Effectively assimilate satellite land surface temperature into offline land surface models within ensemble-based assimilation frameworks

Land surface temperature (LST) plays a vital role in controlling the water and energy fluxes at the interface between the land and atmosphere, and the main aim of assimilating LST observations into Land Surface Models (LSMs) is to not only provide better initial conditions for the LSM itself, but also yield more accurate land–atmosphere interactions. While observation systems provide a vast amount of satellite-derived LST observations in recent years, they are not as widely used as soil-moisture observations in land data assimilation (DA), in research or in operations, owing to the fast temporally varying nature of LST. This study proposes a new scheme to effectively improve the impact of LST assimilation by simultaneously updating soil temperature and soil moisture within the upper soil layers. The proposed scheme outperforms the conventional strategy of updating temperature only, and it also yields significant benefits for hydrological variables such as soil moisture and snow. The DA method and LSM used in this study are the Local Ensemble Transform Kalman Filter (LETKF) and the Common Land Model (CoLM), respectively. Moderate Resolution Imaging Spectroradiometer (MODIS) derived LST is assimilated into CoLM every 3 h using the proposed scheme. The assimilation and open-loop experiments are conducted for one year with a global resolution of 0.5 ° ×0.5 ° . The LST shows marginal enhancement after assimilation, owing to its fast-varying nature dominated by atmospheric forcings. However, the BIAS in soil temperature over Northeast Asia is reduced significantly, with a magnitude of 1.0, 1.5, and 2.0 K for the layers within 0–10, 40–100, and 100–200 cm, respectively. Prominent improvements in snow temperature and snow depth are observed over Northeast Asia, with a reduction in root mean square difference (RMSD) of approximately 4 K and 150 mm, respectively. The improvements in soil water content are also notable, particularly over humid tropical regions. The largest reductions in unbiased RMSD of soil water content over the Amazon Rainforest are approximately 0.06, 0.12, 0.15, and 6.00 kg m −2 for the layers within 0–10, 10–40, 40–100, and 100–200 cm, respectively. These consistent improvements in both the energy and water components of CoLM demonstrate the effectiveness of the proposed scheme and the importance of LST assimilation for land-surface-process modeling.