State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems
Vetra-Carvalho, S.; Van Leeuwen, P.J.; Nerger, L.; Barth, A.; Altaf, M.U.; Brasseur, P.; Kirchgessner, P.; Beckers, J.-M. (2018). State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems. Tellus, Ser. A, Dyn. meteorol. oceanogr. 70(1): 1-43. https://hdl.handle.net/10.1080/16000870.2018.1445364
In: Tellus. Series A: Dynamic Meteorology and Oceanography. Blackwell: Copenhagen. ISSN 0280-6495; e-ISSN 1600-0870, meer
|   |
| Author keywords |
ensemble Kalman filter; particle filter; data assimilation; high dimension; non Gaussian |
| Auteurs | | Top |
- Vetra-Carvalho, S.
- Van Leeuwen, P.J.
- Nerger, L.
- Barth, A., meer
|
- Altaf, M.U.
- Brasseur, P.
- Kirchgessner, P.
- Beckers, J.-M., meer
|
|
| Abstract |
This paper compares several commonly used state-of-the-art ensemble-based data assimilation methods in a coherent mathematical notation. The study encompasses different methods that are applicable to high-dimensional geophysical systems, like ocean and atmosphere and provide an uncertainty estimate. Most variants of Ensemble Kalman Filters, Particle Filters and second-order exact methods are discussed, including Gaussian Mixture Filters, while methods that require an adjoint model or a tangent linear formulation of the model are excluded. The detailed description of all the methods in a mathematically coherent way provides both novices and experienced researchers with a unique overview and new insight in the workings and relative advantages of each method, theoretically and algorithmically, even leading to new filters. Furthermore, the practical implementation details of all ensemble and particle filter methods are discussed to show similarities and differences in the filters aiding the users in what to use when. Finally, pseudo-codes are provided for all of the methods presented in this paper. |
|
