Articles | Volume 21, issue 5
https://doi.org/10.5194/npg-21-955-2014
https://doi.org/10.5194/npg-21-955-2014
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, X. Yi, L. Wang, X. Liang, S. Zhang, X. Zhang, and X. Zheng

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, https://doi.org/10.5194/hess-24-5187-2020,https://doi.org/10.5194/hess-24-5187-2020, 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, https://doi.org/10.5194/npg-24-329-2017,https://doi.org/10.5194/npg-24-329-2017, 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, https://doi.org/10.5194/gmd-8-805-2015,https://doi.org/10.5194/gmd-8-805-2015, 2015
Short summary

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
A range of outcomes: the combined effects of internal variability and anthropogenic forcing on regional climate trends over Europe
Clara Deser and Adam S. Phillips
Nonlin. Processes Geophys., 30, 63–84, https://doi.org/10.5194/npg-30-63-2023,https://doi.org/10.5194/npg-30-63-2023, 2023
Short summary
Extending ensemble Kalman filter algorithms to assimilate observations with an unknown time offset
Elia Gorokhovsky and Jeffrey L. Anderson
Nonlin. Processes Geophys., 30, 37–47, https://doi.org/10.5194/npg-30-37-2023,https://doi.org/10.5194/npg-30-37-2023, 2023
Short summary
Guidance on how to improve vertical covariance localization based on a 1000-member ensemble
Tobias Necker, David Hinger, Philipp Johannes Griewank, Takemasa Miyoshi, and Martin Weissmann
Nonlin. Processes Geophys., 30, 13–29, https://doi.org/10.5194/npg-30-13-2023,https://doi.org/10.5194/npg-30-13-2023, 2023
Short summary
Weather pattern dynamics over western Europe under climate change: predictability, information entropy and production
Stéphane Vannitsem
Nonlin. Processes Geophys., 30, 1–12, https://doi.org/10.5194/npg-30-1-2023,https://doi.org/10.5194/npg-30-1-2023, 2023
Short summary
Using a hybrid optimal interpolation–ensemble Kalman filter for the Canadian Precipitation Analysis
Dikraa Khedhaouiria, Stéphane Bélair, Vincent Fortin, Guy Roy, and Franck Lespinas
Nonlin. Processes Geophys., 29, 329–344, https://doi.org/10.5194/npg-29-329-2022,https://doi.org/10.5194/npg-29-329-2022, 2022
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.