Optimal averaging of soil moisture predictions from ensemble land surface model simulations

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Date

2015-11-01

Author

Crow, W. T.
Su, C. -H.
Ryu, D.
Yılmaz, Mustafa Tuğrul

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The correct interpretation of ensemble 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 technique for obtaining such information using an instrumental variable (IV) regression approach and comparisons against a long-term surface soil moisture data set acquired from satellite remote sensing. Application of the approach to multimodel ensemble soil moisture output from Phase 2 of the North American Land Data Assimilation System (NLDAS-2) and European Space Agency (ESA) Soil Moisture (SM) Essential Climate Variable (ECV) data set allows for the calculation of optimal weighting coefficients for individual members of the NLDAS-2 LSM ensemble and a biased-minimized estimate of uncertainty in a deterministic soil moisture analysis derived via optimal averaging. As such, it provides key information required to accurately condition soil moisture expectations using information gleaned from a multimodel 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.

Subject Keywords

Soil moisture can be predicted from a mulit-model ensemble, Interpretation of the ensemble requires model error covariance information, Such information can be obtained using an instrumental variable approach

URI

https://hdl.handle.net/11511/38305

Journal

WATER RESOURCES RESEARCH

DOI

https://doi.org/10.1002/2015wr016944

Collections

Department of Civil Engineering, Article

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Citation Formats

W. T. Crow, C.-H. Su, D. Ryu, and M. T. Yılmaz, “Optimal averaging of soil moisture predictions from ensemble land surface model simulations,” WATER RESOURCES RESEARCH, pp. 9273–9289, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38305.