A Geometric Algebra-Inspired Spatio-temporal Learning Model for Sea Level Variability Reconstruction Under Incomplete Observations

A Variationally Constrained Attention Model for Sea Surface Height Reconstruction With Multisource Observations

Sea surface height (SSH) is a key variable for characterizing ocean dynamics, yet its high-resolution reconstruction remains challenging due to sparse satellite observations and the limited ability of conventional methods to represent multiscale nonlinear processes. This study proposes a physics-constrained SSH reconst...

Reconstructing subsurface temperature fields from single-point time series: a metric learning approach in the South China Sea

Subsurface ocean observations remain severely limited in spatial coverage due to the high cost and operational difficulty of in-situ deployment. Although moored buoys and profiling floats enable continuous, minute-level sampling at fixed locations, the temporal evolution information they record is largely underutil...

Nonlinear Latent-Space Data Assimilation for Sea Surface Height Reconstruction from Sparse Observations

Estimating multiscale ocean-surface states from sparse observations is challenging because the state is high-dimensional, sampling is irregular, and posterior distributions can be strongly non-Gaussian. We develop Latent-LWETKF, a structured latent-space implementation of the localized weighted ensemble transform Kalma...

4DVarGen: A 4D Variational-Inspired Generative Model for Eddy-Resolving Surface Ocean Reconstruction

4DVarGen is proposed, a 4DVar-inspired generative framework for reconstructing sea surface variable fields at eddy-resolving scales from sparse remote-sensing observations that establishes a mathematical equivalence between 4DVar and an observation-guided denoising process.

Pure and physics-guided deep learning approaches for spatio-temporal groundwater level prediction

Groundwater represents a key element of the water cycle, yet it exhibits intricate and context-dependent relationships that make its modeling a challenging task. Theory-based models have been the cornerstone of scientific understanding. However, their computational demands, simplifying assumptions, and calibration requ...

Deep Learning-Based Meteorological Data Downscaling: A Comparative Study with Physics-Informed CNN and a Component-Level Ablation Analysis