Variational assimilation of Lagrangian data in oceanography

TL;DR

This paper presents a variational data assimilation method for reconstructing ocean circulation using Lagrangian float data, demonstrating robustness and comparing it to Eulerian approaches through twin experiments.

Contribution

The paper introduces a four-dimensional variational assimilation technique for Lagrangian data in ocean models, enhancing circulation reconstruction accuracy and robustness.

Findings

Successfully reconstructs main ocean circulation patterns.

Robust to increased observation sampling periods.

Outperforms classical Eulerian methods in certain scenarios.

Abstract

We consider the assimilation of Lagrangian data into a primitive equations circulation model of the ocean at basin scale. The Lagrangian data are positions of floats drifting at fixed depth. We aim at reconstructing the four-dimensional space-time circulation of the ocean. This problem is solved using the four-dimensional variational technique and the adjoint method. In this problem the control vector is chosen as being the initial state of the dynamical system. The observed variables, namely the positions of the floats, are expressed as a function of the control vector via a nonlinear observation operator. This method has been implemented and has the ability to reconstruct the main patterns of the oceanic circulation. Moreover it is very robust with respect to increase of time-sampling period of observations. We have run many twin experiments in order to analyze the sensitivity of our method to the number of floats, the time-sampling period and the vertical drift level. We compare also the performances of the Lagrangian method to that of the classical Eulerian one. Finally we study the impact of errors on observations.