Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 22
Articles | Volume 15, issue 22
https://doi.org/10.5194/gmd-15-8325-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-8325-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 22
Development and technical paper
 | 
18 Nov 2022
Development and technical paper |  | 18 Nov 2022

A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF

Shunji Kotsuki, Takemasa Miyoshi, Keiichi Kondo, and Roland Potthast

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Latest update: 20 Nov 2024
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Short summary
Data assimilation plays an important part in numerical weather prediction (NWP) in terms of combining forecasted states and observations. While data assimilation methods in NWP usually assume the Gaussian error distribution, some variables in the atmosphere, such as precipitation, are known to have non-Gaussian error statistics. This study extended a widely used ensemble data assimilation algorithm to enable the assimilation of more non-Gaussian observations.
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