State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems
van Leeuwen, Peter Jan
KAUST DepartmentWater Desalination and Reuse Research Center (WDRC)
Biological and Environmental Sciences and Engineering (BESE) Division
Online Publication Date2018-03-21
Print Publication Date2018-01
Permanent link to this recordhttp://hdl.handle.net/10754/630565
MetadataShow full item record
AbstractThis 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.
CitationVetra-Carvalho S, van Leeuwen PJ, Nerger L, Barth A, Altaf MU, et al. (2018) State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems. Tellus A: Dynamic Meteorology and Oceanography 70: 1445364. Available: http://dx.doi.org/10.1080/16000870.2018.1445364.
SponsorsThis work was supported by the SANGOMA EU Project [grant number FP7-SPACE-2011-1-CT-283580-621 SANGOMA]. PJvL thanks the European Research Council (ERC) for funding of the CUNDA project under the European Unions Horizon 2020 research and innovation programme.
PublisherInforma UK Limited