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Ensemble data assimilation without ensembles: methodology and application to ocean data assimilation
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Keppenne, Christian L. Rienecker, Michele M. Vernieres, Guillaume Kovach, Robin M. |
| Copyright Year | 2013 |
| Description | Two methods to estimate background error covariances for data assimilation are introduced. While both share properties with the ensemble Kalman filter (EnKF), they differ from it in that they do not require the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The first method is referred-to as SAFE (Space Adaptive Forecast error Estimation) because it estimates error covariances from the spatial distribution of model variables within a single state vector. It can thus be thought of as sampling an ensemble in space. The second method, named FAST (Flow Adaptive error Statistics from a Time series), constructs an ensemble sampled from a moving window along a model trajectory. The underlying assumption in these methods is that forecast errors in data assimilation are primarily phase errors in space and/or time. |
| File Size | 139698 |
| Page Count | 15 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_20140011280 |
| Archival Resource Key | ark:/13960/t4wh7k70z |
| Language | English |
| Publisher Date | 2013-01-01 |
| Access Restriction | Open |
| Subject Keyword | Data Assimilation Inverse Modeling Kalman Filter Assimilation Interpolation Ocean Models Error Analysis Spatial Distribution Covariance Time Series Analysis Annual Variations Temperature Profiles Kalman Filters Complex Systems Salinity Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
