Articles | Volume 15, issue 22
Geosci. Model Dev., 15, 8325–8348, 2022
https://doi.org/10.5194/gmd-15-8325-2022
Geosci. Model Dev., 15, 8325–8348, 2022
https://doi.org/10.5194/gmd-15-8325-2022
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 et al.

Data sets

Processed data and scripts for visualization of Kotsuki et al. (2022; GMDD) Shunji Kotsuki https://doi.org/10.5281/zenodo.6586309

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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.