Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 17
Articles | Volume 19, issue 17
https://doi.org/10.5194/gmd-19-8321-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-8321-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 17
Model description paper
 | 
09 Sep 2026
Model description paper |  | 09 Sep 2026

A novel Gauss-Hermite High-Order Sampling Hybrid ensemble filter for computationally efficient data assimilation in geosciences – Part 1: Application to Lorenz-96 in PythonDA v1.2.2

Simone Spada, Anna Teruzzi, Stefano Maset, Stefano Salon, Cosimo Solidoro, and Gianpiero Cossarini

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Short summary
In geosciences, data assimilation (DA) combines modeled dynamics and observations to reduce simulation uncertainties. Uncertainties can be dynamically and effectively estimated in ensemble DA methods. With respect to current techniques, the novel Gauss-Hermite High-Order Sampling Hybrid filter (GHOSH) ensemble DA scheme is designed to improve accuracy by reaching a higher approximation order, without increasing computational costs, as demonstrated in idealized Lorenz96 tests.
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