Frequency-Aware Vision Transformers for High-Fidelity Super-Resolution of Earth System Models

TL;DR

This paper introduces frequency-aware vision transformer frameworks, ViSIR and ViFOR, to improve high-fidelity super-resolution of Earth system models by effectively capturing high-frequency details and reducing spectral bias.

Contribution

The paper proposes two novel frequency-aware transformer-based models, ViSIR and ViFOR, that enhance super-resolution of Earth system data by explicitly addressing spectral bias and high-frequency detail recovery.

Findings

ViFOR achieves up to 2.6 dB PSNR improvement.

Models outperform convolutional, generative, and vanilla transformer baselines.

Effective high-frequency detail reconstruction in Earth system model super-resolution.

Abstract

Super-resolution can play an essential role in enhancing the spatial fidelity of Earth System Model outputs, allowing fine-scale structures highly beneficial to climate science to be recovered from coarse simulations. However, traditional deep super-resolution methods, including convolutional and transformer based models, tend to exhibit spectral bias, reconstructing low-frequency content more readily than valuable high-frequency details. In this work, we introduce ViSIR and ViFOR, two frequency-aware frameworks. ViSIR stands for the Vision Transformer-Tuned Sinusoidal Implicit Representation. ViSIR combines vision transformers with sinusoidal activations to mitigate spectral bias. ViFOR stands for the Vision Transformer Fourier Representation Network. ViFOR integrates explicit Fourier based filtering for independent low- and high-frequency learning. Evaluated on the E3SM-HR Earth system dataset across surface temperature, shortwave, and longwave fluxes, these models outperform leading Convolutional NN, Generative Networks, and vanilla transformer baselines, with ViFOR demonstrating up to 2.6~dB improvements in Peak Signal to Noise Ratio and higher Structural Similarity.