Development and preliminary validation of a land surface image assimilation system based on the common land model

Abstract. Data assimilation is an essential approach to improve the predictions of land surface models.​ Due to the characteristics of single-column models, assimilation of land surface information has mostly focused on improving the assimilation of single-point variables. However, land surface variables affect short-term climate more through large-scale anomalous forcing, so it is indispensable to pay attention to the accuracy of the anomalous spatial structure of land surface variables. In this study, a land surface image assimilation system capable of optimizing the spatial structure of the background field is constructed by introducing the curvelet analysis method and taking the similarity of image structure as a weak constraint. The ERA5_Land soil moisture reanalysis data are used as ideal observation for the preliminary effectiveness validation of the image assimilation system. ​The results show that the new image assimilation system is able to well absorb the spatial structure information of the observed data and has a remarkable ability to adjust the spatial structure of soil moisture in the land model.​ ​The spatial correlation coefficient between model surface soil moisture and observation has increased from 0.39 to about 0.67 after assimilation. By assimilating the surface soil moisture data and combining with the model physical processes, the image assimilation system can also gradually improve the spatial structure of deep soil moisture, with the spatial correlation coefficient between model soil moisture and observation increased from 0.35 to about 0.57. The forecast results show that the positive assimilation effect could be maintained for more than 30 days. ​The results of this study adequately demonstrate the application potential of image assimilation system in the short-term climate prediction.