Готові списки джерел за темами / Storm prediction

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  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Добірка наукової літератури з теми "Storm prediction"

Автор: Grafiati
Опубліковано: 25 січня 2025

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Статті в журналах з теми "Storm prediction"

1

Siek, M., and D. P. Solomatine. "Nonlinear chaotic model for predicting storm surges." Nonlinear Processes in Geophysics 17, no. 5 (2010): 405–20. http://dx.doi.org/10.5194/npg-17-405-2010.

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Анотація:
Abstract. This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea,
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2

Ge, Zongyuan. "Description, Origination and Prediction of Geomagnetic Storm." Highlights in Science, Engineering and Technology 72 (December 15, 2023): 217–30. http://dx.doi.org/10.54097/cpf07c70.

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Анотація:
The research of geomagnetic storm has developed rapidly, and many new geomagnetic storm prediction methods have appeared. In order to summarize the previous research on geomagnetic storms, and points out the improvement direction of several existing forecasting methods. This paper uses the method of literature research to introduce the basic knowledge of geomagnetic storms, the interplanetary origin, and three forecasting methods: analysis of the change of cosmic ray flux to predict geomagnetic storms, evaluation of neural networks to analyze solar wind data for geomagnetic storm prediction an
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3

Tausia, Javier, Camus Paula, Ana Rueda, et al. "SHORT TERM SPATIALLY DENSE PREDICTION OF STORM SURGE ALONG THE NEW ZEALAND COASTLINE." Coastal Engineering Proceedings, no. 37 (September 1, 2023): 119. http://dx.doi.org/10.9753/icce.v37.management.119.

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Анотація:
Storm surge is the rise in water level generated by wind and atmospheric pressure changes associated with tropical or mid-latitude storms. In conjunction with tides, it is one major driver of coastal flooding associated with storms events. Because local inundation is strongly modulated by the local shape of the coastline and the bathymetric slope, accurate storm surge prediction by the mean of traditional numerical models requires the use of very fine grids and is hence very resource intensive. This means that the performance of a live prediction system based on such methods will likely be sub
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4

Tang, Rongxin, Fantao Zeng, Zhou Chen, Jing-Song Wang, Chun-Ming Huang, and Zhiping Wu. "The Comparison of Predicting Storm-Time Ionospheric TEC by Three Methods: ARIMA, LSTM, and Seq2Seq." Atmosphere 11, no. 4 (2020): 316. http://dx.doi.org/10.3390/atmos11040316.

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Анотація:
Ionospheric structure usually changes dramatically during a strong geomagnetic storm period, which will significantly affect the short-wave communication and satellite navigation systems. It is critically important to make accurate ionospheric predictions under the extreme space weather conditions. However, ionospheric prediction is always a challenge, and pure physical methods often fail to get a satisfactory result since the ionospheric behavior varies greatly with different geomagnetic storms. In this paper, in order to find an effective prediction method, one traditional mathematical metho
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5

Salmun, H., A. Molod, K. Wisniewska, and F. S. Buonaiuto. "Statistical Prediction of the Storm Surge Associated with Cool-Weather Storms at the Battery, New York." Journal of Applied Meteorology and Climatology 50, no. 2 (2011): 273–82. http://dx.doi.org/10.1175/2010jamc2459.1.

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Abstract The winter and early spring weather in the New York City metropolitan region is highly influenced by extratropical storm systems, and the storm surge associated with these systems is one of the main factors contributing to inundation of coastal areas. This study demonstrates the predictive capability of an established statistical relationship between the “storm maximum” storm surge associated with an extratropical storm system and the “average maximum” significant wave height during that storm. Data from publicly available retrospective forecasts of sea level pressure and wave heights
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6

Chakraborty, Shibaji, and Steven Karl Morley. "Probabilistic prediction of geomagnetic storms and the Kp index." Journal of Space Weather and Space Climate 10 (2020): 36. http://dx.doi.org/10.1051/swsc/2020037.

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Анотація:
Geomagnetic activity is often described using summary indices to summarize the likelihood of space weather impacts, as well as when parameterizing space weather models. The geomagnetic index K p in particular, is widely used for these purposes. Current state-of-the-art forecast models provide deterministic K p predictions using a variety of methods – including empirically-derived functions, physics-based models, and neural networks – but do not provide uncertainty estimates associated with the forecast. This paper provides a sample methodology to generate a 3-hour-ahead K p prediction with unc
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7

Šaur, David, and Juan Carlos Beltrán-Prieto. "Algorithm of conversion of meteorological model parameters." MATEC Web of Conferences 292 (2019): 01032. http://dx.doi.org/10.1051/matecconf/201929201032.

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Анотація:
This article is focused on the forecasting severe storms with the Algorithm of Storm Prediction as a new forecasting tool for the prediction of the convective precipitation, severe storm phenomena and the risk of flash floods. The first chapter contains information about two applications on which basis are computed forecast ouptuts of this algorithm. Further, this chapter is also objected on more detailed descripition of the second application known as the Algorithm of conversion of meteorological model parameters . Predictive outputs generated by this algorithm are verified on 63 storm events
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8

Barks, C. Shane. "Adjustment of Regional Regression Equations for Urban Storm-Runoff Quality Using At-Site Data." Transportation Research Record: Journal of the Transportation Research Board 1523, no. 1 (1996): 141–46. http://dx.doi.org/10.1177/0361198196152300117.

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Regional regression equations have been developed to estimate urban storm-runoff loads and mean concentrations using a national data base. Four statistical methods using at-site data to adjust the regional equation predictions were developed to provide better local estimates. The four adjustment procedures are a single-factor adjustment, a regression of the observed data against the predicted values, a regression of the observed values against the predicted values and additional local independent variables, and a weighted combination of a local regression with the regional prediction. Data col
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9

Qiao, Yuezhong, Yaguang Zhuo, and Wenming Zhang. "Informer Model based Wind Power Forecast with Tropical Storms Present." Journal of Physics: Conference Series 2717, no. 1 (2024): 012005. http://dx.doi.org/10.1088/1742-6596/2717/1/012005.

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Abstract When severe tropical storms pass through, regional wind speeds fluctuate greatly, and the volatility of wind farm output also increases significantly. At the same time, the duration of tropical storms is long, and it is difficult for short-term time series data prediction models to be effective, in which case the unstable output of wind turbines will have a greater impact on power system dispatching. This paper first examines the characteristics of tropical storm movement, namely the change in wind speed, and then uses the Informer long time series data prediction model to predict the
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10

Łoś, Marcelina, Kamil Smolak, Guergana Guerova, and Witold Rohm. "GNSS-Based Machine Learning Storm Nowcasting." Remote Sensing 12, no. 16 (2020): 2536. http://dx.doi.org/10.3390/rs12162536.

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Анотація:
Nowcasting of severe weather events and summer storms, in particular, are intensively studied as they have great potential for large economic and societal losses. Use of Global Navigation Satellite Systems (GNSS) observations for weather nowcasting has been investigated in various regions. However, combining the vertically integrated water vapour (IWV) with vertical profiles of wet refractivity derived from GNSS tomography has not been exploited for short-range forecasts of storms. In this study, we introduce a methodology to use the synergy of IWV and tomography-based vertical profiles to pre
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Дисертації з теми "Storm prediction"

1

Lee, Michael. "Rapid Prediction of Tsunamis and Storm Surges Using Machine Learning." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103154.

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Анотація:
Tsunami and storm surge are two of the main destructive and costly natural hazards faced by coastal communities around the world. To enhance coastal resilience and to develop effective risk management strategies, accurate and efficient tsunami and storm surge prediction models are needed. However, existing physics-based numerical models have the disadvantage of being difficult to satisfy both accuracy and efficiency at the same time. In this dissertation, several surrogate models are developed using statistical and machine learning techniques that can rapidly predict a tsunami and storm surge
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2

Suyanto, Adhi. "Estimating the exceedance probabilities of extreme floods using stochastic storm transportation and rainfall - runoff modelling." Thesis, University of Newcastle Upon Tyne, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386794.

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Анотація:
Methods of estimating floods with return periods of up to one hundred years are reasonably well established, and in the main rely on extrapolation of historical flood data at the site of interest. However, extrapolating the tails of fitted probability distributions to higher return periods is very unreliable and cannot provide a satisfactory basis for extreme flood estimation. The probable maximum flood concept is an alternative approach, which is often used for critical cases such as the location of nuclear power plants, and is viewed as a consequence of a combination of a probable maximum pr
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3

Hanson, Clair Elizabeth. "A cyclone climatology of the North Atlantic and its implications for the insurance market." Thesis, University of East Anglia, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365137.

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4

Jafari, Alireza. "Analysis and Prediction of Wave Transformation from Offshore into the Surfzone under Storm Condition." Thesis, Griffith University, 2013. http://hdl.handle.net/10072/366745.

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Surfzone wave transformation under storm conditions is investigated through field and laboratory measurements in this study. The observations have been used to examine currently available models of wave energy dissipation. Detailed field data has been collected by means of a novel method which was first introduced by Nielsen (1988). This method has been utilised through a common program between Griffith University and The University of Queensland at The Spit on the Gold Coast in Southeast Queensland. The facility primarily consists of a manometer tube array with 12 different manometer tube len
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5

Anderson, Ian. "Improving Detection And Prediction Of Bridge Scour Damage And Vulnerability Under Extreme Flood Events Using Geomorphic And Watershed Data." ScholarWorks @ UVM, 2018. https://scholarworks.uvm.edu/graddis/823.

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Bridge scour is the leading cause of bridge damage nationwide. Successfully mitigating bridge scour problems depends on our ability to reliably estimate scour potential, design safe and economical foundation elements that account for scour potential, identify vulnerabilities related to extreme events, and recognize changes to the environmental setting that increase risk at existing bridges. This study leverages available information, gathered from several statewide resources, and adds watershed metrics to create a comprehensive, georeferenced dataset to identify parameters that correlate to br
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6

Anderson, John W. "An analysis of a dust storm impacting Operation Iraqi Freedom, 25-27 March 2003." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FAnderson.pdf.

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7

Zhu, Dan. "Electric Distribution Reliability Analysis Considering Time-varying Load, Weather Conditions and Reconfiguration with Distributed Generation." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/26557.

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This dissertation is a systematic study of electric power distribution system reliability evaluation and improvement. Reliability evaluation of electric power systems has traditionally been an integral part of planning and operation. Changes in the electric utility coupled with aging electric apparatus create a need for more realistic techniques for power system reliability modeling. This work presents a reliability evaluation technique that combines set theory and Graph Trace Analysis (GTA). Unlike the traditional Markov approach, this technique provides a fast solution for
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8

Geggis, Lorna M. "Do you see what I mean? : Measuring consensus of agreement and understanding of a National Weather Service informational graphic." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002119.

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9

Frifra, Ayyoub. "Assessing and predicting extreme events in Western France." Electronic Thesis or Diss., Nantes Université, 2024. http://www.theses.fr/2024NANU2012.

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Анотація:
Les régions côtières sont de plus en plus exposées à des événements extrêmes en raison des impacts combinés du changement climatique et de l’urbanisation. Cette thèse examine les risques côtiers le long de la côte ouest de la France, en mettant l’accent sur la prévision des tempêtes et de la simulation de la vulnérabilité future aux inondations côtières. Des approches d’apprentissage automatique et d’apprentissage profond ont été utilisées pour améliorer la prédiction des aléas et évaluer les risques futurs. Une nouvelle méthodologie combinant Long Short-Term Memory (LSTM) et Extreme Gradient
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10

Kimock, Joseph. "Predicting commissary store success." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/44595.

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Approved for public release; distribution is unlimited<br>What external factors affect a commissary store’s success? This thesis analyzes the impact of demographics, local prices and competitors on commissary stores sales per square foot. These three factors were found to account for approximately 60 percent of the variation in sales per square foot between different store locations. The only influential groups for commissary success were active duty members, retirees, and their dependents-Reservists and National Guard members had no impact. Equally important was the price differential between
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Книги з теми "Storm prediction"

1

Fine, Gary Alan. Authors of the storm: Meteorologists and the culture of prediction. University of Chicago Press, 2007.

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2

Talukder, Jyotirmoy. Living with cyclone: Study on storm surge prediction and disaster preparedness. Community Development Library, 1992.

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3

Funakoshi, Yuji. Coupling a finite element storm surge model of the North Carolina sounds with operational ocean and weather prediction models. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Ocean Service, Office of Coast Survey, Coast Survey Development Laboratory, 2010.

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4

John D. Cox - undifferentiated. Storm watchers: The turbulent history of weather prediction from Franklin's kite to El Niño. John Wiley, 2002.

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5

Yum, Sang Guk. Extreme Storm Surge Return Period Prediction Using Tidal Gauge Data and Estimation of Damage to Structures from Storm-Induced Wind Speed in South Korea. [publisher not identified], 2019.

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6

Guo, Lei. Semi-empirical prediction of pesticide loading in the Sacramento and San Joaquin Rivers during winter storm seasons. California Environmental Protection Agency, Dept. of Pesticide Regulation, Environmental Monitoring Branch, 2003.

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7

K, Prasad. Environmental and synoptic conditions associated with nor'westers and tornadoes in Bangladesh: An appraisal based on numerical weather prediction (NWP) guidance products. SAARC Meteorological Research Centre, 2006.

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8

P, Kauahikaua James, Tilling Robert I, and Geological Survey (U.S.), eds. The story of the Hawaiian Volcano Observatory: A remarkable first 100 years of tracking eruptions and earthquakes. U.S. Dept. of the Interior, U.S. Geological Survey, 2011.

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9

Colin, Wilson. Serial killer investigations: The story of forensics and profiling through the hunt for the world's worst murderers. Hylas Pub., 2006.

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10

United States. Dept. of Agriculture., United States. Federal Emergency Management Agency., and United States. Dept. of Commerce., eds. Saving lives with an all-hazard warning network. U.S. Dept. of Agriculture, 1999.

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Частини книг з теми "Storm prediction"

1

Sagalyn, Rita C., and Sidney A. Bowhill. "Progress in Geomagnetic Storm Prediction." In Environmental Effects on Spacecraft Positioning and Trajectories. American Geophysical Union, 2013. http://dx.doi.org/10.1029/gm073p0157.

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2

James, A., and D. J. Elliott. "The Modelling of Storm Water Pollution." In Water Pollution: Modelling, Measuring and Prediction. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3694-5_11.

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3

Burrows, R., and W. Wang. "Determination of Spill Characteristics of Combined Sewer Overflows and Coastal Storm Outfalls." In Water Pollution: Modelling, Measuring and Prediction. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3694-5_19.

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4

Ciavola, Paolo, Oscar Ferreira, Ap Van Dongeren, Jaap Van Thiel de Vries, Clara Armaroli, and Mitchell Harley. "Prediction of Storm Impacts on Beach and Dune Systems." In Hydrometeorological Hazards. John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118629567.ch3d.

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5

Wang, Qian, Jianhua Chen, and Kelin Hu. "Storm Surge Prediction for Louisiana Coast Using Artificial Neural Networks." In Neural Information Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46675-0_43.

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6

Lin, Fudong, Xu Yuan, Yihe Zhang, et al. "Comprehensive Transformer-Based Model Architecture for Real-World Storm Prediction." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43430-3_4.

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7

Albasri, Mohamed Abdulrasool Juma, Sini Raj Pulari, Shaima Shawqi Almeer, and Shriram K. Vasudevan. "AI-Powered Dust Storm Movement Prediction System Using Satellite Imagery." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4149-6_23.

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8

Mannan, Abdul, and Arjumand Habib. "Understanding the Properties of Cyclonic Storm ‘Aila’Using NWP Technique." In Monitoring and Prediction of Tropical Cyclones in the Indian Ocean and Climate Change. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-7720-0_32.

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Dube, S. K., A. D. Rao, Jismy Poulose, M. Mohapatra, and T. S. Murty. "Storm Surge Inundation in South Asia under Climate Change Scenarios." In Monitoring and Prediction of Tropical Cyclones in the Indian Ocean and Climate Change. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-7720-0_30.

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Froude, Lizzie S. R., and Robert J. Gurney. "Storm Prediction Research and its Application to the Oil/Gas Industry." In NATO Science for Peace and Security Series C: Environmental Security. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-3692-6_16.

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Тези доповідей конференцій з теми "Storm prediction"

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Nunavath, Vimala, and Sindre Kristoffersen Olsen. "Prediction of Storm Water Overflow in Municipality Using Machine Learning*." In 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS). IEEE, 2024. http://dx.doi.org/10.1109/icds62089.2024.10756334.

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Kamagata, Abubakar Hamisu, Dharm Singh Jat, Saravanakumar Paramasivam, Attlee M. Gamundani, and Muhammad Zahir Ramli. "Simulation and Prediction of Machine Learning-Based Storm Surge Forecasting Model." In 2024 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC). IEEE, 2024. https://doi.org/10.1109/etncc63262.2024.10767516.

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Davis, Ian, Hadi Hemmati, Richard C. Holt, Michael W. Godfrey, Douglas Neuse, and Serge Mankovskii. "Storm prediction in a cloud." In 2013 5th International Workshop on Principles of Engineering Service-Oriented Systems (PESOS). IEEE, 2013. http://dx.doi.org/10.1109/pesos.2013.6635975.

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Prime, Thomas. "Relocatable Tide Prediction and Storm Surge Forecasting." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77926.

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Анотація:
The marine environment represents a large and important resource for communities around the world. However, the marine environment increasingly presents hazards that can have a large negative impact. One important marine hazard results from storms and their accompanying surges. This can lead to coastal flooding, particularly when surge and astronomical high tides align, with resultant impacts such as destruction of property, saline degradation of agricultural land and coastal erosion. Where tide and storm surge information are provided and accessed in a timely, accurate and understandable way,
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5

Wolfson, Marilyn M., William J. Dupree, Roy M. Rasmussen, Matthias Steiner, Stanley G. Benjamin, and Steven S. Weygandt. "Consolidated storm prediction for aviation (CoSPA)." In 2008 Integrated Communications, Navigation and Surveillance Conference (ICNS). IEEE, 2008. http://dx.doi.org/10.1109/icnsurv.2008.4559190.

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Warnock, April M., Christopher S. Ruf, and Mary Morris. "Storm surge prediction with cygnss winds." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8127624.

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Pirklbauer, Kevin, and Rainhard Dieter Findling. "Storm Operation Prediction: Modeling the Occurrence of Storm Operations for Fire Stations." In 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 2021. http://dx.doi.org/10.1109/percomworkshops51409.2021.9430944.

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Russell, Carl. "Predicting Airspace Capacity Impacts Using the Consolidated Storm Prediction for Aviation." In 10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference. American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-9163.

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Qahwaji, Rami, and Tufan Colak. "Prediction of halloween storm with automated solar activity prediction tool (ASAP)." In 2009 4th International Conference on Recent Advances in Space Technologies (RAST). IEEE, 2009. http://dx.doi.org/10.1109/rast.2009.5158277.

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Zhang, Yuhang, Weimin Zhen, Liang Chen, et al. "Ionospheric TEC Storm Prediction Based on AdaBoost-BP." In 2022 Cross Strait Radio Science & Wireless Technology Conference (CSRSWTC). IEEE, 2022. http://dx.doi.org/10.1109/csrswtc56224.2022.10098394.

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Звіти організацій з теми "Storm prediction"

1

Torres, Marissa, Norberto Nadal-Caraballo, and Alexandros Taflanidis. Rapid tidal reconstruction for the Coastal Hazards System and StormSim part II : Puerto Rico and U.S. Virgin Islands. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41482.

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Анотація:
This Coastal and Hydraulics Engineering Technical Note (CHETN) describes the continuing efforts towards incorporating rapid tidal time-series reconstruction and prediction capabilities into the Coastal Hazards System (CHS) and the Stochastic Storm Simulation System (StormSim). The CHS (Nadal-Caraballo et al. 2020) is a national effort for the quantification of coastal storm hazards, including a database and web tool (https://chs.erdc.dren.mil) for the deployment of results from the Probabilistic Coastal Hazard Analysis (PCHA) framework. These PCHA products are developed from regional studies s
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2

Torres, Marissa, and Norberto Nadal-Caraballo. Rapid tidal reconstruction with UTide and the ADCIRC tidal database. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41503.

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The quantification of storm surge is vital for flood hazard assessment in communities affected by coastal storms. The astronomical tide is an integral component of the total still water level needed for accurate storm surge estimates. Coastal hazard analysis methods, such as the Coastal Hazards System and the StormSim Coastal Hazards Rapid Prediction System, require thousands of hydrodynamic and wave simulations that are computationally expensive. In some regions, the inclusion of astronomical tides is neglected in the hydrodynamics and tides are instead incorporated within the probabilistic f
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3

Cialone, Mary, Jessamin Straub, Britt Raubenheimer, et al. A large-scale community storm processes field experiment : the During Nearshore Event Experiment (DUNEX) overview reference report. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/46548.

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The DUring Nearshore Event EXperiment (DUNEX) was a series of large-scale nearshore coastal field experiments focused on during-storm, nearshore coastal processes. The experiments were conducted on the North Carolina coast by a multidisciplinary group of over 30 research scientists from 2019 to 2021. The overarching goal of DUNEX was to collaboratively gather information to improve understanding of the interactions of coastal water levels, waves, and flows, beach and dune evolution, soil behavior, vegetation, and groundwater during major coastal storms that affect infrastructure, habitats, and
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4

O'Neill, Clare, Andy Saulter, Christopher Stokes, and Breogán Gómez. Application of bias correction to the Met Office operational storm surge forecast. Met Office, 2025. https://doi.org/10.62998/tymo5223.

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The Met Office runs an operational storm surge forecast system for the UK based on a 7 km configuration of NEMO forced by wind and air pressure from Met Office global atmosphere forecasts. The water level forecast comprises the surge residual from the model added to the harmonic tide prediction derived from observations. A new automated bias correction has been added to the system to improve forecast performance. The correction has 3 components: a mean sea level correction to account for different mean sea levels in the forced and unforced model runs, a monthly correction to account for the at
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5

Chang, Edmund Kar-Man. Final Scientific/Technical Report for Subseasonal to Seasonal Prediction of Extratropical Storm Track Activity over the U.S. using NMME data. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1405606.

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6

Kimock, Joseph. Predicting Commissary Store Success. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada621046.

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7

Wissink, Andrew, Jude Dylan, Buvana Jayaraman, et al. New capabilities in CREATE™-AV Helios Version 11. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40883.

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CREATE™-AV Helios is a high-fidelity coupled CFD/CSD infrastructure developed by the U.S. Dept. of Defense for aeromechanics predictions of rotorcraft. This paper discusses new capabilities added to Helios version 11.0. A new fast-running reduced order aerodynamics option called ROAM has been added to enable faster-turnaround analysis. ROAM is Cartesian-based, employing an actuator line model for the rotor and an immersed boundary model for the fuselage. No near-body grid generation is required and simulations are significantly faster through a combination of larger timesteps and reduced cost
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8

Ginis, Isaac, Deborah Crowley, Peter Stempel, and Amanda Babson. The impact of sea level rise during nor?easters in New England: Acadia National Park, Boston Harbor Islands, Boston National Historical Park, and Cape Cod National Seashore. National Park Service, 2024. http://dx.doi.org/10.36967/2304306.

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This study examines the potential impact of sea level rise (SLR) caused by climate change on the effects of extratropical cyclones, also known as nor?easters, in four New England coastal parks: Acadia National Park (ACAD), Boston Harbor Islands National Recreation Area (BOHA), Boston National Historical Park (BOST) and Cape Cod National Seashore (CACO). A multi-method approach is employed, including a literature review, observational data analysis, coupled hydrodynamic-wave numerical modeling, 3D visualizations, and communication of findings. The literature review examines previous studies of
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9

Mendillo, Michael, and Jules Aarons. A Plan to Develop Predictive Capability for Equatorial Scintillation Storms. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada323511.

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10

Mendillo, Michael, and Jules Aarons. A Plan to Develop Predictive Capability for Equatorial Scintillation Storms. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada328537.

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