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

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Crow W. T., Su C. -., Ryu D., YILMAZ M. T.

WATER RESOURCES RESEARCH, vol.51, no.11, pp.9273-9289, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 51 Issue: 11
  • Publication Date: 2015
  • Doi Number: 10.1002/2015wr016944
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.9273-9289
  • Middle East Technical University Affiliated: Yes


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.