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Qidong Yang, Chia-Ying Lee, Michael K. Tippett, Daniel R. Chavas, and Thomas R. Knutson

), which impacts TC wind structure to certain degree. Land–sea roughness contrast ( Wong and Chan 2007 ) and air–sea interaction ( Lee and Chen 2012 , 2014 ), as well as the interactions between TCs and midlatitude circulations ( Komaromi and Doyle 2018 ), can influence TC surface wind structure as well. In recent years, machine learning (ML) has made incremental progress in geophysical sciences. Several recent works have applied ML methods in TC studies. Racah et al. (2017) and Kim et al. (2019

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G. Eli Jergensen, Amy McGovern, Ryan Lagerquist, and Travis Smith

dataset (2004–11). These difficulties highlight the benefits of machine learning (ML), which can achieve human-level accuracy in image classification in a small fraction of the time ( Quartz 2017 ). The success of ML-based image classification in meteorology has been less dramatic, likely because definitions of meteorological phenomena (e.g., linear hybrids) are less objective. Nonetheless, it has achieved notable successes in meteorology as well. For example, Wang et al. (2016) use ML to detect sea

Open access
Stephen A. Shield and Adam L. Houston

likely than in an environment that may have the same EHI value but more SRH and less CAPE. Similarly, SCP is not based on a proven physical process but on the observations of supercells which leads to short comings in certain situations. For example, since each term in SCP is multiplied together a low value of SCP would result from an environment with high shear and low cape, although supercells occur in such environments ( Sherburn and Parker 2014 ). Machine learning has the ability to generate

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Vittorio A. Gensini, Cody Converse, Walker S. Ashley, and Mateusz Taszarek

.e., modeled proximity soundings) and passed to various machine learning classification algorithms for training, testing, and cross-validation. Machine learning approaches for severe convective weather diagnostic and prognostic applications have garnered significant attention in the last five years, and their value added is demonstrable (e.g., Gagne et al. 2017 ; Lagerquist et al. 2017 ; McGovern et al. 2017 ; Czernecki et al. 2019 ; Gagne et al. 2019 ; Mostajabi et al. 2019 ; Burke et al. 2020

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Léonard Boussioux, Cynthia Zeng, Théo Guénais, and Dimitris Bertsimas

use statistical techniques but further include atmospheric variables provided by dynamical models ( DeMaria et al. 2005 ). Last, ensemble models combine the forecasts made by multiple runs of a single model ( Cangialosi 2020 ). Moreover, consensus models typically combine individual operational forecasts with a simple or weighted average ( Sampson et al. 2008 ; Simon et al. 2018 ; Cangialosi 2020 ; Cangialosi et al. 2020 ). In addition, recent developments in deep learning (DL) enable machine

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Christoph Böhm, Jan H. Schween, Mark Reyers, Benedikt Maier, Ulrich Löhnert, and Susanne Crewell

transfer simulations for a limited amount of frequency bands, the aim of this study is to explore the entire spectral information available from spaceborne sensors. To capture different, yet presumably distinct, radiative signatures that are characteristic for various fog, cloud or clear-sky scenes, a machine learning technique is applied to recognize the relevant patterns. Machine learning techniques are becoming increasingly popular in remote sensing and earth observation (e.g., Gardner and Dorling

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Julián David Rojo Hernández, Óscar José Mesa, and Upmanu Lall

states to study the relationships between ENSO and sunspots, and more recently Conti et al. (2017) analyzed the transition probabilities for four prespecified states of ENSO. Some previous works on ENSO dynamics with homogeneous and nonhomogeneous Markov models include Rajagopalan et al. (1997) and Conti et al. (2017) . Machine learning approaches have also been developed as noted in Lima et al. (2009 , 2015) , Tangang et al. (1998) , Hsieh (2001) , and Ruiz et al. (2005) . d. This paper

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Julie A. Haggerty, Allyson Rugg, Rodney Potts, Alain Protat, J. Walter Strapp, Thomas Ratvasky, Kristopher Bedka, and Alice Grandin

(NWP) models, satellite cloud retrievals, and ground-based radar products. An artificial intelligence approach based on fuzzy-logic technology is implemented to combine information derived from each data source. Research aircraft measurements acquired from field experiments are used to objectively train the algorithm using a machine-learning technique. Section 2 explains the approach upon which ALPHA is based. Observations used to train and verify the method are presented in section 3

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Zhi Zhang, Dagang Wang, Jianxiu Qiu, Jinxin Zhu, and Tingli Wang

machine learning (ML). Recently, an increasing number of studies have been applying the ML technique into the hydrometeorological field ( Liu et al. 2015 ; Tao et al. 2018 ; Hayatbini et al. 2019b ; Reichstein et al. 2019 ; Sadeghi et al. 2020 ; Asanjan et al. 2018 ). ML is powerful in its ability to extract implicit patterns from high-dimensional, nonlinear, and multivariate data in complex and dynamic environments ( Hao et al. 2016 ; Shen et al. 2017 ), and can represent complex processes that

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Dan Lu, Goutam Konapala, Scott L. Painter, Shih-Chieh Kao, and Sudershan Gangrade

calibration resulting in biased streamflow simulations in the data-scarce catchments ( Clark et al. 2017 ). Recently, machine learning approaches, long short-term memory (LSTM) networks in particular, have been applied for hydrologic simulations ( Boyraz and Engin 2018 ; Kratzert et al. 2018 , 2019b ; Zhu et al. 2020 ; Fang and Shen 2020 ). LSTM is a purely data-driven model that enables learning, from time series input and output data, the system patterns associated with the observed dynamical system

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