Articles | Volume 15, issue 22
https://doi.org/10.5194/gmd-15-8325-2022
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, Takemasa Miyoshi, Keiichi Kondo, and Roland Potthast

Related authors

Model Predictive Control with Foreseeing Horizon Designed to Mitigate Extreme Events in Chaotic Dynamical Systems
Fumitoshi Kawasaki, Atsushi Okazaki, Kenta Kurosawa, Tadashi Tsuyuki, and Shunji Kotsuki
EGUsphere, https://doi.org/10.5194/egusphere-2025-1785,https://doi.org/10.5194/egusphere-2025-1785, 2025
This preprint is open for discussion and under review for Nonlinear Processes in Geophysics (NPG).
Short summary
Meteorological Landscape of Tropical Cyclone
Pascal Oettli, Keita Tokuda, Yusuke Imoto, and Shunji Kotsuki
EGUsphere, https://doi.org/10.5194/egusphere-2025-1458,https://doi.org/10.5194/egusphere-2025-1458, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Observation error estimation in climate proxies with data assimilation and innovation statistics
Atsushi Okazaki, Diego Carrio, Quentin Dalaiden, Jarrah Harrison-Lofthouse, Shunji Kotsuki, and Kei Yoshimura
EGUsphere, https://doi.org/10.5194/egusphere-2025-1389,https://doi.org/10.5194/egusphere-2025-1389, 2025
Short summary
Bottom–up approach for mitigating extreme events under limited intervention options: a case study with Lorenz 96
Takahito Mitsui, Shunji Kotsuki, Naoya Fujiwara, Atsushi Okazaki, and Keita Tokuda
EGUsphere, https://doi.org/10.5194/egusphere-2025-987,https://doi.org/10.5194/egusphere-2025-987, 2025
Short summary
Bridging Data Assimilation and Control: Ensemble Model Predictive Control for High-Dimensional Nonlinear Systems
Kenta Kurosawa, Atsushi Okazaki, Fumitoshi Kawasaki, and Shunji Kotsuki
EGUsphere, https://doi.org/10.5194/egusphere-2025-595,https://doi.org/10.5194/egusphere-2025-595, 2025
Short summary

Related subject area

Atmospheric sciences
Improving winter condition simulations in SURFEX-TEB v9.0 with a multi-layer snow model and ice
Gabriel Colas, Valéry Masson, François Bouttier, Ludovic Bouilloud, Laura Pavan, and Virve Karsisto
Geosci. Model Dev., 18, 3453–3472, https://doi.org/10.5194/gmd-18-3453-2025,https://doi.org/10.5194/gmd-18-3453-2025, 2025
Short summary
UA-ICON with the NWP physics package (version ua-icon-2.1): mean state and variability of the middle atmosphere
Markus Kunze, Christoph Zülicke, Tarique A. Siddiqui, Claudia C. Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev., 18, 3359–3385, https://doi.org/10.5194/gmd-18-3359-2025,https://doi.org/10.5194/gmd-18-3359-2025, 2025
Short summary
Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025,https://doi.org/10.5194/gmd-18-3311-2025, 2025
Short summary
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025,https://doi.org/10.5194/gmd-18-3265-2025, 2025
Short summary
Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025,https://doi.org/10.5194/gmd-18-3211-2025, 2025
Short summary

Cited articles

Acevedo, W., Fallah, B., Reich, S., and Cubasch, U.: Assimilation of pseudo-tree-ring-width observations into an atmospheric general circulation model, Clim. Past, 13, 545–557, https://doi.org/10.5194/cp-13-545-2017, 2017. 
Ades, M. and van Leeuwen, P. J.: An exploration of the equivalent weights particle filter, Q. J. Roy. Meteor. Soc., 139, 820–840, https://doi.org/10.1002/qj.1995, 2013. 
Ades, M. and van Leeuwen, P. J.: The equivalent-weights particle filter in a high-dimensional system, Q. J. Roy. Meteor. Soc., 141, 484–503, https://doi.org/10.1002/qj.2370, 2015. 
Anderson, J. L.: An Ensemble Adjustment Kalman Filter for Data Assimilation, Mon. Weather Rev., 129, 2884–2903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2, 2001. 
Anderson, J. L. and Anderson, S. L.: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758, https://doi.org/10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2, 1999. 
Download
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.
Share