Model state and parameter estimation by assimilation of temperature profiles with an ensemble Kalman filter in North Sea applications
(2012)
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- Authors
- Supervisors
- Deleersnijder, Eric
- Abstract
- (en) Coastal management and maritime safety strongly rely on accurate representations of the sea state. Both dynamical models and observations provide abundant pieces of information. However, none of them provides the complete picture. The assimilation of data into models is one way to improve our knowledge of the ocean state. Its application in coastal models remains challenging because of the wide range of temporal and spatial variabilities of the processes. This dissertation investigates the assimilation of temperature profiles with an ensemble Kalman filter in 1-D and 3-D North Sea simulations. Two approaches of data assimilation are considered: model state estimation and combined model state and parameter estimation. In model state estimation, adequate parameters for model error sampling are first determined using a square root algorithm that assumes observations to be perfect. Then, a similar but more realistic algorithm able to account for observations' errors (low rank square root algorithm) is applied. A detailed assessment of the benefit of data assimilation on temperature is carried out, including a comparison against independent observations. For combined model state and parameter estimation the low rank square root algorithm is applied to a set of model parameters primarily involved in the representation of the temperature of the upper mixed layer. They are the optical attenuation and the surface heat exchange coefficients. Moreover, the spatial variability of the parameters' estimation is examined at two stations typical of the hydrodynamical regimes of the North Sea in summer.
- Affiliations
Citations
Ponsar, S. (2012). Model state and parameter estimation by assimilation of temperature profiles with an ensemble Kalman filter in North Sea applications. https://hdl.handle.net/2078.5/160192