Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method
Abstract
Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. And for a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. An adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final global uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.
- Authors:
-
- Univ. of Wisconsin, Madison, WI (United States). Center for Climate Research and Dept. of Atmospheric and Oceanic Sciences
- Peking Univ., Beijing (China). Lab. of Ocean, Atmospheric Studies
- Princeton Univ., NJ (United States). Search Results NOAA Geophysical Fluid Dynamics Lab.
- Chinese Academy of Meteorological Sciences, Beijing (China)
- Argonne National Lab. (ANL), Argonne, IL (United States). Mathematics and Computer Science Division
- Publication Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1395035
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Climate
- Additional Journal Information:
- Journal Volume: 27; Journal Issue: 11; Journal ID: ISSN 0894-8755
- Publisher:
- American Meteorological Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; optimization; statistical techniques; climate models; data assimilation; ensembles
Citation Formats
Liu, Y., Liu, Z., Zhang, S., Rong, X., Jacob, R., Wu, S., and Lu, F. Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method. United States: N. p., 2014.
Web. doi:10.1175/JCLI-D-13-00091.1.
Liu, Y., Liu, Z., Zhang, S., Rong, X., Jacob, R., Wu, S., & Lu, F. Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method. United States. https://doi.org/10.1175/JCLI-D-13-00091.1
Liu, Y., Liu, Z., Zhang, S., Rong, X., Jacob, R., Wu, S., and Lu, F. Thu .
"Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method". United States. https://doi.org/10.1175/JCLI-D-13-00091.1. https://www.osti.gov/servlets/purl/1395035.
@article{osti_1395035,
title = {Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method},
author = {Liu, Y. and Liu, Z. and Zhang, S. and Rong, X. and Jacob, R. and Wu, S. and Lu, F.},
abstractNote = {Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. And for a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. An adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final global uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.},
doi = {10.1175/JCLI-D-13-00091.1},
journal = {Journal of Climate},
number = 11,
volume = 27,
place = {United States},
year = {Thu May 29 00:00:00 EDT 2014},
month = {Thu May 29 00:00:00 EDT 2014}
}
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