Role of subsurface physics in the assimilation of surface soil moisture observations ?

Content Provider	Semantic Scholar
Copyright Year	2009
Abstract	Root zone soil moisture controls the land-atmosphere exchange of water and energy and exhibits memory that may be useful for climate prediction at monthly scales. Assimilation of satellite-based surface soil moisture observations into a land surface model is an effective way to estimate large-scale root zone soil moisture. The propagation of surface information into deeper soil layers depends on the modelspecific representation of subsurface physics that is used in the assimilation system. In a suite of experiments we assimilate synthetic surface soil moisture observations into four different models (Catchment, Mosaic, Noah and CLM) using the Ensemble Kalman Filter. We demonstrate that identical twin experiments significantly overestimate the information that can be obtained from the assimilation of surface soil moisture observations. The second key result indicates that the potential of surface soil moisture assimilation to improve root zone information is higher when the surface to root zone coupling is stronger. Our experiments also suggest that (faced with unknown true subsurface physics) overestimating surface to root zone coupling in the assimilation system provides more robust skill improvements in the root zone compared with underestimating the coupling. When CLM is excluded from the analysis, the skill improvements from using models with different vertical coupling strengths are comparable for different subsurface truths. Finally, the skill improvements through assimilation were found to be sensitive to the regional climate and soil types. ? Corresponding author: S. V. Kumar. Tel. 001-301-286-8663. Fax: 001-301-286-8624, email: Sujay.V.Kumar@nasa.gov
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://gmao.gsfc.nasa.gov/pubs/docs/Kumar371.pdf
Language	English
Access Restriction	Open
Subject Keyword	Channel length modulation Coupling (computer programming) Data assimilation Email Ensemble Kalman filter Exclusion Exhibits as Topic Experiment Fax Land Surface Model (LSM version 1.0) Numerical weather prediction Software propagation Subsurface scattering Synthetic intelligence Twin anatomical layer
Content Type	Text
Resource Type	Article