Готові списки джерел за темами / Storm prediction

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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 (September 6, 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, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.
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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 and using very low frequency signal to predict geomagnetic storms. The advantages and disadvantages of the above three forecasting methods are compared. According to the analysis, one can have a relatively comprehensive understanding of geomagnetic storms and grasp the basic ideas of the existing geomagnetic storm forecast methods, the forecast lead and accuracy of geomagnetic storm can be achieved by combining many existing forecasting methods. A deeper study of the relationship between Earth and the Sun could also lead to the discovery of new methods for predicting geomagnetic storms.
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3

Tausia, Javier, Camus Paula, Ana Rueda, Fernando Mendez, Sébastien Delaux, Karin Bryan, Antonio Cofino, Carine Costa, Jorge Perez, and Remy Zingfogel. "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 subject to a trade-off between prediction accuracy, prediction speed and cost (Wang et al., 2009). Several publications have demonstrated the potential of machine learning approaches for the prediction of storm surge (e.g. (Tiggeloven et al., 2021), (Cagigal et al, 2020)). However, the developed methods often focus on local predictors and aim at predicting storm surge at a single location at a time. In this study, we explore the use of several data driven methods as an alternative to numerical methods to predict storm surge along the coast of New Zealand.
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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 (March 25, 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 method (autoregressive integrated moving average—ARIMA) and two deep learning algorithms (long short-term memory—LSTM and sequence-to-sequence—Seq2Seq) are investigated for the short-term predictions of ionospheric TEC (Total Electron Content) under different geomagnetic storm conditions based on the MIT (Massachusetts Institute of Technology) madrigal observation from 2001 to 2016. Under the extreme condition, the performance limitation of these methods can be found. When the storm is stronger, the effective prediction horizon of the methods will be shorter. The statistical analysis shows that the LSTM can achieve the best prediction accuracy and is robust for the accurate trend prediction of the strong geomagnetic storms. In contrast, ARIMA and Seq2Seq have relatively poor performance for the prediction of the strong geomagnetic storms. This study brings new insights to the deep learning applications in the space weather forecast.
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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 (February 1, 2011): 273–82. http://dx.doi.org/10.1175/2010jamc2459.1.

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Анотація:
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, along with a regression equation for storm surge, were used to predict the storm-maximum storm surge for 41 storms in the New York metropolitan region during the period from February 2005 to December 2008. The statistical storm-surge estimates were compared with the surge values predicted by NOAA’s extratropical storm-surge model and NOAA’s operational surge forecast, which includes an error correction, and with water gauge observations taken at the Battery, located at the southern tip of Manhattan Island, New York. The mean difference between the statistical surge prediction and the observed values is shown to be smaller than the difference between NOAA’s deterministic surge prediction and the observed surge at the 95% significance level and to be statistically indistinguishable from the difference between NOAA’s operational surge forecast and the observed values of surge. These statistical estimates can be used as part of a system for predicting coastal flooding.
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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 uncertainty bounds and from this provide a probabilistic geomagnetic storm forecast. Specifically, we have used a two-layered architecture to separately predict storm (K p ≥ 5−) and non-storm cases. As solar wind-driven models are limited in their ability to predict the onset of transient-driven activity we also introduce a model variant using solar X-ray flux to assess whether simple models including proxies for solar activity can improve the predictions of geomagnetic storm activity with lead times longer than the L1-to-Earth propagation time. By comparing the performance of these models we show that including operationally-available information about solar irradiance enhances the ability of predictive models to capture the onset of geomagnetic storms and that this can be achieved while also enabling probabilistic forecasts.
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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, which is occurred in the territory of the Zlín Region in 2015-2017. The results chapter solves the comparison of the success rate of the manually and computed-processed outputs calculated in the Algorithm of Storm Prediction. Primarily, these outputs will be used for increasing efectivity of preventive measures against flash floods not only by the Fire Rescue Service, but also by flood authorities and crisis management bodies.
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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 (January 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 collected at five representative storm-runoff sites during 22 storms in Little Rock, Arkansas, were used to verify, and, when appropriate, adjust the regional regression equation predictions. Comparison of observed values of storm-runoff loads and mean concentrations to the predicted values from the regional regression equations for nine constituents (chemical oxygen demand, suspended solids, total nitrogen as N, total ammonia plus organic nitrogen as N, total phosphorus as P, dissolved phosphorus as P, total recoverable copper, total recoverable lead, and total recoverable zinc) showed large prediction errors ranging from 63 percent to more than several thousand percent. Prediction errors for 6 of the 18 regional regression equations were less than 100 percent and could be considered reasonable for water-quality prediction equations. The regression adjustment procedure was used to adjust five of the regional equation predictions to improve the predictive accuracy. For seven of the regional equations the observed and the predicted values are not significantly correlated. Thus neither the unadjusted regional equations nor any of the adjustments were appropriate. The mean of the observed values was used as a simple estimator when the regional equation predictions and adjusted predictions were not appropriate.
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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 (March 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 total change in wind turbine output in the next 10 days after the storm has passed. The actual case proves that the Informer model is ideal in predicting the long time series of wind power output during a tropical storm.
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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 (August 6, 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 predict 0–2 h of storms using a machine learning approach for Poland. Moreover, we present an analysis of the importance of features that take part in the prediction process. The accuracy of the model reached over 87%, and the precision of prediction was about 30%. The results show that wet refractivity below 6 km and IWV on the west of the storm are among the significant parameters with potential for predicting storm location. The analysis of IWV demonstrated a correlation between IWV changes and storm occurrence.
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Більше джерел

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 without substantial loss of accuracy, with respect to high-fidelity physics-based models. First, a tsunami run-up response function (TRRF) model is developed that can rapidly predict a tsunami run-up distribution from earthquake fault parameters. This new surrogate modeling approach reduces the number of simulations required to build a surrogate model by separately modeling the leading order contribution and the residual part of the tsunami run-up distribution. Secondly, a TRRF-based inversion (TRRF-INV) model is developed that can infer a tsunami source and its impact from tsunami run-up records. Since this new tsunami inversion model is based on the TRRF model, it can perform a large number of tsunami forward simulations in tsunami inversion modeling, which is impossible with physics-based models. And lastly, a one-dimensional convolutional neural network combined with principal component analysis and k-means clustering (C1PKNet) model is developed that can rapidly predict the peak storm surge from tropical cyclone track time series. Because the C1PKNet model uses the tropical cyclone track time series, it has the advantage of being able to predict more diverse tropical cyclone scenarios than the existing surrogate models that rely on a tropical cyclone condition at one moment (usually at or near landfall). The surrogate models developed in this dissertation have the potential to save lives, mitigate coastal hazard damage, and promote resilient coastal communities.
Doctor of Philosophy
Tsunami and storm surge can cause extensive damage to coastal communities; to reduce this damage, accurate and fast computer models are needed that can predict the water level change caused by these coastal hazards. The problem is that existing physics-based computer models are either accurate but slow or less accurate but fast. In this dissertation, three new computer models are developed using statistical and machine learning techniques that can rapidly predict a tsunami and storm surge without substantial loss of accuracy compared to the accurate physics-based computer models. Three computer models are as follows: (1) A computer model that can rapidly predict the maximum ground elevation wetted by the tsunami along the coastline from earthquake information, (2) A computer model that can reversely predict a tsunami source and its impact from the observations of the maximum ground elevation wetted by the tsunami, (3) A computer model that can rapidly predict peak storm surges across a wide range of coastal areas from the tropical cyclone's track position over time. These new computer models have the potential to improve forecasting capabilities, advance understanding of historical tsunami and storm surge events, and lead to better preparedness plans for possible future tsunamis and storm surges.
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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 precipitation with the worst possible prevailing catchment conditions. Return periods are not usually quoted although they are implicitly thought to be of the order of tens of thousand of years. There are many less critical situations which still justify greater flood protection than would be provided for an estimated one-hundred year flood. There is therefore a need for techniques which can be used to estimate floods with return periods of up to several thousand years. The predictive approach adopted here involves a combination of a probabilistic storm transposition technique with a physically-based distributed rainfall-runoff model. Extreme historical storms within a meteorologically homogeneous region are, conceptually, moved to the catchment of interest, and their return periods are estimated within a probabilistic framework. Known features of storms such as depth, duration, and perhaps approximate shape will, together with catchment characteristics, determine much of the runoff response. But there are other variables which also have an effect and these include the space-time distribution of rainfall within the storm, storm velocity and antecedent catchment conditions. The effects of all these variables on catchment response are explored.
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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 lengths varying from 60 m to 500 m offshore and a concrete manhole excavated into the dune system to house the monitoring station. Accordingly, this system has enabled the monitoring of a detailed wave height profile across the surfzone under any conditions from the safety of the “bunker” on land. The findings of new laboratory experiments on the frequency response of the semi-rigid manometer tubes are also presented which extend and improve upon the previous work of Nielsen et al. (1993). Testing was conducted over a range of frequencies (0.0067 Hz< f <2 Hz) and tube lengths (10 m< L <900 m). New frequency response factors are determined by fitting the semiempirical gain function of Nielsen et al. (1993) to the observed gain data. As a result, new predictive formulas for the empirical coefficients as a function of tube parameters are provided in this study. Wave induced pore pressure in the surfzone seabed is investigated based on the recorded field data. Two well-known models, i.e. Hsu and Jeng (1994) and Sleath (1970), are assessed against the field measurements. The findings validate the accuracy of the models and indicate that the extent of energy dissipation due to the overlying sand is less than 5% and depends on the incident wave length.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith School of Engineering
Science, Environment, Engineering and Technology
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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 bridges damaged in an extreme flood event. Understanding the underlying relationships between existing bridge condition, fluvial stresses, and geomorphological changes is key to identifying vulnerabilities in both existing and future bridge infrastructure. In creating this comprehensive database of bridge inspection records and associated damage characterization, features were identified that correlate to and discriminate between levels of bridge damage. Stream geomorphic assessment features were spatially joined to every bridge, marking the first time that geomorphic assessments have been broadly used for estimating bridge vulnerability. Stream power assessments and watershed delineations for every bridge and stream reach were generated to supplement the comprehensive database. Individual features were tested for their significance to discriminate bridge damage, and then used to create empirical fragility curves and probabilistic predictions maps to aid in future bridge vulnerability detection. Damage to over 300 Vermont bridges from a single extreme flood event, the August 28, 2011 Tropical Storm Irene, was used as the basis for this study. Damage to historic bridges was also summarized and tabulated. In some areas of Vermont, the storm rainfall recurrence interval exceeded 500 years, causing widespread flooding and damaging over 300 bridges. With a dataset of over 330 features for more than 2,000 observations to bridges that were damaged as well as not damaged in the storm, an advanced evolutionary algorithm performed multivariate feature selection to overcome the shortfalls of traditional logistic regression analysis. The analysis identified distinct combinations of variables that correlate to the observed bridge damage under extreme food events.
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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 large system reliability evaluation by managing computer memory efficiently with iterators, assuming a single failure at a time. A reconfiguration for restoration algorithm is also created to enhance the accuracy of the reliability evaluation, considering multiple concurrent failures. As opposed to most restoration simulation methods used in reliability analysis, which convert restoration problems into mathematical models and only can solve radial systems, this new algorithm seeks the reconfiguration solution from topology characteristics of the network itself. As a result the new reconfiguration algorithm can handle systems with loops. In analyzing system reliability, this research takes into account time-varying load patterns, and seeks approaches that are financially justified. An exhaustive search scheme is used to calculate optimal locations for Distributed Generators (DG) from the reliability point of view. A Discrete Ascent Optimal Programming (DAOP) load shifting approach is proposed to provide low cost, reliability improvement solutions. As weather conditions have an important effect on distribution component failure rates, the influence of different types of storms has been incorporated into this study. Storm outage models are created based on ten yearsâ worth of weather and power outage data. An observer is designed to predict the number of outages for an approaching or on going storm. A circuit corridor model is applied to investigate the relationship between power outages and lightning activity.
Ph. D.
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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 Boosting (XGBoost) est proposée pour prévoir les caractéristiques et l’occurrence des tempêtes le long de la côte ouest de la France. Parallèlement, un système de modélisation du développement urbain a été appliqué pour prédire les scénarios d’expansion future en Vendée, en analysant la susceptibilité aux inondations. Un réseau de neurones artificiel combiné à une chaîne de Markov a permis de simuler trois scénarios de croissance urbaine: statu quo, protection de l’environnement et planification urbaine stratégique. Les zones inondables à haut risque et les estimations de l’élévation future du niveau de la mer ont ensuite été utilisées pour évaluer les risques d’inondation futurs dans le cadre de chaque scénario de croissance. Les résultats montrent l’efficacité des modèles LSTM et XGBoost pour la prévision des caractéristiques et de l’occurrence des tempêtes. L’approche de modélisation de la croissance urbaine révèle les zones vulnérables aux inondations dans chaque scénario. Cette thèse fournit des outils pour renforcer la résilience et la durabilité dans les zones côtières
Coastal regions are increasingly exposed to extreme events due to the combined impacts of climate change and urbanization. This thesis examines coastal hazards along France’s western coast, emphasizing storm prediction and the simulation of future vulnerability to coastal urban floodind. The research employs machine learning (ML) and deep learning (DL) approaches to improve hazard prediction and assess potential future risks. It introduces a novel methodology that combines Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) to forecast storm features and occurrences along the western coast of France. Additionally, an urban development modeling system was applied to predict future expansion scenarios in the Vendée region, analyzing potential flood susceptibility under each scenario. An Artificial Neural Network combined with a Markov Chain was utilized to simulate three future urban growth scenarios; business-as-usual, environmental protection, and strategic urban planning. High-risk flood zones and future sea level rise estimates were then used to assess future flood risk under each growth scenario. The research findings demonstrate the efficiency of LSTM and XGBoost in predicting storm characteristics and occurrences. Moreover, the urban growth modeling approach forecasts future development sites and specific urban areas vulnerable to flooding, allowing for the evaluation of the impact of various development trajectories on future flood risk. This thesis contributes to coastal hazard prediction, urban planning, and risk management, providing useful tools for improving resilience and sustainability in coastal zones
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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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Анотація:
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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 commercial grocery stores and commissary stores in the local area. The number of competitors did not matter in sales predictions.
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Книги з теми "Storm prediction"

1

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

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Talukder, Jyotirmoy. Living with cyclone: Study on storm surge prediction and disaster preparedness. Dhaka, Bangladesh: 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. Silver Spring, Md.]: 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. New York: John Wiley, 2002.

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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. [New York, N.Y.?]: [publisher not identified], 2019.

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Guo, Lei. Semi-empirical prediction of pesticide loading in the Sacramento and San Joaquin Rivers during winter storm seasons. Sacramento: 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. Dhaka: 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. Reston, Va: 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. Irvington, NY: 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. [Washington, D.C.?]: 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, 157–73. Washington, D. C.: 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, 155–65. Dordrecht: 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, 265–78. Dordrecht: 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, 227–52. Chichester, UK: 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, 396–405. Cham: 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, Purushottam Sigdel, Li Chen, Lu Peng, and Nian-Feng Tzeng. "Comprehensive Transformer-Based Model Architecture for Real-World Storm Prediction." In Lecture Notes in Computer Science, 54–71. Cham: 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, 319–29. Singapore: 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, 374–84. Dordrecht: 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, 355–63. Dordrecht: 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, 241–52. Dordrecht: 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"

1

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), 1–7. 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), 1–7. IEEE, 2024. https://doi.org/10.1109/etncc63262.2024.10767516.

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3

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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4

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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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, the data can provide: 1. Evidence for planning: Statistics of past conditions such as the probability of extreme event occurrence can be used to help plan improvements to coastal infrastructure that are able to withstand and mitigate the hazard from a given extreme event. 2. Early warning systems: Short term forecasts of storm surge allow provide early warnings to coastal communities enabling them to take actions to allow them to withstand extreme events, e.g. deploy flood prevention measures or mobilise emergency response measures. Data regarding sea level height can be provided from various in-situ observations such as tide gauges and remote observations such as satellite altimetry. However, to provide a forecast at high spatial and temporal resolution a dynamic ocean model is used. Over recent decades the National Oceanography Centre has been a world leading in developing coastal ocean models. This paper will present our progress on a current project to develop an information system for the Madagascan Met Office. The project, C-RISC, being executed in partnership with Sea Level Research Ltd, is translating the current modelling capability of NOC in storm surge forecasting and tidal prediction into a system that will provide information that can be easily transferred to other regions and is scalable to include other hazard types The outcome, an operational high-resolution storm surge warning system that is easy to relocate, will directly benefit coastal communities, giving them information they need to make effective decisions before and during extreme storm surge events.
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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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6

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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8

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

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9

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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10

Zhang, Yuhang, Weimin Zhen, Liang Chen, Ming Ou, Xiao Yu, Yan Wang, and Longjiang Chen. "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.), August 2021. http://dx.doi.org/10.21079/11681/41482.

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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 such as the North Atlantic Coast Comprehensive Study (NACCS) (Nadal-Caraballo et al. 2015; Cialone et al. 2015) and the ongoing South Atlantic Coast Study (SACS). The PCHA framework considers hazards due to both tropical and extratropical cyclones, depending on the storm climatology of the region of interest. The CHS supports feasibility studies, probabilistic design of coastal structures, and flood risk management for coastal communities and critical infrastructure. StormSim (https://stormsim.erdc.dren.mil) is a suite of tools used for statistical analysis and probabilistic modeling of historical and synthetic storms and for stochastic design and other engineering applications. One of these tools, the Coastal Hazards Rapid Prediction System (CHRPS) (Torres et al. 2020), can perform rapid prediction of coastal storm hazards, including real-time hurricane-induced flooding. This CHETN discusses the quantification and validation of the Advanced Circulation (ADCIRC) tidal constituent database (Szpilka et al. 2016) and the tidal reconstruction program Unified Tidal analysis (UTide) (Codiga 2011) in the Puerto Rico and U.S. Virgin Islands (PR/USVI) coastal regions. The new methodology discussed herein will be further developed into the Rapid Tidal Reconstruction (RTR) tool within the StormSim and CHS frameworks.
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Torres, Marissa, and Norberto Nadal-Caraballo. Rapid tidal reconstruction with UTide and the ADCIRC tidal database. Engineer Research and Development Center (U.S.), August 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 framework. There is a need for a rapid, reliable, and accurate tide prediction methodology to provide spatially dense reconstructed or predicted tidal time series for historical, synthetic, and forecasted hurricane scenarios. A methodology is proposed to combine the tidal harmonic information from the spatially dense Advanced Circulation hydrodynamic model tidal database with a rapid tidal reconstruction and prediction program. In this study, the Unified Tidal Analysis program was paired with results from the tidal database. This methodology will produce reconstructed (i.e., historical) and predicted tidal heights for coastal locations along the United States eastern seaboard and beyond and will contribute to the determination of accurate still water levels in coastal hazard analysis methods.
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Cialone, Mary, Jessamin Straub, Britt Raubenheimer, Jenna Brown, Katherine Brodie, Nicole Elko, Patrick Dickhudt, 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.), March 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 communities. In the short term, these high-quality field measurements will lead to better understanding of during-storm processes, impacts and post-storm recovery and will enhance US academic coastal research programs. Longer-term, DUNEX data and outcomes will improve understanding and prediction of extreme event physical processes and impacts, validate coastal processes numerical models, and improve coastal resilience strategies and communication methods for coastal communities impacted by storms. This report focuses on the planning and preparation required to conduct a large-scale field experiment, the collaboration amongst researchers, and lessons learned. The value of a large-scale experiment focused on storm processes and impacts begins with the scientific gains from the data collected, which will be available and used for decades to come.
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O'Neill, Clare, Andy Saulter, Christopher Stokes, and Breogán Gómez. Application of bias correction to the Met Office operational storm surge forecast. Met Office, January 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 atmospheric component included in harmonic tide predictions, and an empirical constant correction to account for sea level rise and other effects not currently included in the surge model. The correction scheme is shown to significantly improve the overall forecast skill compared to the original uncorrected forecast. The number of forecasts that fall outside the target 20 cm accuracy is reduced from an average of 17% (whole tide cycle) and 22% (high waters only) to less than 6% (in both cases). The average RMS error is reduced from 14 cm (whole tide cycle) and 15 cm (high waters) to 9 cm (in both cases) and the average bias is reduced from 11 cm to 1cm (all tide cycle) and 13 cm to 3 cm (high waters). The correction was added to the operational system in August 2024.
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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), October 2017. http://dx.doi.org/10.2172/1405606.

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6

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

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7

Wissink, Andrew, Jude Dylan, Buvana Jayaraman, Beatrice Roget, Vinod Lakshminarayan, Jayanarayanan Sitaraman, Andrew Bauer, James Forsythe, Robert Trigg, and Nicholas Peters. New capabilities in CREATE™-AV Helios Version 11. Engineer Research and Development Center (U.S.), June 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 per step. ROAM calculations of the JVX tiltrotor configuration give a comparably accurate download prediction to traditional body-fitted calculations with Helios, at 50X less computational cost. The unsteady wake in ROAM is not as well resolved, but wake interactions may be a less critical issue for many design considerations. The second capability discussed is the addition of six-degree-of-freedom capability to model store separation. Helios calculations of a generic wing/store/pylon case with the new 6-DOF capability are found to match identically to calculations with CREATE™-AV Kestrel, a code which has been extensively validated for store separation calculations over the past decade.
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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 nor?easters and associated storm surges in New England and SLR projections across the study domain due to climate change. The observational data analysis evaluates the characteristics of nor?easters and their effects, providing a basis for validating the model. Numerical modeling is performed using the Advanced Circulation (ADCIRC) model, coupled with the Simulating Waves in the Nearshore (SWAN) model to simulate storm surges and waves. The model was validated against available observations and demonstrated its ability to simulate water levels, inland inundation, and wave heights in the study area with high accuracy. The validated model was used to simulate three powerful nor?easters (April 2007, January 2018, and March 2018) and each storm was simulated for three sea levels, (1) a baseline mean sea level representative of the year 2020, as well as with a (2) 1 ft of SLR and (3) 1 m of SLR. Analysis of the model output was used to assess the vulnerability of the parks to nor?easters by examining peak impacts in the park areas. Additional simulations were conducted to evaluate the role of waves in predicting peak water levels and the impact of inlet configurations on storm surges within coastal embayments behind the barrier beach systems in the southern Cape Cod region. The project developed maps, three-dimensional visualizations, and an interpretive film to assist the parks in planning for resource management, maintenance, emergency management, visitor access, safety, education, and outreach. These tools provide a better understanding of the potential impacts of nor?easters and SLR and enable the parks to better prepare for future storms.
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Mendillo, Michael, and Jules Aarons. A Plan to Develop Predictive Capability for Equatorial Scintillation Storms. Fort Belvoir, VA: Defense Technical Information Center, January 1997. http://dx.doi.org/10.21236/ada323511.

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

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