.png){kind=icon}
Return
A data assimilation method for log-normally distributed observational errors
.png){kind=icon}
PRE
AI
DOI：10.1256/qj.05.222.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Hessian
Jacobian
preconditioner
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.9
Papers:
5.8K
Citations:
2.4W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSave
ShareSave
ShareSaveShareSave
View more