  The ocean profoundly influences human activities and plays a critical role in
climate regulation. Our understanding has improved over the last decades with
the advent of satellite remote sensing data, allowing us to capture essential
quantities over the globe, e.g., sea surface height (SSH). However, ocean
satellite data presents challenges for information extraction due to their
sparsity and irregular sampling, signal complexity, and noise. Machine learning
(ML) techniques have demonstrated their capabilities in dealing with
large-scale, complex signals. Therefore we see an opportunity for ML models to
harness the information contained in ocean satellite data. However, data
representation and relevant evaluation metrics can be the defining factors when
determining the success of applied ML. The processing steps from the raw
observation data to a ML-ready state and from model outputs to interpretable
quantities require domain expertise, which can be a significant barrier to
entry for ML researchers. OceanBench is a unifying framework that provides
standardized processing steps that comply with domain-expert standards. It
provides plug-and-play data and pre-configured pipelines for ML researchers to
benchmark their models and a transparent configurable framework for researchers
to customize and extend the pipeline for their tasks. In this work, we
demonstrate the OceanBench framework through a first edition dedicated to SSH
interpolation challenges. We provide datasets and ML-ready benchmarking
pipelines for the long-standing problem of interpolating observations from
simulated ocean satellite data, multi-modal and multi-sensor fusion issues, and
transfer-learning to real ocean satellite observations. The OceanBench
framework is available at github.com/jejjohnson/oceanbench and the dataset
registry is available at github.com/quentinf00/oceanbench-data-registry.
