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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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Frank Woodcock and Diana J. M. Greenslade

superseded MOS as the official objective forecast guidance in March 2005. This study employs OCF to generate 24-h predictions of significant wave height at 18 wave observation locations around Australia. Direct model output forecasts, interpolated from numerical wave models (five models for deep-water sites and four for shallow-water sites), provide the underlying component forecasts in the OCF composite. The main objective is to investigate whether OCF improves on its component forecasts. Additionally

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Joseph Sedlar, Laura D. Riihimaki, Kathleen Lantz, and David D. Turner

and (c) low cumulus periods. At least 120 min of a consecutive cloud event must have been observed to be included in the analysis. For low stratiform, there were 311 events; for low cumulus, there were 124. Data are from 2014 to 2018 at ARM SGP. The other cloud regimes in Fig. 2a are much less frequent than low clouds and cirrus. To improve separability of cloud regimes during the training phase of the machine-learning classifier, congestus clouds are combined with deep convection, and

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Wei Zhang, Bing Fu, Melinda S. Peng, and Tim Li

, 1998 ; Fu et al. 2007 ). Over the decades, significant advancements have been made in understanding the physical mechanisms and processes involved in TC genesis ( Gray 1968 ; McBride 1981 ; Craig and Gray 1996 ; Fu et al. 2007 ; Wang et al. 2007 ; Peng et al. 2012 ; Fu et al. 2012 ). Gray (1968) suggested several favorable environmental parameters for TC genesis: a sufficiently deep warm ocean layer, conditional instability through a deep atmospheric layer, higher

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Richard L. Bankert, Cristian Mitrescu, Steven D. Miller, and Robert H. Wade

—brightness temperature greater than 273 K, 2) supercooled water or mixed phase—composed entirely of supercooled water droplets or both ice and supercooled, and 3) glaciated (optically thick ice) clouds—entirely ice crystals or glaciated tops (e.g., deep convection)—are applied and the pixel’s cloud type is assigned. 4. GOES cloud classifier—Implicit physics Using a supervised learning method that was first applied to AVHRR data ( Tag et al. 2000 ), an IP cloud classifier has been developed and further refined for

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A. K. Showalter

fig. 2 of the Byers and Rodebush reportimply a much deeper on-shore flow than off-shoreflow during a twenty-four hour period. If a value of25 were arbitrarily added to all of the convergenceand divergence values thus displacing the zero lines infig. 2 downward approximately 25 points, the relativedepths of the day and night breeze would appear tobe approximately correct. Most meteorologists arefamiliar with the fact that on-shore breezes arestronger than off-shore land breezes, but my discussion

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