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Noah D. Brenowitz, Tom Beucler, Michael Pritchard, and Christopher S. Bretherton

climate change ( Schneider et al. 2017 ). Owing to advances in both computing and available datasets, machine learning (ML) is now a viable alternative for traditional parameterization. Viewed from the perspective of ML, parameterization is a straightforward regression problem. A parameterization maps a set of inputs, namely, atmospheric profiles of humidity and temperature, to some outputs, profiles of subgrid heating and moistening. Krasnopolsky et al. (2005) and Chevallier et al. (1998

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Andrew E. Mercer, Alexandria D. Grimes, and Kimberly M. Wood

. 2010 ), such as the distribution of diabatic heating (e.g., Nolan et al. 2007 ). Note that this RI definition is typically the most difficult to classify ( Kaplan et al. 2015 ), and our study relies on data too coarse to resolve small-scale features, so the lack of evident RI/non-RI distinguishing characteristics in these composites was anticipated. Importantly, these results were all statistically significant, implying that the orientation of the moisture gradient coupled with better

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