12 Jul 2022
12 Jul 2022

Extending Ensemble Kalman Filter Algorithms to Assimilate Observations with an Unknown Time Offset

Elia Gorokhovsky1,a and Jeffrey L. Anderson1 Elia Gorokhovsky and Jeffrey L. Anderson
  • 1National Center for Atmospheric Research, Boulder, CO, USA
  • acurrent affiliation: California Institute of Technology, Pasadena, CA, USA

Abstract. Data assimilation (DA), the statistical combination of computer models with measurements, is applied in a variety of scientific fields involving forecasting of dynamical systems, most prominently in atmospheric and ocean sciences. The existence of misreported or unknown observation times (time error) poses a unique and interesting problem for DA. Mapping observations to incorrect times causes bias in the prior state and affects assimilation. Algorithms that can improve the performance of ensemble Kalman filter DA in the presence of observing time error are described. Algorithms that can estimate the distribution of time error are also developed. These algorithms are then combined to produce extensions to ensemble Kalman filters that can both estimate and correct for observation time errors. A low-order dynamical system is used to evaluate the performance of these methods for a range of magnitudes of observation time error. The most successful algorithms must explicitly account for the nonlinearity in the evolution of the prediction model.

Elia Gorokhovsky and Jeffrey L. Anderson

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-536', Anonymous Referee #1, 16 Aug 2022
  • RC2: 'Comment on egusphere-2022-536', Anonymous Referee #2, 20 Aug 2022
  • AC1: 'Comment on egusphere-2022-536', Elia Gorokhovsky, 19 Oct 2022

Elia Gorokhovsky and Jeffrey L. Anderson

Elia Gorokhovsky and Jeffrey L. Anderson


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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.