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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #315197
Title: Optimal averaging of soil moisture predictions from ensemble land surface model simulations

Author
item Crow, Wade
item SU, CHUN-HSU - University Of Melbourne
item RYU, D. - University Of Melbourne
item YILMAZ, M.T. - Collaborator

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 4/15/2015
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: The correct interpretation of ensemble 3 soil moisture information obtained from the parallel implementation of multiple land surface models (LSMs) requires information concerning the LSM ensemble’s mutual error covariance. Here we propose a new technique for obtaining such information using an instrumental variable (IV) regression approach and comparisons against a long-term surface soil moisture dataset obtained from satellite remotesensing. Application of the approach to multi-model ensemble soil moisture output from the North American Land Data Assimilation System (NLDAS-2), and multi-satellite European Space Agency (ESA) Soil Moisture (SM) Essential Climate Variable (ECV) dataset, allows for the calculation of optimal weighting coefficients for individual members of a the NLDAS-2 ensemble and a biased-minimized estimate of uncertainty in a deterministic soil moisture analysis derived via such optimal weighted averaging. As such, it provides key information required to accurately condition soil moisture expectations using information gleaned from a multi-model LSM ensemble. However, existing continuity and rescaling concerns surrounding the generation of long-term, satellite-based soil moisture products must likely be resolved before the proposed approach can be applied with full confidence.