Assimilation of continuously streamed monitored data is an essential component of a digital twin. The assimilated data are then used to ensure the digital twin is a true representation of the monitored system; one way this is achieved is by calibration of simulation models, whether data-derived or physics-based. Traditional manual calibration is not time-efficient in this context; new methods are required for continuous calibration. In this paper, a particle filter methodology for continuous calibration of the physics-based model element of a digital twin is presented and applied to an example of an underground farm. The results are compared against static Bayesian calibration and are shown to give insight into the time variation of dynamically varying model parameters.