|Kumar, Sujay - UMD-GEST/NASA GSFC|
|Reichle, Rolf - UMD-GEST/NASA GSFC|
|Peters-Lidard, Christa - NASA GSFC|
|Koster, Randal - NASA GSFC|
|Zhan, Xiwu - NOAA NESDIS|
|Eylander, John - USAF WEATHER AGENCY|
|Houser, Paul - GEORGE MASON UNIV.|
Submitted to: Advances in Water Resources
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 1, 2008
Publication Date: October 1, 2008
Citation: Kumar, S.V., Reichle, R.H., Peters-Lidard, C.D., Koster, R.D., Zhan, X., Crow, W.T., Eylander, J.B., Houser, P.R. 2008. A land surface data assimilation framework using the land information system: Description and application. Advances in Water Resources. 31:1419-1432. Interpretive Summary: Land data assimilation systems are powerful tools for efficiently integrating remotely sensed observations into land surface models used for drought monitoring, irrigation schedule and hydrologic forecasting. However, such systems are complex and time-consuming to implement from scratch for a particular application. This paper describes the results of a multi-agency effort (including ARS) to design and test a common land data assimilation platform for scientific study and action agency implementation. Such a system in important first step in realizing the potential advantages of remote sensed observations for key land surface modeling applications.
Technical Abstract: The Land Information System (LIS) is a hydrologic modeling framework 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 interoperability of land surface models, meteorologic inputs, land surface parameters and observational data. In this work, we describe a data assimilation extension of the LIS framework that allows the incorporation and interplay of multiple sequential data assimilation algorithms, multiple observational sources and multiple land surface models. The implemented data assimilation algorithms vary in complexity, ranging from direct insertion to Ensemble Kalman Filtering (EnKF). The LIS data assimilation extension is uniquely suited to compare the assimilation of various data types in different land surface models within a single framework, which is demonstrated here with a suite of synthetic soil moisture and snow assimilation experiments. The high performance infrastructure in LIS provides adequate support to efficiently conduct the data assimilation simulations of high computational granularity.