Abstract
Abstract in Undetermined
Marine operations depend on the ability to forecast suddenly appearing storms, and failures often cause great damage. As part of a sea-state alarm study, meteorological forecasts overlaid with satellite observations sent to ships have been found to be a useful too]. In this paper we present a real-time assimilation algorithm that extends this tool using statistical methods. The algorithm is applied to near-surface ocean wind fields. A Kalman filter based on a spatio-temporal state-space model provides a basis for emulation of the atmospheric model. The main contribution of this paper is the algorithm that makes it possible to use the information in the satellite observations over the full spatial domain of interest at a real-time basis.
Marine operations depend on the ability to forecast suddenly appearing storms, and failures often cause great damage. As part of a sea-state alarm study, meteorological forecasts overlaid with satellite observations sent to ships have been found to be a useful too]. In this paper we present a real-time assimilation algorithm that extends this tool using statistical methods. The algorithm is applied to near-surface ocean wind fields. A Kalman filter based on a spatio-temporal state-space model provides a basis for emulation of the atmospheric model. The main contribution of this paper is the algorithm that makes it possible to use the information in the satellite observations over the full spatial domain of interest at a real-time basis.
| Original language | English |
|---|---|
| Pages (from-to) | 319-330 |
| Journal | Environmetrics |
| Volume | 19 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2008 |
Bibliographical note
publicerad online 6 nov.2007Subject classification (UKÄ)
- Probability Theory and Statistics
Free keywords
- real-time assimilation
- spatio-temporal process
- near-surface wind fields
- Kalman filter
