Model error in data assimilation

Research output: Chapter in Book/Report/Conference proceedingChapter

13 Scopus citations

Abstract

This chapter provides various perspectives on an important challenge in data assimilation: model error. While the overall goal is to understand the implication of model error of any type in data assimilation, we emphasize on the effect of model error from unresolved scales. In particular, connection to related subjects under different names in applied mathematics, such as the Mori-Zwanzig formalism and the averaging method, was discussed with the hope that the existing methods can be more accessible and eventually be used appropriately. We will classify existing methods into two groups: the statistical methods for those who directly estimate the low-order model error statistics; and the stochastic parameterizations for those who implicitly estimate all statistics by imposing stochastic models beyond the traditional unbiased white noise Gaussian processes. We will provide theory to justify why stochastic parameterization, as one of the main theme in this book, is an adequate tool for mitigating model error in data assimilation. Finally, we will also discuss challenges in lifting this approach in general applications and provide an alternative nonparametric approach.

Original languageEnglish (US)
Title of host publicationNonlinear and Stochastic Climate Dynamics
PublisherCambridge University Press
Pages276-317
Number of pages42
ISBN (Electronic)9781316339251
ISBN (Print)9781107118140
DOIs
StatePublished - Jan 19 2017

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)

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    Harlim, J. (2017). Model error in data assimilation. In Nonlinear and Stochastic Climate Dynamics (pp. 276-317). Cambridge University Press. https://doi.org/10.1017/9781316339251.011