Articles | Volume 30, issue 1
https://doi.org/10.5194/npg-30-37-2023
https://doi.org/10.5194/npg-30-37-2023
Research article
 | 
07 Feb 2023
Research article |  | 07 Feb 2023

Extending ensemble Kalman filter algorithms to assimilate observations with an unknown time offset

Elia Gorokhovsky and Jeffrey L. Anderson

Related authors

Advantages of assimilating multispectral satellite retrievals of atmospheric composition: a demonstration using MOPITT carbon monoxide products
Wenfu Tang, Benjamin Gaubert, Louisa Emmons, Daniel Ziskin, Debbie Mao, David Edwards, Avelino Arellano, Kevin Raeder, Jeffrey Anderson, and Helen Worden
Atmos. Meas. Tech., 17, 1941–1963, https://doi.org/10.5194/amt-17-1941-2024,https://doi.org/10.5194/amt-17-1941-2024, 2024
Short summary
Bounded and categorized: targeting data assimilation for sea ice fractional coverage and non-negative quantities in a single column multi-category sea ice model
Molly Wieringa, Christopher Riedel, Jeffrey Anderson, and Cecilia Bitz
EGUsphere, https://doi.org/10.5194/egusphere-2023-2016,https://doi.org/10.5194/egusphere-2023-2016, 2023
Short summary
Exploring Non-Gaussian Sea Ice Characteristics via Observing System Simulation Experiments
Christopher Riedel and Jeffrey Anderson
EGUsphere, https://doi.org/10.5194/egusphere-2023-96,https://doi.org/10.5194/egusphere-2023-96, 2023
Short summary
The potential for geostationary remote sensing of NO2 to improve weather prediction
Xueling Liu, Arthur P. Mizzi, Jeffrey L. Anderson, Inez Fung, and Ronald C. Cohen
Atmos. Chem. Phys., 21, 9573–9583, https://doi.org/10.5194/acp-21-9573-2021,https://doi.org/10.5194/acp-21-9573-2021, 2021
Short summary
Estimating parameters in a sea ice model using an ensemble Kalman filter
Yong-Fei Zhang, Cecilia M. Bitz, Jeffrey L. Anderson, Nancy S. Collins, Timothy J. Hoar, Kevin D. Raeder, and Edward Blanchard-Wrigglesworth
The Cryosphere, 15, 1277–1284, https://doi.org/10.5194/tc-15-1277-2021,https://doi.org/10.5194/tc-15-1277-2021, 2021
Short summary

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
Evolution of small-scale turbulence at large Richardson numbers
Lev Ostrovsky, Irina Soustova, Yuliya Troitskaya, and Daria Gladskikh
Nonlin. Processes Geophys., 31, 219–227, https://doi.org/10.5194/npg-31-219-2024,https://doi.org/10.5194/npg-31-219-2024, 2024
Short summary
Improving Ensemble Data Assimilation through Probit-space Ensemble Size Expansion for Gaussian Copulas (PESE-GC)
Man-Yau Chan
EGUsphere, https://doi.org/10.5194/egusphere-2023-2699,https://doi.org/10.5194/egusphere-2023-2699, 2023
Short summary
How far can the statistical error estimation problem be closed by collocated data?
Annika Vogel and Richard Ménard
Nonlin. Processes Geophys., 30, 375–398, https://doi.org/10.5194/npg-30-375-2023,https://doi.org/10.5194/npg-30-375-2023, 2023
Short summary
Using orthogonal vectors to improve the ensemble space of the ensemble Kalman filter and its effect on data assimilation and forecasting
Yung-Yun Cheng, Shu-Chih Yang, Zhe-Hui Lin, and Yung-An Lee
Nonlin. Processes Geophys., 30, 289–297, https://doi.org/10.5194/npg-30-289-2023,https://doi.org/10.5194/npg-30-289-2023, 2023
Short summary
Review article: Towards strongly coupled ensemble data assimilation with additional improvements from machine learning
Eugenia Kalnay, Travis Sluka, Takuma Yoshida, Cheng Da, and Safa Mote
Nonlin. Processes Geophys., 30, 217–236, https://doi.org/10.5194/npg-30-217-2023,https://doi.org/10.5194/npg-30-217-2023, 2023
Short summary

Cited articles

Amrhein, D. E.: How large are temporal representativeness errors in paleoclimatology?, Clim. Past, 16, 325–340, https://doi.org/10.5194/cp-16-325-2020, 2020. 
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.: A local least squares framework for ensemble filtering, Mon. Weather Rev., 131, 634–642, https://doi.org/10.1175/1520-0493(2003)131<0634:Allsff>2.0.Co;2, 2003. 
Anderson, J. L.: Data and code used to generate figures in Gorokhovsky and Anderson, Zenodo [code and data set], https://doi.org/10.5281/zenodo.7576692, 2023. 
Download
Short summary
Older observations of the Earth system sometimes lack information about the time they were taken, posing problems for analyses of past climate. To begin to ameliorate this problem, we propose new methods of varying complexity, including methods to estimate the distribution of the offsets between true and reported observation times. The most successful method accounts for the nonlinearity in the system, but even the less expensive ones can improve data assimilation in the presence of time error.