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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Stephen G. Penny, Jebb Q. Stewart, Amy McGovern, David Hall, John E. Ten Hoeve, Jason Hickey, Hung-Lung Allen Huang, John K. Williams, Kayo Ide, Philippe Tissot, Sue Ellen Haupt, Kenneth S. Casey, Nikunj Oza, Alan J. Geer, Eric S. Maddy, and Ross N. Hoffman

The Earth and environmental sciences (collectively Earth science in what follows) stand to benefit from leveraging rapid advances in artificial intelligence (AI) from diverse applied science fields due to the combination of fast paced increases in data availability and computational capabilities. Leveraging algorithms used in other fields—what might be called meta-transfer learning—is accelerating the use of AI for environmental data and Earth system applications. We summarize here the main

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Fernando Jaume-Santero, David Barriopedro, Ricardo García-Herrera, and Jürg Luterbacher

information from climate datasets have recently emerged as promising tools to reconstruct spatial fields, while preserving major features of the variability ( Carro-Calvo et al. 2021 ; Kadow et al. 2020 ; Vaccaro et al. 2021 ). Joining statistics and computer science, artificial intelligence (AI) is a multidisciplinary field with different areas of expertise such as machine learning ( LeCun et al. 2015 ; Kadow et al. 2020 ), and optimization ( Swarnkar and Swarnkar 2019 ; Soto et al. 2019 ). Regarding

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Amir Ouyed, Xubin Zeng, Longtao Wu, Derek Posselt, and Hui Su

Eq. ( 11 )]. Values were computed for 0000 UTC 1 Jan 2006, Δ t = 60 min, and P = 850 hPa. The structure of the error distribution also raises some questions about overfitting. In this study, we derived an error distribution, under the assumption of an available ground truth V truth . In an operational setting, we would not calculate the error ϵ = V NWP − V truth , since we will use the NWP field itself, e.g., V NWP = V truth + ϵ , rather than constructing an artificial V NWP

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Long Jin, Cai Yao, and Xiao-Yan Huang

recognized ( Nohara and Tanaka 2004 ; Zhou and Johnny 2006 ). At present, traditional mathematic modeling methods, such as multivariate analysis and time series analysis are widely used in statistical prediction and dynamical–statistical prediction ( Zhou and Huang 1997 ; Ding et al. 2002 ), in which the future state of a prediction object is forecasted using a statistical prediction equation ( Zhou and Huang 1997 ; Ding et al. 2002 ). With the development of the artificial intelligence technique

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Steven D. Campbell and Stephen H. Olson

VOL. 4, NO. I JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY MARCH 1987Recognizing Low-Altitude Wind Shear Hazards from Doppler Weather Radar: An Artificial Intelligence Approach* STEVEN D. CAMPBELL AND STEPHEN H. OLSONLincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA 02173(Manuscript received 23 April 1986, in final form 12 December 1986)ABSTRACT This paper describes an artificial

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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

Modern artificial intelligence (AI) techniques can aid forecasters on a wide variety of high-impact weather phenomena. Weather significantly impacts society for better and for worse. For example, severe weather hazards caused over $7.9 billion of property damage in 2015 ( National Oceanic and Atmospheric Administration/National Centers for Environmental Information 2016 ; CoreLogic 2016 ). The National Academies of Sciences, Engineering, and Medicine (2016) cites improving forecasting of

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EDITORIALArtificial IntelligencemThe Challenge and the Opportunity The interdisciplinary studies commonly referred to as "Artificial Intelligence" haveresulted in new ways to use computers that are potentially as far-reaching as anyprevious advance in automatic data processing. The challenge to the meteorologicalcommunity is to maintain the proper balance between unrealistic expectations andthe negativism that has been the usual reaction to exaggerated claims. The research efforts

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Timothy Olander, Anthony Wimmers, Christopher Velden, and James P. Kossin

, especially in areas where the ADT has struggled or has not been as thoroughly examined and improved. This paper reports on the development of a MLP model to augment the ADT intensity estimation process. This “artificial intelligence (AI)” enhanced ADT (AiDT) model is executed after the real-time ADT processing sequence is completed for an active TC. It modifies the ADT intensity estimate by applying MLP techniques to the ADT analysis parameters familiar to operational TC users. Many different MLP

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Donald S. Frankel, James Stark Draper, James E. Peak, and J. Carr McLeod

The Artificial Intelligence (AI) Needs Analysis Workshop provided a forum for representatives from government agencies and private industries to explore ways to use Al to solve various problems. Past accomplishments using Al were presented, and areas where Al might be used in future efforts were identified. Each agency suggested their particular problem areas where Al might be expected to solve difficult problems. Reports from three working groups suggested future research areas in which Al has the potential for success.

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Sue Ellen Haupt, David John Gagne, William W. Hsieh, Vladimir Krasnopolsky, Amy McGovern, Caren Marzban, William Moninger, Valliappa Lakshmanan, Philippe Tissot, and John K. Williams

Artificial intelligence (AI) and machine learning (ML) have become important tools for environmental scientists and engineers, both in research and in applications. Although these methods have become quite popular in recent years, they are not new; AI and ML have been used in the environmental sciences (ES) for decades. The American Meteorological Society (AMS) Committee on Artificial Intelligence Applications to Environmental Science (AI Committee) has been promoting, advancing, and

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