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Loris Foresti, Ioannis V. Sideris, Daniele Nerini, Lea Beusch, and Urs Germann

historical overview on deep learning, we refer to Schmidhuber (2015) . To our knowledge, the first study that tested the usage of ANNs for precipitation nowcasting is by French et al. (1992) . The authors trained an ANN to predict the evolution of synthetic rainfall fields, but did not find significantly higher skill compared to Lagrangian persistence. Grecu and Krajewski (2000) went a step further by separating the prediction problem into two steps: the estimation of the radar echo motion and the use

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Paul M. Tag and James E. Peak

714JOURNAL OF APPLIED METEOROLOGYVo~.u~ 35Machine Learning of Maritime Fog Forecast Rules PAUL M. TAGNaval Research Laboratory, Monterey, California JAMF~S E. PEAKComputer Sciences Corporation, Monterey, California(Manuscript received 1 May 1995, in final form 19 October 1995)ABSTRACT In recent years, the field of artificial intelligence has contributed significantly to the science of meteorology,most notably in the now familiar form of expert systems. Expert systems have

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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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Gary Lackmann

that S2S studies were welcomed. In early 2019, we modified the WAF terms of reference to clarify that such studies are welcomed, with predictive horizons extending out to a few years. To accommodate such submissions, a leading researcher in this area, Prof. Ben Kirtman (University of Miami), joined the editorial board in January 2019. 2) Machine learning (ML), artificial intelligence (AI), and deep learning (DL) have experienced rapid growth in many areas, including in forecasting applications

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Gary Lackmann

that S2S studies were welcomed. In early 2019, we modified the WAF terms of reference to clarify that such studies are welcomed, with predictive horizons extending out to a few years. To accommodate such submissions, a leading researcher in this area, Prof. Ben Kirtman (University of Miami), joined the editorial board in January 2019. 2) Machine learning (ML), artificial intelligence (AI), and deep learning (DL) have experienced rapid growth in many areas, including in forecasting applications

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J. Shao

the possibility of being “trapped” in a local minimum where a learning process likely fails, the learning rate is progressively decreased. As the rate decreases, the network takes smaller downhill steps and its weights settle into a minimum configuration without overshooting the stable position. Therefore, the network is hopefully able to bypass local minima and then find some deeper minima or a better solution without oscillating wildly. For this purpose, the learning rate is taken as a function

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Ethan L. Nelson, Tristan S. L’Ecuyer, Adele L. Igel, and Susan C. van den Heever

three satellite and radar meteorology course offerings at the University of Wisconsin–Madison to date, either as an in-class laboratory activity or an out-of-class deeper thought assignment. An initial assessment of student learning gains and perceptions with the associated learning activity was completed through the University of Wisconsin–Madison Delta Program for Research, Teaching, and Learning Certificate Internship program. This assessment showed a positive reception and self

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Donat Perler and Oliver Marchand

1. Introduction This study investigates adaptive boosting, a relatively new classification method introduced by Freund and Schapire (1997) , to numerical weather prediction (NWP) output postprocessing. As a case study, we show how this machine learning method can be used to detect thunderstorms in NWP forecast output fields. NWP uses the basic physical equations of the atmosphere for simulation. Predicting thunderstorms with NWP models is inherently difficult. This is because of the rather

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Tom H. Durrant, Diana J. M. Greenslade, Ian Simmonds, and Frank Woodcock

spatially homogeneous corrections, to corrections that vary both in space and time. In an effort to eliminate the need to manually monitor and update the applied corrections, an automatic, self-learning correction method is proposed, applicable to operational forecast winds. This work was performed with the intention of developing an operational system applicable to the Bureau’s forecasting environment. The work was carried out within the context of replacing the Bureau’s operational wave model

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Scott E. Kalafatis, Jasmine Neosh, Julie C. Libarkin, Kyle Powys Whyte, and Chris Caldwell

composed of their own unique blend of reflective activities to continually monitor and enhance their work. This finding highlights that even though there were suggested practices offered and documented in this study, effective learning by doing is a deeply personalized process where participants’ past history, current experiences, and future goals come into contact with one another in the service of personal and professional growth. Suggested practices have reflective value as objects or ideals that

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