Measuring Dynamical Prediction Utility Using Relative Entropy

By Richard Kleeman. 2002

287 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Global and Planetary Change, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/5e7abd579515668c109dd4ff09916a00f43aefa8

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Citations in the corpus

Generalization of the Ignorance Score: Continuous Ranked Version and Its Decomposition (2012, background): “Information-theoretical concepts have already been suggested, for example by DelSole (2004), Kleeman (2002), or Leung and North (1990).”
Challenges in Climate Science and Contemporary Applied Mathematics (2012, background): “Kleeman [67] first applied these ideas to the prediction skill for long range forecasting in perfect models and these concepts have been developed extensively by scientists in CAOS at CIMS with many applications and associated theory [69, 81, 68, 1, 47] in the context of perfect models.”
Skill of Multimodel ENSO Probability Forecasts (2008, methodology): “Kleeman (2002) examined this issue in several simple models including a stochastically forced coupled ocean–atmosphere model used to predict ENSO, finding that changes in the ensemble mean provided most of the prediction utility.”
Two Limits of Initial-Value Decadal Predictability in a CGCM (2010, methodology): “We take into account both factors (section 3b) by using relative entropy (Kleeman 2002) to measure”
Decadal climate prediction: an update from the trenches (2014, methodology): “Some investigators have used relative entropy from information theory (Kleeman 2002; Majda et al. 2005; DelSole 2004) as a comprehensive measure of predictability, though this method works best with large ensembles or large numbers of hindcasts.”
Improved Entropy Weighting Model in Water Quality Evaluation (2019, unknown): “…η, whose distribution laws are {p(ξ1), p(ξ2), … , p(ξM)} and {p(η1), p(η2), … , p(ηM)}, respectively, scientists developed a relative entropy R(ξ,η) as follows (Kleeman 2002, Shell 2008): R ξ;ηð Þ ¼ ∑ M k¼1 p ξkð Þ⋅log p ξkð Þ p ηkð Þ ; ð4Þ where the base number of the logarithmic function is…”
Predictability: Recent insights from information theory (2007, methodology): “Three measures of predictability have been proposed that satisfy these principles: mutual information [Leung and North, 1990], predictive information [Schneider and Griffies, 1999], and relative entropy [ Kleeman, 2002 ].”
Evaluating Probabilistic Forecasts Using Information Theory (2002, background): “Using ignorance naturally connects the problems of practically evaluating real forecasts to the information-theoretic framework for weather and climate prediction, which has been constructed by other workers in the field (Leung and North 1990; Kleeman 2002).”
Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation (2010, methodology): “The measure has been used in geophysical, highdimensional applications, in predictability (Kleeman 2002), in the statistical modeling of geophysical dynamical systems (Haven et al.”
Estimation of Seasonal Precipitation Tercile-Based Categorical Probabilities from Ensembles (2007, background): “For reliable forecasts, the difference between the climatological and forecast pdfs represents predictability, and several measures of this difference have been developed to quantify predictability (Kleeman 2002; DelSole 2004; Tippett et al. 2004; DelSole and Tippett 2007).”
Reliability of ENSO Dynamical Predictions (2005, methodology): “These components can be interpreted respectrively as the dispersion and signal components of the utility of a prediction (Kleeman 2002).”
Average Predictability Time. Part I: Theory (2009, background): “…of the variables— measures satisfying these properties have been proposed by Leung and North (1990), Schneider and Griffies (1999), and Kleeman (2002) (see DelSole and Tippett 2007 for a review)—and the second is the integration of this measure of predictability with respect to all…”