Cluster Sampling Filters for Non-Gaussian Data Assimilation
| dc.contributor.author | Attia, A. | en |
| dc.contributor.author | Moosavi, Azam | en |
| dc.contributor.author | Sandu, Adrian | en |
| dc.contributor.department | Computer Science | en |
| dc.date.accessioned | 2017-03-06T18:31:25Z | en |
| dc.date.available | 2017-03-06T18:31:25Z | en |
| dc.date.issued | 2016-08-19 | en |
| dc.description.abstract | This paper presents a fully non-Gaussian version of the Hamiltonian Monte Carlo (HMC) sampling filter. The Gaussian prior assumption in the original HMC filter is relaxed. Specifically, a clustering step is introduced after the forecast phase of the filter, and the prior density function is estimated by fitting a Gaussian Mixture Model (GMM) to the prior ensemble. Using the data likelihood function, the posterior density is then formulated as a mixture density, and is sampled using a HMC approach (or any other scheme capable of sampling multimodal densities in high-dimensional subspaces). The main filter developed herein is named "cluster HMC sampling filter" (ClHMC). A multi-chain version of the ClHMC filter, namely MC-ClHMC is also proposed to guarantee that samples are taken from the vicinities of all probability modes of the formulated posterior. The new methodologies are tested using a quasi-geostrophic (QG) model with double-gyre wind forcing and bi-harmonic friction. Numerical results demonstrate the usefulness of using GMMs to relax the Gaussian prior assumption in the HMC filtering paradigm. | en |
| dc.identifier.uri | http://hdl.handle.net/10919/75260 | en |
| dc.language.iso | en | en |
| dc.relation.uri | http://arxiv.org/abs/1607.03592v2 | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | stat.CO | en |
| dc.subject | cs.NA | en |
| dc.subject | stat.AP | en |
| dc.title | Cluster Sampling Filters for Non-Gaussian Data Assimilation | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |
| pubs.organisational-group | /Virginia Tech | en |
| pubs.organisational-group | /Virginia Tech/All T&R Faculty | en |
| pubs.organisational-group | /Virginia Tech/Engineering | en |
| pubs.organisational-group | /Virginia Tech/Engineering/COE T&R Faculty | en |
| pubs.organisational-group | /Virginia Tech/Engineering/Computer Science | en |