 |
| Titel |
A local particle filter for high dimensional geophysical systems |
| VerfasserIn |
S. G. Penny, T. Miyoshi |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
2198-5634
|
| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 2, no. 6 ; Nr. 2, no. 6 (2015-12-07), S.1631-1658 |
| Datensatznummer |
250115203
|
| Publikation (Nr.) |
 copernicus.org/npgd-2-1631-2015.pdf |
|
|
|
|
|
| Zusammenfassung |
| A local particle filter (LPF) is introduced that outperforms traditional
ensemble Kalman filters in highly nonlinear/non-Gaussian scenarios, both in
accuracy and computational cost. The standard Sampling Importance Resampling
(SIR) particle filter is augmented with an observation-space localization
approach, for which an independent analysis is computed locally at each
gridpoint. The deterministic resampling approach of Kitagawa is adapted for
application locally and combined with interpolation of the analysis weights
to smooth the transition between neighboring points. Gaussian noise is
applied with magnitude equal to the local analysis spread to prevent
particle degeneracy while maintaining the estimate of the growing dynamical
instabilities. The approach is validated against the Local Ensemble
Transform Kalman Filter (LETKF) using the 40-variable Lorenz-96 model. The
results show that: (1) the accuracy of LPF surpasses LETKF as the forecast
length increases (thus increasing the degree of nonlinearity), (2) the cost
of LPF is significantly lower than LETKF as the ensemble size increases, and
(3) LPF prevents filter divergence experienced by LETKF in cases with
non-Gaussian observation error distributions. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|