This thesis addresses data assimilation, which typically refers to the estimation of the state of a physical system given a model and observations, and its application to short-term precipitation forecasting. A general introduction to data assimilation is given, both from a deterministic and stochastic point of view. Data assimilation algorithms are reviewed, in the static case (when no dynamics are involved), then in the dynamic case. A double experiment on two non-linear models, the Lorenz 63 and the Lorenz 96 models, is run and the comparative performance of the methods is discussed in terms of quality of the assimilation, robustness in the non-linear regime and computational time.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:502434 |
| Date | January 2008 |
| Creators | Barillec, Remi Louis |
| Publisher | Aston University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://publications.aston.ac.uk/15276/ |
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