Articles | Volume 29, issue 1
https://doi.org/10.5194/npg-29-77-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-29-77-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Feb 2022
Research article |
| 18 Feb 2022
| 18 Feb 2022
Ensemble Riemannian data assimilation: towards large-scale dynamical systems
Department of Civil, Environmental and Geo-Engineering and Saint Anthony Falls Laboratory, University of Minnesota-Twin Cities, Twin Cities, Minnesota, USA
Ardeshir Ebtehaj
Department of Civil, Environmental and Geo-Engineering and Saint Anthony Falls Laboratory, University of Minnesota-Twin Cities, Twin Cities, Minnesota, USA
Peter Jan van Leeuwen
Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
Gilad Lerman
School of Mathematics, University of Minnesota-Twin Cities, Twin Cities, Minnesota, USA
Efi Foufoula-Georgiou
Department of Civil and Environmental Engineering and Department of Earth System Science, University of California Irvine, Irvine, California, USA
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Short summary
The outputs from Earth system models are optimally combined with satellite observations to produce accurate forecasts through a process called data assimilation. Many existing data assimilation methodologies have some assumptions regarding the shape of the probability distributions of model output and observations, which results in forecast inaccuracies. In this paper, we test the effectiveness of a newly proposed methodology that relaxes such assumptions about high-dimensional models.
The outputs from Earth system models are optimally combined with satellite observations to...
