HR: 11:05h
AN: H32A-04    [Abstracts]
TI: An Integrated System for Sequential Hydrologic Data Assimilation using the Land Information System
AU: * Kumar, S V
EM: sujay@hsb.gsfc.nasa.gov
AF: University of Maryland, Baltimore County/ NASA GSFC, Code 614.3, NASA GSFC, Greenbelt, MD 20771, United States
AU: Reichle, R
EM: reichle@gmao.gsfc.nasa.gov
AF: University of Maryland Baltimore county/NASA GSFC, GMAO/NASA GSFC, Greenbelt, MD 20771, United States
AU: Peters-Lidard, C
EM: cpeters@hsb.gsfc.nasa.gov
AF: NASA GSFC, Code 614.3, NASA GSFC, Greenbelt, MD 20771, United States
AU: Koster, R
EM: koster@janus.gsfc.nasa.gov
AF: NASA GSFC, GMAO, NASA GSFC, Greenbelt, MD 20771, United States
AB: 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. Recently, the LIS framework has been enhanced by developing an interoperable extension for sequential data assimilation, thereby providing a comprehensive framework that can integrate data assimilation techniques, hydrologic models, observations, and the required computing infrastructure. The extensible LIS data assimilation framework allows the incorporation and interplay of multiple observational sources, multiple data assimilation algorithms, and multiple land surface models. These capabilities are demonstrated using a suite of observing system simulation experiments (OSSEs) that assimilate different sources of observational data into different land surface models to propagate observational information in space and time using assimilation algorithms with varying complexity ranging from rule-based approaches to ensemble Kalman Filtering (EnKF). The assimilation of soil moisture, snow cover, and snow water equivalent data is demonstrated using the Noah and Catchment land surface models using a number of sequential assimilation algorithms. These experiments illustrate the sensitivity of model parameterizations and physical representations on the efficiency of the assimilation process and the relative merits of the assimilation approaches. Further, the system also provides an infrastructure to diagnose the consequences of assumptions on model and observation error properties on the accuracy of assimilated products. These experiments demonstrate the use of LIS data assimilation framework as an ideal testbed for development and evaluation of techniques in hydrologic data assimilation.
DE: 1847 Modeling
DE: 1855 Remote sensing (1640)
DE: 1899 General or miscellaneous
DE: 3315 Data assimilation
SC: Hydrology [H]
MN: 2007 Joint Assembly