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Stephan Rasp, Hauke Schulz, Sandrine Bony, and Bjorn Stevens

identification. In these situations, machine learning techniques, particularly deep learning (see “Deep learning for vision tasks in the geosciences” sidebar), have demonstrated their ability to mimic the human capacity for identifying patterns, also from satellite cloud imagery (e.g., Wood and Hartmann 2006 ). However, the application and assessment of such techniques is often limited by the tedious task of obtaining sufficient training data, so much so that (in cloud studies at least) these approaches

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Imme Ebert-Uphoff and Kyle Hilburn

) for deeper study. From simple neurons to powerful neural networks. An artificial neural network (ANN; or NN for short) is a machine learning method loosely inspired by the human brain. An NN consists of a set of neurons (aka nodes ) that are connected by synapses which pass signals between the neurons. In sequential NNs , which are the primary type considered here, all neurons are arranged in a sequence of layers, and signals pass in a one-directional manner from the input layer through

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Massimo Bonavita, Rossella Arcucci, Alberto Carrassi, Peter Dueben, Alan J. Geer, Bertrand Le Saux, Nicolas Longépé, Pierre-Philippe Mathieu, and Laure Raynaud

First ECMWF–ESA Workshop on Machine Learning for Earth System Observation and Prediction What : ECMWF and ESA convened a workshop to explore the current status, prospects, and opportunities in the application of machine learning/deep learning for Earth system observation and prediction. When : 5–8 October 2020 Where : Online; https://events.ecmwf.int/event/172/ Almost 400 researchers from across the world joined the first ECMWF–ESA Workshop on Machine Learning for Earth System Observation and

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Casey E. Davenport

via careful instructional design. Importantly, if extraneous load is reduced, then more effort can be put toward deeper, long-term understanding (i.e., a higher germane load; Sweller et al. 1998 ; Paas et al. 2003 ). One proven method to reduce extraneous cognitive load and enhance student learning is known as worked examples . In essence, worked examples represent an expert-constructed guide that provides in-depth, step-by-step explanations of how to solve a problem or perform a complex task

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Jonathan D. W. Kahl

The Atmospheric Science program at the University of Wisconsin—Milwaukee regularly offers the general education course Survey of Meteorology, serving over 400 students each year. This article describes a systematic inquiry into the teaching and learning goals of the course and the adequacy of current methods used to assess student performance. Following a survey of the six faculty members with teaching responsibilities for the course, common student learning goals of meteorological content and the application of meteorological concepts to observations were identified. Student surveys, designed to assess both the extent to which these learning goals were being met as well as the depth of learning, were administered to 241 students during the 2005–06 academic year. Results indicate that 80% of students surveyed met the content learning goal, while the application learning goal was met by only 66% of students. A deeper level of application learning, involving pattern recognition and the separation of concepts into component parts, was achieved by only 45% of the students. A comparison of student survey results with course grade distributions indicates that current grading practices are adequate for assessing the content learning goal but are inadequate for assessing the application learning goal.

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Amy McGovern, Ryan Lagerquist, David John Gagne II, G. Eli Jergensen, Kimberly L. Elmore, Cameron R. Homeyer, and Travis Smith

Machine learning model interpretation and visualization focusing on meteorological domains are introduced and analyzed. Machine learning (ML) and deep learning (DL; LeCun et al. 2015 ) have recently achieved breakthroughs across a variety of fields, including the world’s best Go player ( Silver et al. 2016 , 2017 ), medical diagnosis ( Rakhlin et al. 2018 ), and galaxy classification ( Dieleman et al. 2015 ). Simple forms of ML (e.g., linear regression) have been used in meteorology since at

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Peter J. Walton, Morgan B. Yarker, Michel D. S. Mesquita, and Friederike E. L. Otto

; however, having enough bandwidth to be able to download can be problematic to some participants. This course structure though does reflect more closely Kolb’s (1984) model of experiential learning, where it is suggested that participants will form deeper understanding concepts (in this case climate modeling) through actively participating in an exercise. The final point to be taken from comparing the two courses is the issue of certification. As well as wanting to develop their own knowledge

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David R. Perkins IV, Jennifer Vanos, Christopher Fuhrmann, Michael Allen, David Knight, Cameron C. Lee, Angela Lees, Andrew Leung, Rebekah Lucas, Hamed Mehdipoor, Sheila Tavares Nascimento, Scott Sheridan, and Jeremy Spencer

learning, a natural research inquiry surfaced: how do we enhance the teaching and learning of biometeorology in higher education to both promote the discipline and elicit deeper learning into common meteorological, climatological, and biological concepts? As a result of this inquiry, the second international workshop held by the ISB’s SNP group sought to tackle this very issue. Transforming core tenants of biometeorology and education into collaborative initiatives on an international scale was a key

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S. L. Sellars

Initiative; associate director, Center for Machine Learning and Intelligent Systems, UCI. WORKSHOP HIGHLIGHTS. A noticeable theme throughout the workshop was that technological advances in hardware and software have allowed data-driven approaches to emerge as powerful tools that can be used in the era of big data and “deep analysis.” In addition, many of these technologies allow for massive data transfers, storage, and analysis approaches—necessary features to process enormous and often complex datasets

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Casey E. Davenport, Christian S. Wohlwend, and Thomas L. Koehler

throughout a semester, and do not necessarily measure deep understanding, as many exams tend to emphasize lower-order cognition ( Crooks 1988 ). The Force Concept Inventory (FCI; Hestenes et al. 1992 ), developed in the early 1990s, revealed the superficial nature of conceptual understanding of introductory physics topics by a significant proportion of college students. The application of this result dramatically shifted perceptions of the teaching and learning of physics, and subsequently radically

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