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

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 variab...

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Bibliographic Details
Published in:Water resources research 2015-11, Vol.51 (11), p.9273-9289
Main Authors: Crow, W. T., Su, C.-H., Ryu, D., Yilmaz, M. T.
Format: Article
Language:English
Subjects:
Climate change
Coefficients
Computer simulation
Covariance
Data
Data acquisition
Data assimilation
Data collection
Data processing
ensemble
land surface modeling
Land surface models
Moisture
Regression analysis
Remote sensing
Rescaling
Satellites
Scaling
Soil
Soil analysis
Soil conditions
Soil moisture
Soil surfaces
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Summary: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. Key Points: 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
ISSN:0043-1397
1944-7973
DOI:10.1002/2015WR016944