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

applied techniques known as supervised learning methods, where the user oversees many aspects of the study, including the case selection, the predictor selection, the classification criteria and method, and traditional cross-validation techniques ( Russell and Norvig 2010 ). Techniques such as regression and classification fall under the broader heading of supervised learning methods. These methods have value as evidenced by the improvements seen in RI forecasts in recent years ( Kaplan et al. 2015

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Kyle A. Hilburn, Imme Ebert-Uphoff, and Steven D. Miller

1. Introduction Geostationary Operational Environmental Satellite (GOES) imagery is a key element of U.S. operational weather forecasting, supporting the need for high-resolution, rapidly refreshing imagery for situational awareness ( Line et al. 2016 ). While used extensively by human forecasters, its usage in data assimilation (DA) for numerical weather prediction (NWP) models is limited. Instead DA makes greater usage of microwave and infrared sounder data on low-Earth-orbiting satellites

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Christina Kumler-Bonfanti, Jebb Stewart, David Hall, and Mark Govett

is not a tropical cyclone ( American Meteorological Society 2019 ). Heuristic-based models typically require a specific set of meteorological variables provided by weather model outputs and often cannot be run directly with observations from sources such as satellites without further information or routines. Machine-learning (ML) techniques can be used effectively to identify a range of different meteorological features that may be broadly classified as regions of interest (ROI). For instance, ML

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