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Joseph Sedlar, Laura D. Riihimaki, Kathleen Lantz, and David D. Turner

and (c) low cumulus periods. At least 120 min of a consecutive cloud event must have been observed to be included in the analysis. For low stratiform, there were 311 events; for low cumulus, there were 124. Data are from 2014 to 2018 at ARM SGP. The other cloud regimes in Fig. 2a are much less frequent than low clouds and cirrus. To improve separability of cloud regimes during the training phase of the machine-learning classifier, congestus clouds are combined with deep convection, and

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Wei Zhang, Bing Fu, Melinda S. Peng, and Tim Li

, 1998 ; Fu et al. 2007 ). Over the decades, significant advancements have been made in understanding the physical mechanisms and processes involved in TC genesis ( Gray 1968 ; McBride 1981 ; Craig and Gray 1996 ; Fu et al. 2007 ; Wang et al. 2007 ; Peng et al. 2012 ; Fu et al. 2012 ). Gray (1968) suggested several favorable environmental parameters for TC genesis: a sufficiently deep warm ocean layer, conditional instability through a deep atmospheric layer, higher

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Richard L. Bankert, Cristian Mitrescu, Steven D. Miller, and Robert H. Wade

—brightness temperature greater than 273 K, 2) supercooled water or mixed phase—composed entirely of supercooled water droplets or both ice and supercooled, and 3) glaciated (optically thick ice) clouds—entirely ice crystals or glaciated tops (e.g., deep convection)—are applied and the pixel’s cloud type is assigned. 4. GOES cloud classifier—Implicit physics Using a supervised learning method that was first applied to AVHRR data ( Tag et al. 2000 ), an IP cloud classifier has been developed and further refined for

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A. K. Showalter

fig. 2 of the Byers and Rodebush reportimply a much deeper on-shore flow than off-shoreflow during a twenty-four hour period. If a value of25 were arbitrarily added to all of the convergenceand divergence values thus displacing the zero lines infig. 2 downward approximately 25 points, the relativedepths of the day and night breeze would appear tobe approximately correct. Most meteorologists arefamiliar with the fact that on-shore breezes arestronger than off-shore land breezes, but my discussion

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Amy McGovern, Kimberly L. Elmore, David John Gagne II, Sue Ellen Haupt, Christopher D. Karstens, Ryan Lagerquist, Travis Smith, and John K. Williams

and trained in multiple layers, ANNs can represent any nonlinear function. They also provide the foundation for deep learning methods. ANNs have been used in a wide variety of meteorology applications since the late 1980s ( Key et al. 1989 ), including cloud classification ( Bankert 1994 ), tornado prediction and detection ( Marzban and Stumpf 1996 ; Lakshmanan et al. 2005 ), damaging winds ( Marzban and Stumpf 1998 ), hail size ( Marzban and Witt 2001 ; Manzato 2013 ), precipitation

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Amy McGovern, Andrea Balfour, Marissa Beene, and David Harrison

games as an educational tool provides opportunities for deeper learning . [Available online at .] Martínez-Arocho , A. G. , P. S. Buffum , and K. E. Boyer , 2014 : Developing a game-based learning curriculum for “big data” in middle school . Proc. 45th ACM Tech. Symp. on Computer Science Education , Atlanta, GA , Assoc. Comput. Mach. , 712 , doi: 10.1145/2538862.2544296 . McClarty , K. L. , A. Orr , P. M

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Gregory R. Herman and Russ S. Schumacher

1. Introduction Machine learning algorithms have demonstrated considerable utility in many scientific disciplines, including computer vision (e.g., Rosten and Drummond 2006 ), natural language processing (e.g., Collobert et al. 2011 ), and bioinformatics (e.g., Larrañaga et al. 2006 ). Machine learning has also been used with considerable success in a wide range of future prediction scenarios, from financial market analysis (e.g., Cao and Tay 2003 ) to election forecasting (e

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Filipe Aires, Francis Marquisseau, Catherine Prigent, and Geneviève Sèze

.8, 31.4, and 89 GHz. It is a cross-track scanning radiometer, with ±48.3° from nadir with a total of 30 earth fields of view of 3.3° per scan line, providing a nominal spatial resolution of 48 km at nadir. The swath is approximately 2000 km and the instrument realizes one scan in 8 s. The AMSU-B microwave radiometer is designed to measure the atmospheric water vapor profile, with three channels in the H 2 O line at 183.31 GHz plus two window channels at 89 and 150 GHz that enable deeper penetration

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Gary S. E. Lagerloef

T-S relations, but at therisk of introducing artificial variations at the subdivision boundaries. Second, the method should remainobjective. Third, the vertical distribution of parametersshould be taken into account. An examination of theGulf of Alaska T-S distributions (Fig. 2a) reveals thata temperature of, say, 4-C at a shallow depth is morelikely to coincide with a lower salinity, while the sametemperature at a deeper level will coincide with a highersalinity. It should also be noted that

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Junho Yang, Mikyoung Jun, Courtney Schumacher, and R. Saravanan

. , 1987 : Dividing the indivisible: Using simple symmetry to partition variance explained. Proc. Second International Tampere Conf. in Statistics , Tampere, Finland, Department of Mathematical Sciences, University of Tampere, 245–260. Rasp , S. , M. S. Pritchard , and P. Gentine , 2018 : Deep learning to represent sub-grid processes in climate models . Proc. Natl. Acad. Sci. USA , 115 , 9684 – 9689 , . 10.1073/pnas.1810286115 Rienecker , M. M

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