Simulation-informed deep learning for enhanced SWOT observations of fine-scale ocean dynamics
This work addresses the challenge of accurately observing fine-scale oceanic processes for oceanographers, though it is incremental as it builds on existing neural methods with a novel unsupervised approach.
The paper tackled the problem of noise obscuring fine-scale ocean dynamics in SWOT satellite data by introducing SIMPGEN, an unsupervised adversarial learning framework that combines real observations with simulated data to remove noise while preserving features, achieving better performance than existing neural methods.
Oceanic processes at fine scales are crucial yet difficult to observe accurately due to limitations in satellite and in-situ measurements. The Surface Water and Ocean Topography (SWOT) mission provides high-resolution Sea Surface Height (SSH) data, though noise patterns often obscure fine scale structures. Current methods struggle with noisy data or require extensive supervised training, limiting their effectiveness on real-world observations. We introduce SIMPGEN (Simulation-Informed Metric and Prior for Generative Ensemble Networks), an unsupervised adversarial learning framework combining real SWOT observations with simulated reference data. SIMPGEN leverages wavelet-informed neural metrics to distinguish noisy from clean fields, guiding realistic SSH reconstructions. Applied to SWOT data, SIMPGEN effectively removes noise, preserving fine-scale features better than existing neural methods. This robust, unsupervised approach not only improves SWOT SSH data interpretation but also demonstrates strong potential for broader oceanographic applications, including data assimilation and super-resolution.