In the geophysical community the issue of fusing data into models is referred to as inverse methods and data assimilation, whose aim is finding the best estimate of the state, by combining information from the observations and from the numerical and theoretical knowledge of the underlying governing dynamical laws. This package provides a simple R framework for sequential data assimilation based on ensemble covariances [Evensen (1994) <doi:10.1016/0167-2789(94)90130-9>]. The package includes the mean-preserving ensemble transform Kalman filter described by Ott et al. <doi:10.1111/j.1600-0870.2004.00076.x>, and also provides the simplified sensitivity-based filters described in Garcia-Pintado and Paul (2018) <doi:10.5194/gmd-11-5051-2018>.
Package details |
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Author | Javier Garcia-Pintado |
Maintainer | Javier Garcia-Pintado <jgarciapintado@marum.de> |
License | GPL (>=2) |
Version | 1.0-0 |
URL | https://github.com/garciapintado/rDAF |
Package repository | View on GitHub |
Installation |
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