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Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method
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DOI：10.1175/JCLI-D-13-00091.1.png)
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摘要
En 中文
Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. 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. Here, 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.
Keyword:
SIMULATED RADAR DATA
ROOT KALMAN FILTER
EARTH SYSTEM MODEL
DATA ASSIMILATION
MICROPHYSICAL PARAMETERS
COVARIANCE INFLATION
EQUATORIAL PACIFIC
ATMOSPHERIC STATE
TROPICAL PACIFIC
PART I
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IF:
4
论文数:
1.4W
被引数:
5.9W
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Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter.png)
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OCEAN MODELLINGIF2.9
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