Temperature prediction in the SiB2 model based on EnKF assimilation method

摘要

The Ensemble Kalman filter (EnKF) is a sequential data assimilation method, which applies an ensemble of model states to represent the error statistics of the model estimation and to predict the error statistics continuously updated in time. It has been proved to be an efficient approach to handle strong nonlinear dynamics and large state spaces. Firstly, we simulate the temperature at each layer through the thermal model, which is a sub-model of the Simple Biosphere Model (SiB2) with field observation data. Furthermore, we modify the real value by data assimilation based on EnKF and obtain another temperature curve. Finally, the real value, open loop and EnKF assimilation of the temperature at each layer are compared and analysed. The results indicate that the EnKF method is practical and efficient for dealing with soil temperature when the external condition is relatively, stable whereas it is inaccurate in a changing external environment; for instance, the assimilation cannot reflect the trend of canopy temperature.