Title: Scalable parallel solution and uncertainty quantification techniques for variational data assimilation

Author: Vishwas Rao (Virginia Tech.)
Adrian Sandu (Virginia Tech.)

Data assimilation (DA) uses physical measurements along with a physical model to estimate the parameters or state of a physical system. Solution to DA problems using the variational approach require multiple evaluations of the associated cost function and gradient. In this work we present a scalable algorithm based on augmented Lagrangian approach to solve the 4D-Var. The augmented Lagrangian framework facilitates parallel cost function and gradient computations. We show that the methodology is highly scalable with increasing problem size by applying it for the Lorenz-96 model. We also develop a systematic framework to quantify the impact of data and model uncertainties on the solution to the 4D-Var.

GMAO Head: Steven Pawson Global Modeling and Assimilation Office NASA Goddard Space Flight Center

Curator: Nikki Privé Last Updated: Feb 9 2015