# OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectories

## TL;DR

OASIS introduces a diffusion adversarial network to improve ocean salinity data imputation, effectively handling sparse, irregular, and noisy drifter measurements by integrating physical covariates.

## Contribution

The paper presents a novel diffusion adversarial framework, OASIS, specifically designed for ocean salinity imputation from sparse drifter trajectories, overcoming limitations of traditional and existing machine learning methods.

## Key findings

- OASIS outperforms traditional interpolation methods in accuracy.
- The framework effectively handles data sparsity and noise.
- Incorporates physical covariates for improved imputation quality.

## Abstract

Ocean salinity plays a vital role in circulation, climate, and marine ecosystems, yet its measurement is often sparse, irregular, and noisy, especially in drifter-based datasets. Traditional approaches, such as remote sensing and optimal interpolation, rely on linearity and stationarity, and are limited by cloud cover, sensor drift, and low satellite revisit rates. While machine learning models offer flexibility, they often fail under severe sparsity and lack principled ways to incorporate physical covariates without specialized sensors. In this paper, we introduce the OceAn Salinity Imputation System (OASIS), a novel diffusion adversarial framework designed to address these challenges.