Articles | Volume 30, issue 1
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

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Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
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Cited articles

Amrhein, D. E.: How large are temporal representativeness errors in paleoclimatology?, Clim. Past, 16, 325–340,, 2020. 
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.: A local least squares framework for ensemble filtering, Mon. Weather Rev., 131, 634–642,<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],, 2023. 
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.