A non-linear least squares enhanced POD-4DVar algorithm for data assimilation

• Published in: Tellus. Series A, Dynamic meteorology and oceanography Vol. 67; no. 1; pp. 25340 - 12
• Main Authors: Tian, Xiangjun, Feng, Xiaobing
• Format: Journal Article
• Language: English
• Published: Stockholm Taylor & Francis 01.01.2015; Ubiquity Press; Stockholm University Press
• Subjects: 4DVar; Algorithms; Computer simulation; Data assimilation; Data collection; Experiments; Gauss-Newton method; Iterative methods; Least squares method; Mathematical models; Meteorology; Methods; non-linear ensemble; non-linear least squares; Nonlinearity; observing system simulation experiments; Oceanography; Perturbation methods; Rain; Remote sensing; Weather forecasting

This paper presents a novel non-linear least squares enhanced proper orthogonal decomposition (POD)-based 4DVar algorithm (referred as NLS-4DVar) for the non-linear ensemble-based 4DVar. In the algorithm, the Gauss-Newton iterative method is employed to handle the non-quadratic non-linearity of the...