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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Jebb Q. Stewart, Eric S. Maddy, Narges Shahroudi, and Ross N. Hoffman

(e.g., Gagne et al. 2017 ; Campos et al. 2019 ). It is now clear (see references cited as examples in the body of this essay) that AI approaches, including recent advances in ML technology, such as Transfer Learning and Long and Short Term Memory Networks (LSTMs; Hochreiter and Schmidhuber 1997 ), Deep and Extreme Learning ( Schmidhuber 2015 ; Goodfellow et al. 2019 ), and Computer Vision, have the potential to meet increasing requirements for and by nowcast and forecast products, including

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Thomas P. Leahy, Francesc Pons Llopis, Matthew D. Palmer, and Niall H. Robinson

-moving vessels. Therefore, if there are some misclassified T-7 (Sippican) probes as Deep Blue (Sippican) or vice versa, the final outcome on fall-rate corrections will be minimal ( Kizu et al. 2011 ). 5. Conclusions and discussion This study has demonstrated that applying machine learning to the classification of XBT probe types allows for an improvement in the accuracy over the current state-of-the-art method ( Palmer et al. 2018 ). This approach also has the advantage that subjective a priori expertise is

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Tao Zhang, Wuyin Lin, Yanluan Lin, Minghua Zhang, Haiyang Yu, Kathy Cao, and Wei Xue

parameterization schemes, such as convection and cloud schemes. Meanwhile, new discoveries from the machine learning framework can be further verified through physics-based interpretation, having the potential to lead to a deeper understanding of the genesis mechanism of TCs. We plan to extend such applications in separate works. Acknowledgments This work is supported by the CMDV Project to Brookhaven National Laboratory under Contract DE-SC0012704 and Brookhaven National Laboratory’s Laboratory Directed

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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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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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