Variational data assimilation for parameter estimation: application to a simple morphodynamic model |
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Authors: | Polly J Smith Sarah L Dance Michael J Baines Nancy K Nichols Tania R Scott |
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Institution: | (1) Department of Mathematics, University of Reading, Reading, RG6 6AX, UK;(2) Environmental Systems Science Centre, University of Reading, Reading, RG6 6AL, UK |
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Abstract: | Data assimilation is a sophisticated mathematical technique for combining observational data with model predictions to produce
state and parameter estimates that most accurately approximate the current and future states of the true system. The technique
is commonly used in atmospheric and oceanic modelling, combining empirical observations with model predictions to produce
more accurate and well-calibrated forecasts. Here, we consider a novel application within a coastal environment and describe
how the method can also be used to deliver improved estimates of uncertain morphodynamic model parameters. This is achieved
using a technique known as state augmentation. Earlier applications of state augmentation have typically employed the 4D-Var,
Kalman filter or ensemble Kalman filter assimilation schemes. Our new method is based on a computationally inexpensive 3D-Var
scheme, where the specification of the error covariance matrices is crucial for success. A simple 1D model of bed-form propagation
is used to demonstrate the method. The scheme is capable of recovering near-perfect parameter values and, therefore, improves
the capability of our model to predict future bathymetry. Such positive results suggest the potential for application to more
complex morphodynamic models. |
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