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Fiaz Ahmed and J. David Neelin

reverse engineer the deep-inflow mass flux here relies on the availability of large amounts of data. This method is an example of a hypothesis-based effort to probe large datasets, in contrast to big data machine-learning methods that are finding greater use in the Earth sciences. Convective transition statistics, being a rather robust property of tropical convection, have great utility in diagnosing GCMs ( Kuo et al. 2017 ). The term B int is an inflow-dependent property of convection, similar in

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Toward a Systematic Evaluation of Warm Conveyor Belts in Numerical Weather Prediction and Climate Models. Part I: Predictor Selection and Logistic Regression Model

Julian F. Quinting and Christian M. Grams

major advantage of the logistic regression approach compared to other classification techniques such as nonlinear support vector machines ( Vapnik 1963 ) or deep learning methods ( McGovern et al. 2019 ): the regression coefficients directly give inference about the importance of each predictor. Thus, logistic regression models can be used quite intuitively to find out the relationship between the predictands and independent predictor variables, and allow to check the model’s plausibility

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Grey S. Nearing, Benjamin L. Ruddell, Martyn P. Clark, Bart Nijssen, and Christa Peters-Lidard

.g., deep learning) can be expected to improve on the first-order approach outlined here. Importantly the question we want to ask is how much information about this particular type of systematic relationship is contained in our experimental data? Once we know the answer to this question, we can ask what portion of that information a particular model is able to reproduce. The basic strategy is to ensure that any process model we might build contains at least as much information about the systematic

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