Comparison of Ensemble-Based Data Assimilation Methods for Sparse Oceanographic Data

TL;DR

This paper compares two ensemble-based data assimilation methods, the localized ensemble Kalman filter and the implicit equal-weights particle filter, for sparse oceanographic data, demonstrating their effectiveness in improving drift prediction accuracy.

Contribution

It introduces tailored localization strategies for sparse data and evaluates two state-of-the-art ensemble methods in oceanographic applications with sparse observations.

Findings

The localized ensemble Kalman filter improves prediction bias and accuracy.

The implicit equal-weights particle filter enhances distribution coverage and spatial connectivity.

Both methods outperform baseline models in drift trajectory forecasts.

Abstract

For oceanographic applications, probabilistic forecasts typically have to deal with i) high-dimensional complex models, and ii) very sparse spatial observations. In search-and-rescue operations at sea, for instance, the short-term predictions of drift trajectories are essential to efficiently define search areas, but in-situ buoy observations provide only very sparse point measurements, while the mission is ongoing. Statistically optimal forecasts, including consistent uncertainty statements, rely on Bayesian methods for data assimilation to make the best out of both the complex mathematical modeling and the sparse spatial data. To identify suitable approaches for data assimilation in this context, we discuss localisation strategies and compare two state-of-the-art ensemble-based methods for applications with spatially sparse observations. The first method is a version of the ensemble-transform Kalman filter, where we tailor a localisation scheme for sparse point data. The second method is the implicit equal-weights particle filter which has recently been tested for related oceanographic applications. First, we study a linear spatio-temporal model for contaminant advection and diffusion, where the analytical Kalman filter provides a reference. Next, we consider a simplified ocean model for sea currents, where we conduct state estimation and predict drift. Insight is gained by comparing ensemble-based methods on a number of skill scores including prediction bias and accuracy, distribution coverage, rank histograms, spatial connectivity and drift trajectory forecasts.