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Montgomery L. Flora, Corey K. Potvin, Patrick S. Skinner, Shawn Handler, and Amy McGovern

, ProbSevere v2.0, the system can now produce probabilistic guidance for separate severe weather hazards ( Cintineo et al. 2020 ). Using a convolutional neural network (CNN; LeCun et al. 1990 ), a deep learning technique, Lagerquist et al. (2020) produced a next-hour tornado prediction system with skill comparable to the ProbSevere system. In an idealized framework, Steinkruger et al. (2020) explored using ML methods to produce automated tornado warning guidance and found promising results. Random

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Amy McGovern, Christopher D. Karstens, Travis Smith, and Ryan Lagerquist

. (1982) demonstrate that strong low-level shear is needed to create long-lived storms. Rotunno et al. (1988) demonstrate that long-lived squall lines are dependent on the interaction of low-level shear and the surface cold pool. Weisman and Klemp (1982 , 1986) demonstrate that wind shear and buoyancy are critical to both storm mode and longevity. Houston and Wilhelmson (2011) numerically study the issue of storm longevity in a low-shear environment and demonstrate that a deep cold pool is

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John R. Mecikalski, Thea N. Sandmæl, Elisa M. Murillo, Cameron R. Homeyer, Kristopher M. Bedka, Jason M. Apke, and Chris P. Jewett

-to-be-severe) deep convection ( Cintineo et al. 2014 , 2020 ). Present state-of-the-art methods that integrate a combination of weather datasets rely on raw and derived geostationary satellite parameters, gridded radar observations and derived products [e.g., the Multi-Radar Multi-Sensor (MRMS) product suite; Zhang et al. 2016 ], and NWP model fields. With respect to severe weather nowcasting (0–1 h forecasting), Probability of Severe Convection (ProbSevere; Cintineo et al. 2014 , 2018 , 2020 ), the

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Allison Engblom, Kristin Timm, Raphael Mazzone, David Perkins, Teresa Myers, and Edward Maibach

study begins to address this research need by qualitatively examining Virginia local news viewers’ interest in learning about climate change from weathercasters, their understanding of climate change messages in on-air examples, and their reactions to on-air climate change content in a television weather forecast. 2. Literature review a. The perception of climate change as a distant threat When asked what comes to mind when they think of climate change (i.e., top-of-mind associations), Americans and

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Cong Wang, Ping Wang, Di Wang, Jinyi Hou, and Bing Xue

extrapolation problem as a video prediction problem and used deep learning techniques to model changes between radar images. Han et al. (2017) translated the convective system nowcasting problems into classification problems. They made radar reflectivity and environmental field information into 3 km × 3 km grids and trained a support vector machine (SVM) classifier for prediction. Herman and Schumacher (2018) used NOAA’s second-generation Global Ensemble Forecast System Reforecast dataset to train a

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Faisal Hossain, Matt Bonnema, Margaret Srinivasan, Ed Beighley, Alice Andral, Bradley Doorn, Indu Jayaluxmi, Susantha Jayasinghe, Yasir Kaheil, Bareerah Fatima, Nicholas Elmer, Luciana Fenoglio, Jerad Bales, Fabien Lefevre, Sébastien Legrand, Damien Brunel, and Pierre-Yves Le Traon

that hackathons tailored to enable rapid prototyping of real-world solutions for EAs using SWOT data are now timely. 6) Programs that encourage deeper engagement for EAs at academic or research centers for immersive learning or training in the United States/France are required for EA organizations and future SWOT user communities. 7) Close and more frequent mentoring support for EAs is needed as projects mature and they begin facing new challenges with data structure and processing. EAs will

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Yonggang Liu, Robert H. Weisberg, and Ruoying He

circulation and its air–sea interactions ( Weisberg et al. 2004 ). a. Self-organizing map and its applications in meteorology and oceanography Techniques for pattern detection in large oceanographic datasets are becoming increasingly important as datasets grow in size and complexity. The self-organizing map (SOM), an artificial neural network based on unsupervised learning, is an effective software tool of feature extraction ( Kohonen 1982 , 2001 ). It provides a nonlinear cluster analysis, mapping high

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Richard L. Bankert and Robert H. Wade

-only Advanced Very High Resolution Radiometer (AVHRR) classifier described in Tag et al. (2000) . Also, all visible and infrared channels (excluding the 13.3- μ m channel from GOES-12 ) were used in the classifier development. An instance-based classification algorithm employs a machine learning technique in which training datasets are stored in their entirety and a distance function is used to make predictions. For most purposes, operational users rely on the availability of near-real-time cloud

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Tobias Becker and Cathy Hohenegger

strong near the boundary layer, and decrease with height in the free troposphere (e.g., Lin and Arakawa 1997 ; Kuang and Bretherton 2006 ). Reasons are versatile: first, only those updrafts with weaker entrainment rates remain positively buoyant and grow deeper; second, updraft size increases with height, and larger updrafts are thought to have smaller entrainment rates (e.g., Morton et al. 1956 ); and third, updraft velocity increases with height, which is thought to imply that more vertical

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F. Chevallier, F. Chéruy, N. A. Scott, and A. Chédin

depends on the distance of f to the allowed functions g on B. Given a norm ‖·‖ on B and given ζ, the maximum error that we tolerate, we want: ∃ θ ∈ R p , ∀ x ∈ A,   ‖ f ( x ) − g ( θ , x )‖ < ζ. (2) The learning phase is devoted to the optimization of the MLP. That is, according to the representativity of the learning set, the algorithm selects a vector θ among all of the possible θ ’s. The so-called back-propagation algorithm enables us to derive the parameters in an iterative way

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