Conditional Generative Models for OceanBench Sea Surface Height Interpolation

Rising sea levels are one of many consequences of anthropogenic climate
change. Over the past few decades, several global observational records have
become available that give a more detailed picture of the increasing
impacts. Nevertheless, there continue to be data challenges, such as
sparsity or signal to noise ratio, that need to be dealt with. Machine Learning (ML)
and specifically, Deep Learning (DL) approaches have presented themselves as valuable
tools for such large-scale and complex data sources. To this end, the OceanBench
Benchmark suite was recently developed to provide a
standardized pre-processing and evaluation framework for Sea Surface Height
(SSH) interpolation tasks involving nadir and Surface Water and Ocean Topography
(SWAT) Altimetry Tracks. From the methodological perspective, a reoccurring
issue is the lack of uncertainty quantification for DL applications in Earth
Observation. Therefore, we extend the suite of metrics provided by OceanBench
to probabilistic evaluation metrics and test state-of-the-art uncertainty
quantification models from the DL community. Specifically, we focus on
Conditional Convolutional Neural Processes (ConvCNP) and
Inpainting Diffusion models as methodologies to quantify
uncertainty for the interpolation task and demonstrate their viability and
advantages over other ML methods for both accuracy and probabilistic metrics.