Assimilation of near-surface temperature using extended Kalman filter

In order to improve the soil temperature profile predictions in land-surface models, an assimilation scheme using the extended Kalman filter is developed. This formulation is based on the discretized diffusion equation of heat transfer through the soil column. The scheme is designed to incorporate the knowledge of the uncertainties in both the model and the measurement. Model uncertainty is estimated by quantifying the model drift from observations when the model is initialized using the observed values. Furthermore, the initial error covariance has a significant influence on the performance of the assimilation scheme. It is shown that an inaccurate initial value for the error covariance can actually diminish the predictive capabilities of the model. When an appropriate initial error covariance is specified, using the top layer soil temperature observations in the assimilation scheme allows for improved predictive capabilities in lower layers. Observations at 30 min intervals have a significant effect on the model predictions in the lower layers. Assimilation of observations at 24 h intervals also has an effect on the lower layer predictive capability of the model, albeit more slowly than the 30 min assimilation scenario.