Bias corrections and prediction intervals for time-series measurements with fouling and calibration drift

Abstract. Environmental time-series measurements are subject to systematic errors from fouling and calibration drift, requiring operators to routinely check, clean, and recalibrate their sensors. Information from these checks can then be used to estimate bias corrections and prediction intervals for the measurements. If the checks are exact, the corrections and intervals have direct solutions. More realistically, the checks are noisy, and the error state is estimated with a Kalman smoother. Check information can also be pooled across sensors in a monitoring network to improve estimates for newly deployed sensors. The method relies on checks that operators already perform, and its computation and storage demands are modest, so it integrates readily into existing monitoring programs.