primarily focused on leveraging single-frame spatial information, largely neglecting the temporal correlations between different time frames that could enhance SR outcomes. Furthermore, climate data are inherently stochastic and noisy, rendering widely used temporal alignment methods, such as optical flow models, ineffective in this context. Consequently, the development of a framework tailored for climate data SR that effectively captures implicit temporal correlations remains an unresolved challenge. To this end, we propose a novel Temporal-Enhanced framework with bidirectional temporal alignment. In essence, our framework establishes a temporal bridge to enhance spatial resolution in climate data SR through bidirectional alignment, leading to improved SR performance. Within this framework, Paired Latent Mapping achieves spatial alignment and noise reduction by unifying latent spaces. Then a Bidirectional Temporal Alignment captures temporal correlations by training forward and backward networks on consecutive latent frames. Temporal Enhanced Super-resolution then optimizes the entire framework for climate data SR. Experiments on large-scale real-world datasets demonstrated the superior performance of our framework.

Problem

• climate data super-resolution
• temporal correlation
• spatial resolution
• temporal alignment
• stochastic noise

Innovation

• Temporal Alignment
• Climate Data Super-Resolution
• Bidirectional Network
• Latent Space Mapping
• Spatiotemporal Modeling