Ensemble Kalman filter for nonconservative moving mesh solvers with a joint physics and mesh location update-Reference-Cited by-同舟云学术

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Ensemble Kalman filter for nonconservative moving mesh solvers with a joint physics and mesh location update

Published:2021-02-23 Issue:736 Volume:147 Page:1539-1561
ISSN:0035-9009
Container-title:Quarterly Journal of the Royal Meteorological Society
language:en
Short-container-title:Q.J.R. Meteorol. Soc

Author:

Sampson Christian¹^ORCID,Carrassi Alberto²³,Aydoğdu Ali⁴^ORCID,Jones Chris K.R.T¹

Affiliation:

1. University of North Carolina Chapel Hill North Carolina USA

2. Department of Meteorology and National Centre for Earth Observations University of Reading Reading UK

3. Mathematical Institute University of Utrecht Utrecht the Netherlands

4. Centro Euro‐Mediterraneo sui Cambiamenti Climatici Bologna Italy

Funder

Office of Naval Research

Publisher

Wiley

Subject

Atmospheric Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3980

Reference30 articles.

1. A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts

2. Data Assimilation

3. Data assimilation using adaptive, non-conservative, moving mesh models

4. Four-dimensional ensemble variational data assimilation and the unstable subspace

5. Data assimilation for moving mesh methods with an application to ice sheet modelling

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3. Ensemble Kalman filter for GAN-ConvLSTM based long lead-time forecasting;Journal of Computational Science;2023-05

4. Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020;The Cryosphere;2023-04-25

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