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Robin L. Tanamachi, Daniel T. Dawson II, and Loran Carleton Parker

learning of atmospheric science, we created an elective “severe storms field work” course within Purdue EAPS. Learning, in this context, is defined as, “the process whereby knowledge is created through the transformation of experience” ( Kolb 1984 ). The rationale for the creation of this course was well expressed by King (1993) : “When students are engaged in actively processing information by reconstructing that information in such new and personally meaningful ways, they are far more likely to

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Andrew M. Dzambo, Margaret Mooney, Zachary J. Handlos, Scott Lindstrom, Yun Hang, and Steve A. Ackerman

other while testing their own knowledge. Worksheet assignments push students to deeper learning, and quizzes ensure they complete and comprehend assigned readings. For the last week, students upload an “elevator speech” video explaining climate change to a stranger in less than 2 minutes. A final project research paper supports good writing skills while combining course topics in a real-world scenario. The AOS 102 course will continue to evolve as new scientific research becomes available, and will

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Xinhua Liu, Kanghui Zhou, Yu Lan, Xu Mao, and Robert J. Trapp

elsewhere around the world, the role of human forecasters has been challenged in favor of applications of artificial intelligence (AI)/machine learning approaches. How to maintain the role of the forecaster in future forecasting has been discussed ( Stuart et al. 2006 , 2007 ) and will likely evolve the role of the conceptual model in the forecasting process. Convection-allowing model (CAM) usage at the National Meteorological Center of China Meteorological Administration (CMA) began in 2014. 1

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Xinhua Liu, Kanghui Zhou, Yu Lan, Xu Mao, and Robert J. Trapp

elsewhere around the world, the role of human forecasters has been challenged in favor of applications of artificial intelligence (AI)/machine learning approaches. How to maintain the role of the forecaster in future forecasting has been discussed ( Stuart et al. 2006 , 2007 ) and will likely evolve the role of the conceptual model in the forecasting process. Convection-allowing model (CAM) usage at the National Meteorological Center of China Meteorological Administration (CMA) began in 2014. 1

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Christine J. Kirchhoff, Joseph J. Barsugli, Gillian L. Galford, Ambarish V. Karmalkar, Kelly Lombardo, Scott R. Stephenson, Mathew Barlow, Anji Seth, Guiling Wang, and Austin Frank

representatives from all SCAs listed except Maryland and Delaware. LEARNING FROM GLOBAL AND NATIONAL ASSESSMENTS. Years of research on global and national CAs offers a number of important lessons. First, research on global assessments ( Farrell and Jäger 2006 ) and the U.S. National CA (NCA) ( Mitchell et al. 2006 ) suggests that involving recognized experts enhances assessment credibility as does using accepted data, methods/tools, numerical models, and scientific peer review. Credibility of CAs can be

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Travis A. O’Brien, Ashley E. Payne, Christine A. Shields, Jonathan Rutz, Swen Brands, Christopher Castellano, Jiayi Chen, William Cleveland, Michael J. DeFlorio, Naomi Goldenson, Irina V. Gorodetskaya, Héctor Inda Díaz, Karthik Kashinath, Brian Kawzenuk, Sol Kim, Mikhail Krinitskiy, Juan M. Lora, Beth McClenny, Allison Michaelis, John P. O’Brien, Christina M. Patricola, Alexandre M. Ramos, Eric J. Shearer, Wen-Wen Tung, Paul A. Ullrich, Michael F. Wehner, Kevin Yang, Rudong Zhang, Zhenhai Zhang, and Yang Zhou

, L20401 , https://doi.org/10.1029/2010GL044696 . 10.1029/2010GL044696 Kurth , T. , and Coauthors , 2018 : Exascale deep learning for climate analytics . Proc. Int. Conf. for High Performance Computing, Networking, Storage, and Analysis , Piscataway, NJ, IEEE, 51, https://dl.acm.org/doi/10.5555/3291656.3291724 . Mudigonda , M. , and Coauthors , 2017 : Segmenting and tracking extreme climate events using neural networks . 31st Conf. on Neural Information Processing System , Long Beach

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Gert-Jan Steeneveld and Jordi Vilà-Guerau de Arellano

An active learning approach is a successful method in teaching atmospheric modelling of the atmospheric environment at the master’s level. Numerical weather prediction (NWP) has rapidly developed from basic single-layer barotropic models in the 1950s to very advanced high-resolution Earth system models. Bauer et al. (2015) explained in detail why weather forecasting has undergone a key silent revolution in society, where the current-day global models show skill for lead times up to 7 days

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G. L. Mullendore and J. S. Tilley

The University of North Dakota partnered with field campaign investigators to provide undergraduate students with an experiential learning opportunity that uniquely integrated classroom activities, operational forecasting, and a large multiagency field campaign. It is crucial for the next generation of scientists, who will deal increasingly with research areas that cross not only disciplinary but also methodological boundaries, to gain as much cross-disciplinary and cross

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Hongbo Liu, Janina V. Büscher, Kevin Köser, Jens Greinert, Hong Song, Ying Chen, and Timm Schoening

; Osterloff et al. 2016a ; Rimavicius and Gelzinis 2017 ; Nilssen et al. 2017 ). Osterloff et al. (2019) successfully linked the polyp activity determined by machine learning to other sensor time series (current, water depth, temperature) to find relationships. The potential of deep learning methods to utilize the enormous number of images taken by conventional red–green–blue (RGB) cameras has also been investigated ( King et al. 2018 ). Underwater hyperspectral imaging (UHI) and multispectral (MS

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Ben Orlove

In recent weeks, people around the world have expressed deep disappointment that the United Nations Climate Change Conference in Copenhagen, Denmark, held in December 2009, did not produce a firm agreement that would reduce greenhouse gas emissions and help the world adapt to present and future impacts of climate change. As commentators have stressed, international agreements are often difficult to establish and the failure of this conference can be traced to many sources. The consensus process

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