Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variables

TL;DR

This paper introduces Neural Fields as a scalable and effective alternative to Optimal Interpolation for reconstructing geophysical ocean variables, demonstrating comparable accuracy and improved scalability in satellite data gap-filling.

Contribution

It presents a novel application of Neural Fields to geoscience interpolation, addressing scalability issues of traditional methods like OI.

Findings

Neural Fields achieve similar accuracy to OI in sea surface height reconstruction.

NerFs demonstrate scalability with large satellite datasets.

The method is practical for real-world geoscience applications.

Abstract

Optimal Interpolation (OI) is a widely used, highly trusted algorithm for interpolation and reconstruction problems in geosciences. With the influx of more satellite missions, we have access to more and more observations and it is becoming more pertinent to take advantage of these observations in applications such as forecasting and reanalysis. With the increase in the volume of available data, scalability remains an issue for standard OI and it prevents many practitioners from effectively and efficiently taking advantage of these large sums of data to learn the model hyperparameters. In this work, we leverage recent advances in Neural Fields (NerFs) as an alternative to the OI framework where we show how they can be easily applied to standard reconstruction problems in physical oceanography. We illustrate the relevance of NerFs for gap-filling of sparse measurements of sea surface height (SSH) via satellite altimetry and demonstrate how NerFs are scalable with comparable results to the standard OI. We find that NerFs are a practical set of methods that can be readily applied to geoscience interpolation problems and we anticipate a wider adoption in the future.