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A data assimilation method for log-normally distributed observational errors
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DOI：10.1256/qj.05.222.png)
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In this paper we change the standard assumption made in the Bayesian framework of variational data assimilation to allow for observational errors that are log-normally distributed. We address the question of which statistic best describes the distribution for the univariate and multivariate cases to justify our choice of the mode. From this choice we derive the associated cost function, Jacobian and Hessian with a normal background. We also find the solution to the Jacobian equal to zero in both model and observational space. Given the Hessian that we derive, we define a preconditioner to aid in the minimization of the cost function. We extend this to define a general form for the preconditioner, given a certain type of cost function.
Keyword:
Hessian
Jacobian
preconditioner
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