A study of Kalman filtering in atmospheric data assimilation is presented. Our research aims at an understanding of the physical and statistical mechanisms as well as the principles underlying its application to atmospheric data assimilation. Both the continuous and the discrete formulations of the filter were considered. Using nonlinear advection diffusion dynamics, a number of aspects in data assimilation were addressed, often by exploring the parameter space or by performing Monte Carlo simulations. The filtering properties, the spatial regularity and indirect inference about the model error covariance were examined with a discrete linear Kalman filter. The dynamics of the mean, variance, and correlation of forecast errors for Burgers' equation were studied. The validity of the tangent linear approximation for Burgers' equation was examined. An ensemble of realizations of the extended Kalman filter has permitted a statistical investigation of its performance, errors and limit of validity.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.41712 |
| Date | January 1994 |
| Creators | Ménard, Richard |
| Contributors | Yau, Peter M. K. (advisor), Daley, Roger (advisor) |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Doctor of Philosophy (Department of Atmospheric and Oceanic Sciences.) |
| Rights | All items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated. |
| Relation | alephsysno: 001393461, proquestno: NN94683, Theses scanned by UMI/ProQuest. |
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