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Relationships between eigen and complex network techniques for the statistical analysis of climate data
eigen complex network techniques statistical analysis climate data
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2013/6/17
Eigen techniques such as empirical orthogonal function (EOF) or coupled pattern (CP) analysis have been frequently used for detecting patterns in multivariate climatological data sets. Recently, stati...
Fast dimension-reduced climate model calibration
Fast dimension-reduced climate model calibration
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2013/4/27
What is the response of the climate system to anthropogenic forcings? This question is addressed typically using projections from climate models. The uncertainty surrounding current climate projection...
An MDL approach to the climate segmentation problem
Changepoints genetic algorithm level shifts
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2010/10/19
This paper proposes an information theory approach to estimate the number of changepoints and their locations in a climatic time series. A model is introduced that has an unknown number of changepoint...
Computational Methods for Parameter Estimation in Climate Models
Parametric Uncertainties InverseProblems SimulatedAnnealing Climate Models
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2009/9/22
Intensive computational methods have been used by Earth scientists
in a wide range of problems in data inversion and uncertainty quantication such
as earthquake epicenter location and climate projec...
Inferring Climate System Properties Using a Computer Model
model calibration climate change climate sensitivity Bayesian methods
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2009/9/22
A method is presented to estimate the probability distributions of
climate system properties based on a hierarchical Bayesian model. At the base
of the model, we use simulations of a climate model i...
Parameter estimation for computationally intensive nonlinear regression with an application to climate modeling
Equilibrium climate sensitivity observed and modeled climate space–time modeling statistical surrogate temperature data
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2010/3/17
Nonlinear regression is a useful statistical tool, relating observed
data and a nonlinear function of unknown parameters. When the
parameter-dependent nonlinear function is computationally intensive...