 |
| Titel |
Implicit particle filtering for models with partial noise, and an application to geomagnetic data assimilation |
| VerfasserIn |
M. Morzfeld, A. J. Chorin |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1023-5809
|
| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 19, no. 3 ; Nr. 19, no. 3 (2012-06-19), S.365-382 |
| Datensatznummer |
250014214
|
| Publikation (Nr.) |
 copernicus.org/npg-19-365-2012.pdf |
|
|
|
|
|
| Zusammenfassung |
| Implicit particle filtering is a sequential Monte Carlo method for data
assimilation, designed to keep the number of particles manageable by
focussing attention on regions of large probability. These regions are found
by minimizing, for each particle, a scalar function F of the state
variables. Some previous implementations of the implicit filter rely on
finding the Hessians of these functions. The calculation of the Hessians can
be cumbersome if the state dimension is large or if the underlying physics
are such that derivatives of F are difficult to calculate, as happens in
many geophysical applications, in particular in models with partial noise,
i.e. with a singular state covariance matrix. Examples of models with partial
noise include models where uncertain dynamic equations are supplemented by
conservation laws with zero uncertainty, or with higher order (in time)
stochastic partial differential equations (PDE) or with PDEs driven by
spatially smooth noise processes. We make the implicit particle filter
applicable to such situations by combining gradient descent minimization with
random maps and show that the filter is efficient, accurate and reliable
because it operates in a subspace of the state space. As an example, we
consider a system of nonlinear stochastic PDEs that is of importance in
geomagnetic data assimilation. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|