feature-based data assimilation in geophysics
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ID: 240011
2018
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Abstract
Many applications in science require that computational models and data be
combined. In a Bayesian framework, this is usually done by defining
likelihoods based on the mismatch of model outputs and data. However,
matching model outputs and data in this way can be unnecessary or impossible.
For example, using large amounts of steady state data is unnecessary because
these data are redundant. It is numerically difficult to assimilate data in
chaotic systems. It is often impossible to assimilate data of a complex
system into a low-dimensional model. As a specific example, consider a
low-dimensional stochastic model for the dipole of the Earth's magnetic
field, while other field components are ignored in the model. The above
issues can be addressed by selecting features of the data, and defining
likelihoods based on the features, rather than by the usual mismatch of model
output and data. Our goal is to contribute to a fundamental understanding of
such a feature-based approach that allows us to assimilate selected aspects
of data into models. We also explain how the feature-based approach can be
interpreted as a method for reducing an effective dimension and derive new
noise models, based on perturbed observations, that lead to computationally
efficient solutions. Numerical implementations of our ideas are illustrated
in four examples.
| Reference Key |
morzfeld2018nonlinearfeature-based
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|---|---|
| Authors | ;M. Morzfeld;J. Adams;S. Lunderman;R. Orozco |
| Journal | BMC research notes |
| Year | 2018 |
| DOI |
10.5194/npg-25-355-2018
|
| URL | |
| Keywords |
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