A Multi-setting Ensemble Land Surface Modeling Framework for Assimilating Satellite Vegetation Observations

Land data assimilation of satellite-based observations is proved in many studies to be a feasible approach for optimizing land surface models (LSMs). However, uncertainties in the LSM are often underestimated when a single model setting is adopted. In this study, a multi-setting ensemble land data assimilation system is developed based on different parameterization schemes in the Noah LSM with multi-parameterization options (Noah-MP). Specifically, remotely sensed Leaf Area Index (LAI) observations are assimilated into Noah-MP with the dynamic vegetation component. Multiple Noah-MP settings are combined together into an ensemble modeling system. The performance of the multi-setting system is compared to the individual Noah-MP runs based on single setting across select regions characterized by different types of land cover and climate. The model outputs are evaluated in terms of LAI, soil moisture, and evapotranspiration against observation and reanalysis data sets.