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| Titel |
Optimal averaging of soil moisture predictions from ensemble land surface model simulations |
| VerfasserIn |
Wade T. Crow, Chun-Hsu Su, Dongryeol Ryu, M. Tugrul Yilmaz |
| Konferenz |
EGU General Assembly 2015
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 17 (2015) |
| Datensatznummer |
250105662
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| Publikation (Nr.) |
 EGU/EGU2015-5196.pdf |
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| Zusammenfassung |
| 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 remote sensing. 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. |
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