Articles | Volume 15, issue 22
Geosci. Model Dev., 15, 8325–8348, 2022
Geosci. Model Dev., 15, 8325–8348, 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.

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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,, 2017. 
Ades, M. and van Leeuwen, P. J.: An exploration of the equivalent weights particle filter, Q. J. Roy. Meteor. Soc., 139, 820–840,, 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,, 2015. 
Anderson, J. L.: An Ensemble Adjustment Kalman Filter for Data Assimilation, Mon. Weather Rev., 129, 2884–2903,<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,<2741:AMCIOT>2.0.CO;2, 1999. 
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.