A Time-parallel Approach to Strong-constraint Four-dimensional Variational Data Assimilation

TL;DR

This paper introduces a parallel-in-time algorithm for 4D-Var data assimilation that enhances computational efficiency by dividing the assimilation window into sub-intervals and using an augmented Lagrangian approach, demonstrated on Lorenz-96 and shallow water models.

Contribution

It presents a novel parallel-in-time algorithm for 4D-Var data assimilation using an augmented Lagrangian method, enabling efficient computation across sub-intervals.

Findings

Effective parallelization of 4D-Var demonstrated on Lorenz-96 model.

Improved performance with combined serial and parallel 4D-Var approaches.

Method offers a new formulation distinct from weakly constrained 4D-Var.

Abstract

A parallel-in-time algorithm based on an augmented Lagrangian approach is proposed to solve four-dimensional variational (4D-Var) data assimilation problems. The assimilation window is divided into multiple sub-intervals that allows to parallelize cost function and gradient computations. Solution continuity equations across interval boundaries are added as constraints. The augmented Lagrangian approach leads to a different formulation of the variational data assimilation problem than weakly constrained 4D-Var. A combination of serial and parallel 4D-Vars to increase performance is also explored. The methodology is illustrated on data assimilation problems with Lorenz-96 and the shallow water models.