Articles | Volume 21, issue 5
Nonlin. Processes Geophys., 21, 955–970, 2014
Nonlin. Processes Geophys., 21, 955–970, 2014

Research article 23 Sep 2014

Research article | 23 Sep 2014

Improving the ensemble transform Kalman filter using a second-order Taylor approximation of the nonlinear observation operator

G. Wu et al.

Related authors

Assimilating shallow soil moisture observations into land models with a water budget constraint
Bo Dan, Xiaogu Zheng, Guocan Wu, and Tao Li
Hydrol. Earth Syst. Sci., 24, 5187–5201,,, 2020
Short summary
An estimate of the inflation factor and analysis sensitivity in the ensemble Kalman filter
Guocan Wu and Xiaogu Zheng
Nonlin. Processes Geophys., 24, 329–341,,, 2017
Short summary
A global carbon assimilation system using a modified ensemble Kalman filter
S. Zhang, X. Zheng, J. M. Chen, Z. Chen, B. Dan, X. Yi, L. Wang, and G. Wu
Geosci. Model Dev., 8, 805–816,,, 2015
Short summary

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
Fast hybrid tempered ensemble transform filter formulation for Bayesian elliptical problems via Sinkhorn approximation
Sangeetika Ruchi, Svetlana Dubinkina, and Jana de Wiljes
Nonlin. Processes Geophys., 28, 23–41,,, 2021
Short summary
A methodology to obtain model-error covariances due to the discretization scheme from the parametric Kalman filter perspective
Olivier Pannekoucke, Richard Ménard, Mohammad El Aabaribaoune, and Matthieu Plu
Nonlin. Processes Geophys., 28, 1–22,,, 2021
Short summary
A method for predicting the uncompleted climate transition process
Pengcheng Yan, Guolin Feng, Wei Hou, and Ping Yang
Nonlin. Processes Geophys., 27, 489–500,,, 2020
Short summary
Statistical postprocessing of ensemble forecasts for severe weather at Deutscher Wetterdienst
Reinhold Hess
Nonlin. Processes Geophys., 27, 473–487,,, 2020
Short summary
Training a convolutional neural network to conserve mass in data assimilation
Yvonne Ruckstuhl, Tijana Janjić, and Stephan Rasp
Nonlin. Processes Geophys. Discuss.,,, 2020
Revised manuscript accepted for NPG
Short summary

Cited articles

Anderson, J. L.: An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus A, 59, 210–224, 2007.
Anderson, J. L.: Spatially and temporally varying adaptive covariance inflation for ensemble filters, Tellus A, 61, 72–83, 2009.
Anderson, J. L. and Anderson, S. L.: A Monte Carlo implementation of the non-linear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758, 1999.
Bishop, C. H. and Toth, Z.: Ensemble transformation and adaptive observations, J. Atmos. Sci., 56, 1748–1765, 1999.
Bishop, C. H., Etherton, J., and Majumdar, J.: Adaptive sampling with the ensemble transform Kalman filte. Part I: Theoretical aspects, Mon. Weather Rev., 129, 420–436, 2001.