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Jun Yang, Weitao Lyu, Ying Ma, Yijun Zhang, Qingyong Li, Wen Yao, and Tianshu Lu

training method for limited sample data and realized the application of the convolutional architecture for fast feature embedding (“Caffe”) deep-learning framework ( Jia et al. 2014 ) in the classification of total-sky cloud images. In this paper, we adopted the trained network parameters and deep-learning model provided by Zhang to perform cloud-type classification. The entire TCIS image set is used to assess the classification accuracy, and Table 1 shows the confusion matrix of the classification

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Ariel E. Cohen, Richard L. Thompson, Steven M. Cavallo, Roger Edwards, Steven J. Weiss, John A. Hart, Israel L. Jirak, William F. Bunting, Jaret W. Rogers, Steven F. Piltz, Alan E. Gerard, Andrew D. Moore, Daniel J. Cornish, Alexander C. Boothe, and Joel B. Cohen

death. As an integral member of the weather enterprise, academia not only lays the foundation for learning within the university classroom, but also plays a key role in fostering research development. As such, under the broader umbrella of academia, the university classroom can be considered as an incubator for R2O. Specifically, the classroom setting provides knowledge, guidance, and practice for students to successfully 1) conduct operationally relevant research as a part of their academic and

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Michael Sprenger, Sebastian Schemm, Roger Oechslin, and Johannes Jenkner

statistical methods with NWP-based forecasts is to apply the Widmer index to the model output. In a more modern terminology, this directly leads to a machine learning (ML) approach for foehn prediction (see Hastie et al. 2009 ). Other names, which essentially mean the same thing, are applied predictive modeling, artificial intelligence, and statistical learning ( Kuhn and Johnson 2013 ). There are many ML methods that could be ( Hastie et al. 2009 ), and indeed have been, applied within a meteorological

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Noah D. Brenowitz, Tom Beucler, Michael Pritchard, and Christopher S. Bretherton

, including a machine-learning parameterization, should capture the dependence of convection to these parameters. One such parameter is the LTS: LTS = θ ⁡ ( 700   hPa ) − SST , where θ is the potential temperature and SST is the sea surface temperature. Low LTS indicates the lower troposphere is conditionally unstable, favoring deep convection. A second controlling parameter is the midtropospheric moisture, defined by Q = ∫ 850 hPa 550 hPa q T   d p g . Cumulus updrafts entrain surrounding air as they

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James R. Baker and Thomas F. Jordan

dependencecould be used in models of seiches or tides. Csanadyand Shaw (1980) found and analyzed the solution fordrift current caused by a suddenly applied constantwind stress, for viscosity that depends on time butnot depth, in the limit of infinitely deep water; theyuse it to describe a turbulent Ekman layer. Madsen(1977) had found a solution describing a turbulentboundary layer near the surface by using viscositythat increases linearly with depth but does notdepend on time, also for infinitely deep water

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Anthony E. Akpan, Mahesh Narayanan, and T. Harinarayana

; Spichak et al. 2011 ). To make reliable predictions, the requisite condition that the ANN method needs while solving complicated problems is to have a large volume of input–output data pairs for it to use in training the network instead of mathematical equations and other empirical relations ( Ali Akcayol and Cinar 2005 ). Currently, the ANN technique is the most popular artificial learning tool in the geosciences, with applications including automatic seismic wave arrival time picking ( Dai and

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John F. Griffiths

1428 JOURNAL OF APPLIED METEOROLOGY VOLUME20The Learning Process Related to Architecture and the Atmosphere JOHN F. GRIFFITHSTexas .4&M University, College Station 77843(Manuscript received 9 February 1981, in final form 18 March 1981)ABSTRACT There has been a rapidly growing awareness in the past few years of the role that the atmospheric scientistcan play in assisting the architect to achieve

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Faisal M. Qamer, Tsegaye Tadesse, Mir Matin, Walter L. Ellenburg, and Benjamin Zaitchik

discussed innovative approaches for regional crop mapping using cloud-based remote sensing and machine learning in the region. The presentations included lessons learned from new and advanced techniques in remote sensing applications for crop area assessments and an advanced deep learning algorithm (e.g., convolutional neural network) to predict vegetation levels across large and heterogeneous geographic regions. In addition, open-access cloud-based solutions for crop area mapping for food security

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Marilia M. F. de Oliveira, Nelson Francisco F. Ebecken, Jorge Luiz Fernandes de Oliveira, and Isimar de Azevedo Santos

structures. Interactions between meteorological (atmospheric pressure, wind, sea surface temperature) and oceanic (salinity and deep sea) variables affect the regular tides and modify the sea level conditions in coastal regions, mainly in restricted waters such as bays. Tropical cyclones and extratropical storms are the main cause of storm surges that can produce damage through high waves and sprawling water over large coastal areas in a single storm. The principal factors involved in the generation and

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Dan Lu, Goutam Konapala, Scott L. Painter, Shih-Chieh Kao, and Sudershan Gangrade

, 507 – 525 , https://doi.org/10.1002/hyp.3360090504 . 10.1002/hyp.3360090504 Beven , K. , 2001 : Rainfall-Runoff Modeling: The Primer . John Wiley & Sons, 360 pp . Boyraz , C. , and S. N. Engin , 2018 : Streamflow prediction with deep learning. Sixth Int. Conf. on Control Engineering Information Technology , Istanbul, Turkey, IEEE, 1–5 , https://doi.org/10.1109/CEIT.2018.8751915 . 10.1109/CEIT.2018.8751915 Clark , M. P. , and Coauthors , 2017 : The evolution of process

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