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Title: IMPLEMENTATION AND APPLICATION OF THE KALMAN FILTER DATA ASSIMILATION APPROACHES IN NASA'S LAND INFORMATION SYSTEM INFRASTRUCTURE

Author
item Zhan, Xiwu
item KUMAR, S - NASA GSFC
item Crow, Wade
item ARSENAULT, K - NASA GSFC
item HOUSER, P - GEORGE MASON UNIVERSITY
item PETERS-LIDARD, C - NASA GSFC

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: 5/1/2006
Publication Date: 6/1/2006
Citation: Zhan, X., Kumar, S.V., Crow, W.T., Arsenault, K., Houser, P., Peters-Lidard, C. 2006. Implementation and application of the Kalman Filter data assimilation approaches in NASA's Land Information System Infrastructure [abstract]. EOS Transactions, American Geophysical Union. 87(36), Joint Meeting Supplement, Abstract H31A-01.

Interpretive Summary:

Technical Abstract: Building on the North-American and Global Land Data Assimilation Systems (LDAS), a Land Information System (LIS) infrastructure has been developed at NASA Goddard Space Flight Center jointly with NOAA-NCEP, NWS and university collaborators. In the context of numerical weather prediction applications, LDAS can provide optimal estimates of land surface state initial conditions by integrating with an ensemble of land surface models the available atmospheric forcing data, remotely sensed observations of precipitation, radiation and some land surface parameters such as land cover and leaf area index. Using high performance computer and communications technologies, LIS is capable of simulating land surface state variables and fluxes at spatial scales down to 1km or even finer. In recent years, land surface state variables such as temperature and soil moisture have been retrieved from remote sensing instruments such as the moderate resolution imaging spectroradiometer (MODIS) and advanced microwave scanning radiometer (AMSR) on NASA’s Terra and Aqua Satellites. Novel data assimilation algorithms such as Kalman filters have been tested to optimally merge these satellite observations with the simulations of land surface models to reduce the uncertainties in the model outputs. Therefore, there is an urgent need to add Kalman filter data assimilation capabilities into the LIS infrastructure for better land surface simulations. Through a collaborative effort, we have implemented three different data assimilation algorithms into LIS (direct insertion, extended Kalman filter-EKF and ensemble Kalman filter-EnKF). In a version of LIS for future release, any of them can be selected to assimilate observations of one or more land surface state variables (such as soil moisture and surface temperature) into any LIS-compliant land surface model (e.g. the Mosaic model, the Noah model, or the Community Land Model-CLM) without major modification to model- or variable-specific subroutines. This presentation describes the construction of the LIS Kalman filter data assimilation plug-in and demonstrates new LIS data assimilation features using synthetic and real satellite remote sensing data.