Physics Encoded Spatial and Temporal Generative Adversarial Network for Tropical Cyclone Image Super‐Resolution

Ruoyi Zhang, Jiawei Yuan, Lujia Ye, Runling Yu, Liling Zhao

IET Image Processing · 01 January 2026 · China Meteorological Administration (CMA), Nanjing University of Information Science & Technology (NUIST), University of Reading

Paper

In one sentence. Physics-encoded GAN super-resolves tropical cyclone satellite image sequences 4x, using a vorticity-based module and temporal discriminator for motion consistency.

Abstract

ABSTRACT High‐resolution satellite imagery is indispensable for tracking the genesis, intensification, and trajectory of tropical cyclones (TCs). However, existing deep learning‐based super‐resolution (SR) methods often treat satellite image sequences as generic videos, neglecting the underlying atmospheric physical laws governing cloud motion. To address this, we propose a physics encoded spatial and temporal generative adversarial network (PESTGAN) for TC image super‐resolution. Specifically, we design a disentangled generator architecture incorporating a PhyCell module, which approximates the vorticity equation via constrained convolutional operators and encodes the resulting approximate physical dynamics as implicit latent representations to separate physical dynamics from visual textures. Furthermore, a dual‐discriminator framework is introduced, employing a temporal discriminator to enforce motion consistency alongside spatial realism. Experiments on the digital typhoon dataset for 4 upscaling demonstrate that PESTGAN establishes a better performance in structural fidelity and perceptual quality. While maintaining competitive pixel‐level accuracy compared to existing approaches, our method significantly excels in reconstructing meteorologically plausible cloud structures with superior physical fidelity.

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