Multiscale neural assimilation scheme for high-resolution sea surface temperature reconstruction from satellite observations.

ABSTRACT: Sea Surface Temperature (SST) retrieved from satellites is a crucial variable for weather prediction and climate monitoring, yet satellite-based SST observations are often hindered by orbital constraints and cloud cover-especially in regions like the North and Baltic Seas where high-resolution data is essential to support operational monitoring and forecasting. Existing SST reanalysis products frequently lack the spatial and temporal granularity needed for these dynamic coastal areas. To address this, we explore an advanced data-driven approach to SST reconstruction using 4DVarNet, an end-to-end deep learning framework that integrates variational data assimilation with machine learning. While 4DVarNet has shown promising results in reconstructing SST fields, it lacks a robust mechanism for

SUBMITTER: Beauchamp M 

PROVIDER: S-EPMC12618594 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature