Abstract:
The Land Information System (LIS; http://lis.gsfc.nasa.gov) is a hydrologic modeling system that integrates various community land surface models, ground and satellite-based observations, and high performance computing and data management tools to enable assessment and prediction of hydrologic conditions at various spatial and temporal scales. The LIS architecture is designed using advanced software engineering principles, allowing the interoperability of land surface models, meteorological inputs, land surface parameters and observational data. The capabilities for sequential data assimilation were recently implemented in LIS, enabling the use of multiple observational sources, multiple data assimilation algorithms, and multiple land surface models. In this study, we demonstrate the assimilation of land surface temperature (LST) observations into uncoupled/offline land surface models in LIS, using the Ensemble Kalman Filter (EnKF) algorithm to improve the estimates of land surface states and fluxes. Because LST estimates from land surface models and satellite retrievals have typically very different climatologies, bias correction is an important aspect of the assimilation system. In a suite of synthetic twin experiments, we evaluate and compare the performance of the assimilation system (i) without bias correction, (ii) with a static (a priori) scaling approach, and (iii) with a dynamic bias estimation algorithm that corrects the biases during the assimilation integration.