Articles | Volume 29, issue 1
Nonlin. Processes Geophys., 29, 77–92, 2022
https://doi.org/10.5194/npg-29-77-2022
Nonlin. Processes Geophys., 29, 77–92, 2022
https://doi.org/10.5194/npg-29-77-2022

Research article 18 Feb 2022

Research article | 18 Feb 2022

Ensemble Riemannian data assimilation: towards large-scale dynamical systems

Sagar K. Tamang et al.

Model code and software

tamangsk/EnRDA: Ensemble Riemannian Data Assimilation (https://github.com/tamangsk/EnRDA) tamangsk https://doi.org/10.5281/zenodo.5047392

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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.