Articles | Volume 28, issue 3
https://doi.org/10.5194/npg-28-295-2021
© Author(s) 2021. 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-28-295-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Jul 2021
Research article |
| 06 Jul 2021
| 06 Jul 2021
Ensemble Riemannian data assimilation over the Wasserstein space
Department of Civil, Environmental, and Geo-Engineering, University of Minnesota – Twin Cities, Twin Cities, Minnesota, USA
St. Anthony Falls Laboratory, University of Minnesota – Twin Cities, Twin Cities, Minnesota, USA
Ardeshir Ebtehaj
CORRESPONDING AUTHOR
Department of Civil, Environmental, and Geo-Engineering, University of Minnesota – Twin Cities, Twin Cities, Minnesota, USA
St. Anthony Falls Laboratory, University of Minnesota – Twin Cities, Twin Cities, Minnesota, USA
Peter J. van Leeuwen
Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
Dongmian Zou
Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, China
Gilad Lerman
School of Mathematics, University of Minnesota – Twin Cities, Twin Cities, Minnesota, USA
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Short summary
Data assimilation aims to improve hydrologic and weather forecasts by combining available information from Earth system models and observations. The classical approaches to data assimilation usually proceed with some preconceived assumptions about the shape of their probability distributions. As a result, when such assumptions are invalid, the forecast accuracy suffers. In the proposed methodology, we relax such assumptions and demonstrate improved performance.
Data assimilation aims to improve hydrologic and weather forecasts by combining available...
