|Reichle, R -|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: September 1, 2009
Publication Date: October 5, 2009
Citation: Crow, W.T., Reichle, R. 2009. Land data assimilation activities in preparation of the NASA Soil Moisture Active Passive (SMAP) Mission [abstract]. WMO Data Assimilation Symposium. 2009 CDROM. Technical Abstract: Slated for launch in 2013, the NASA Soil Moisture Active/Passive mission represents a generational advance in our ability to globally observe time and space variations in surface soil moisture fields. The SMAP mission concept is based on the integrated use of L-band active radar and passive radiometry measurements to optimize both the accuracy and resolution of remotely-sensed soil moisture estimates. Data assimilation activities represent a critical linkage between SMAP products and eventual science and operational applications. In particular, SMAP mission plans call for the generation of a dedicated data assimilation product to vertically extrapolate near-surface (0 to 5-cm) soil moisture retrievals to produce deeper, root-zone (0 to 1-m) soil moisture estimates required by most applications. A global, Ensemble Kalman filtering land data assimilation system capable of generating this product is currently under development at the NASA Global Modeling and Data Assimilation Office. This presentation will highlight two specific elements of this development. First, we will summarize recent efforts to quantify the added value of SMAP soil moisture retrievals for global soil moisture monitoring activities. Existing applications already posses access to soil moisture estimates derived from off-line water balance models constrained solely by observed rainfall and meteorological variables. Clarifying the added benefit of assimilating remotely-sensed surface soil moisture retrievals into such systems (relative to this existing baseline) is critical for articulating expected SMAP impacts on key applications. Second, we will describe ongoing efforts to apply adaptive filtering techniques to land surface data assimilation systems. Land surface modeling error arises from a highly diverse set of sources, and failure to adequately characterize either the origin or structure of errors can lead to a significant reduction in the accuracy of analysis products. Consequently, the development and implementation of an effective adaptive filtering system represents an important goal for efforts to effectively assimilate SMAP soil moisture products.