Efficient Formulation and Implementation of Data Assimilation Methods
| dc.contributor.author | Nino-Ruiz, Elias D. | en |
| dc.contributor.author | Sandu, Adrian | en |
| dc.contributor.author | Cheng, Haiyan | en |
| dc.contributor.department | Computer Science | en |
| dc.date.accessioned | 2018-07-25T12:57:05Z | en |
| dc.date.available | 2018-07-25T12:57:05Z | en |
| dc.date.issued | 2018-07-06 | en |
| dc.date.updated | 2018-07-25T12:41:05Z | en |
| dc.description.abstract | This Special Issue presents efficient formulations and implementations of sequential and variational data assimilation methods. The methods address three important issues in the context of operational data assimilation: efficient implementation of localization methods, sampling methods for approaching posterior ensembles under non-linear model errors, and adjoint-free formulations of four dimensional variational methods. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Nino-Ruiz, E.D.; Sandu, A.; Cheng, H. Efficient Formulation and Implementation of Data Assimilation Methods. Atmosphere 2018, 9, 254. | en |
| dc.identifier.doi | https://doi.org/10.3390/atmos9070254 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/84387 | en |
| dc.language.iso | en | en |
| dc.publisher | MDPI | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | ensemble Kalman filter | en |
| dc.subject | posterior ensemble | en |
| dc.subject | modified Cholesky decomposition | en |
| dc.subject | sampling methods | en |
| dc.subject | empirical orthogonal functions | en |
| dc.subject | Gaussian mixture models | en |
| dc.title | Efficient Formulation and Implementation of Data Assimilation Methods | en |
| dc.title.serial | Atmosphere | en |
| dc.type | Editorial | en |
| dc.type.dcmitype | Text | en |