Improving ocean reanalyses of observationally sparse regions with transfer learning.

ABSTRACT: Oceanic subsurface observations are sparse and lead to large uncertainties in any model-based estimate. We investigate the applicability of transfer learning based neural networks to reconstruct North Atlantic temperatures in times with sparse observations. Our network is trained on a time period with abundant observations to learn realistic physical behavior. Evaluating it within a consistent data assimilation framework, this network learns and reproduces its training data's physical patterns. Additionally, the network is able to transfer these patterns towards a historical ocean heat content estimate in times with sparse observations. Consequently, with infrequent input data, machine learning reconstructions exhibit similar physical structures, while correcting for known errors compared

SUBMITTER: Lentz S 

PROVIDER: S-EPMC11751375 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature