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

The existence of misreported or unknownobservation times (time error) poses a unique and interesting problem forDA. Mapping observations to incorrect times causes bias in the prior stateand affects assimilation.

Algorithms that can improve the performance ofensemble Kalman filter DA in the presence of observing time error aredescribed. Algorithms that can estimate the distribution of time error arealso developed. These algorithms are then combined to produce extensions toensemble Kalman filters that can both estimate and correct for observationtime errors.