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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Ian, Vai-Kei, Rita Tse, Su-Kit Tang, and Giovanni Pau. "Bridging the Gap: Enhancing Storm Surge Prediction and Decision Support with Bidirectional Attention-Based LSTM." Atmosphere 14, no. 7 (June 27, 2023): 1082. http://dx.doi.org/10.3390/atmos14071082.
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Accurate storm surge forecasting is vital for saving lives and avoiding economic and infrastructural damage. Failure to accurately predict storm surge can have catastrophic repercussions. Advances in machine learning models show the ability to improve accuracy of storm surge prediction by leveraging vast amounts of historical and realtime data such as weather and tide patterns. This paper proposes a bidirectional attention-based LSTM storm surge architecture (BALSSA) to improve prediction accuracy. Training and evaluation utilized extensive meteorological and tide level data from 77 typhoon incidents in Hong Kong and Macao between 2017 and 2022. The proposed methodology is able to model complex non-linearities between large amounts of data from different sources and identify complex relationships between variables that are typically not captured by traditional physical methods. BALSSA effectively resolves the problem of long-term dependencies in storm surge prediction by the incorporation of an attention mechanism. It enables selective emphasis on significant features and boosts the prediction accuracy. Evaluation has been conducted using real-world datasets from Macao to validate our storm surge prediction model. Results show that accuracy and robustness of predictions were significantly improved by the incorporation of attention mechanisms in our models. BALSSA captures temporal dynamics effectively, providing highly accurate storm surge forecasts (MAE: 0.0126, RMSE: 0.0003) up to 72 h in advance. These findings have practical significance for disaster risk reduction strategies, saving lives through timely evacuation and early warnings. Experiments comparing BALSSA variations with other machine learning algorithms consistently validate BALSSA’s superior predictive performance. It offers an additional risk management tool for civil-protection agencies and governments, as well as an ideal solution for enhancing storm surge prediction accuracy, benefiting coastal communities.
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Taflanidis, Alexandros, Jize Zhang, Aikaterini Kyprioti, Andrew Kennedy, and Tracy Kijewksi-Correa. "DEVELOPMENTS IN STORM SURGE ESTIMATION USING SURROGATE MODELING TECHNIQUES." Coastal Engineering Proceedings, no. 36v (December 28, 2020): 37. http://dx.doi.org/10.9753/icce.v36v.currents.37.
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Numerical advances in storm surge prediction over the past couple of decades have produced high-fidelity simulation models that permit a detailed representation of hydrodynamic processes and therefore support high accuracy forecasting. Unfortunately, the computational burden of such numerical models is large, requiring thousands of CPU hours for each simulation, something that limits their applicability for hurricane risk assessment. Use of Kriging-based surrogate modeling techniques has been examined to address the aforementioned challenge Jia et al. [2016], Zhang et al. [2018]. This approach can provide fast predictions using a database of high-fidelity, synthetic storms, with the goal of maintaining the accuracy of the numerical model utilized to produce this database, while offering computational efficiency. This contribution overviews initially recent research developments for the application of Kriging for storm surge predictions. Topics discussed include: enhancement of the initial database for nodes (i.e., geographical locations) that have remained dry in some of the database storms; adaptive selection of storms forming the initial database; use of different surrogate modeling tuning techniques and their impact on the metamodel predictive capabilities for storm surge estimation; implementation for estimation of impact due to near-shore processes (breaking waves), something that requires coupling of different numerical models.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/vL38Kv3kLDM
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Tsai, Yu-Lin, Tso-Ren Wu, Chuan-Yao Lin, Simon C. Lin, Eric Yen, and Chun-Wei Lin. "Discrepancies on Storm Surge Predictions by Parametric Wind Model and Numerical Weather Prediction Model in a Semi-Enclosed Bay: Case Study of Typhoon Haiyan." Water 12, no. 12 (November 26, 2020): 3326. http://dx.doi.org/10.3390/w12123326.
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This study explores the discrepancies of storm surge predictions driven by the parametric wind model and the numerical weather prediction model. Serving as a leading-order storm wind predictive tool, the parametric Holland wind model provides the frictional-free, steady-state, and geostrophic-balancing solutions. On the other hand, WRF-ARW (Weather Research and Forecasting-Advanced Research WRF) provides the results solving the 3D time-integrated, compressible, and non-hydrostatic Euler equations, but time-consuming. To shed light on their discrepancies for storm surge predictions, the storm surges of 2013 Typhoon Haiyan in the Leyte Gulf and the San Pedro Bay are selected. The Holland wind model predicts strong southeastern winds in the San Pedro Bay after Haiyan makes landfall at the Leyte Island than WRF-ARW 3 km and WRF-ARW 1 km. The storm surge simulation driven by the Holland wind model finds that the water piles up in the San Pedro Bay and its maximum computed storm surges are almost twice than those driven by WRF-ARW. This study also finds that the storm surge prediction in the San Pedro Bay is sensitive to winds, which can be affected by the landfall location, the storm intensity, and the storm forward speed. The numerical experiment points out that the maximum storm surges can be amplified by more 5–6% inside the San Pedro Bay if Haiyan’s forward speed is increased by 10%.
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Liu, Yun, Qiansheng Zhao, Chunchun Hu, and Nianxue Luo. "Prediction of Storm Surge Water Level Based on Machine Learning Methods." Atmosphere 14, no. 10 (October 16, 2023): 1568. http://dx.doi.org/10.3390/atmos14101568.
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Storm surge disasters result in severe casualties and economic losses. Accurate prediction of storm surge water level is crucial for disaster assessment, early warning, and effective disaster management. Machine learning methods are relatively more efficient and straightforward compared to numerical simulation approaches. However, most of the current research on storm surge water level prediction based on machine learning methods is primarily focused on point predictions. In this study, we explore the feasibility of spatial water level prediction using the ConvLSTM model. We focus on the coastal area of Guangdong Province and employ MIKE21(2019) software to simulate historical typhoons that have made landfall in the region from 1991 to 2018. We construct two datasets: one for direct water level prediction and the other for indirect water level prediction based on water level changes. Utilizing the ConvLSTM network, we employ it to forecast storm surges on both datasets, effectively capturing both temporal and spatial characteristics and thus ensuring the production of dependable results. When directly predicting water levels, we achieve an MAE (mean absolute error) of 0.026 m and an MSE (mean squared error) of 0.0038 m2. In contrast, the indirect prediction approach yields even more promising results, with an MAE of 0.014 m and an MSE of 0.0007 m2. Compared to traditional numerical simulation methods, the ConvLSTM-based approach is simpler, faster, and able to predict water levels accurately without boundary conditions or topographies. Furthermore, we consider worst-case scenarios by predicting the maximum water increase value using the random forest model. Our results indicate that the random forest model can serve as a valuable reference for forecasting the maximum water increase value of typhoon storm surges, supporting effective emergency responses to disasters.
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Molina, Rosa, Giorgio Manno, Carlo Lo Re, Giorgio Anfuso, and Giuseppe Ciraolo. "Storm Energy Flux Characterization along the Mediterranean Coast of Andalusia (Spain)." Water 11, no. 3 (March 11, 2019): 509. http://dx.doi.org/10.3390/w11030509.
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This paper investigates wave climate and storm characteristics along the Mediterranean coast of Andalusia, for the period 1979–2014, by means of the analysis of wave data on four prediction points obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF). Normally, to characterize storms, researchers use the so-called “power index”. In this paper, a different approach was adopted based on the assessment of the wave energy flux of each storm, using a robust definition of sea storm. During the investigated period, a total of 2961 storm events were recorded. They were classified by means of their associated energy flux into five classes, from low- (Class I) to high-energetic (Class V). Each point showed a different behavior in terms of energy, number, and duration of storms. Nine stormy years, i.e., years with a high cumulative energy, were recorded in 1980, 1983, 1990, 1992, 1995, 2001, 2008, 2010, and 2013.
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Costa, Wagner, Déborah Idier, Jérémy Rohmer, Melisa Menendez, and Paula Camus. "Statistical Prediction of Extreme Storm Surges Based on a Fully Supervised Weather-Type Downscaling Model." Journal of Marine Science and Engineering 8, no. 12 (December 16, 2020): 1028. http://dx.doi.org/10.3390/jmse8121028.
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Increasing our capacity to predict extreme storm surges is one of the key issues in terms of coastal flood risk prevention and adaptation. Dynamically forecasting storm surges is computationally expensive. Here, we focus on an alternative data-driven approach and set up a weather-type statistical downscaling for daily maximum storm surge (SS) prediction, using atmospheric hindcasts (CFSR and CFSv2) and 15 years of tidal gauge station measurements. We focus on predicting the storm surge at La Rochelle–La Pallice tidal gauge station. First, based on a sensitivity analysis to the various parameters of the weather-type approach, we find that the model configuration providing the best performance in SS prediction relies on a fully supervised classification using minimum daily sea level pressure (SLP) and maximum SLP gradient, with 1° resolution in the northeast Atlantic domain as the predictor. Second, we compare the resulting optimal model with the inverse barometer approach and other statistical models (multi-linear regression; semi-supervised and unsupervised weather-types based approaches). The optimal configuration provides more accurate predictions for extreme storm surges, but also the capacity to identify unusual atmospheric storm patterns that can lead to extreme storm surges, as the Xynthia storm for instance (a decrease in the maximum absolute error of 50%).
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Morss, Rebecca E., David Ahijevych, Kathryn R. Fossell, Alex M. Kowaleski, and Christopher A. Davis. "Predictability of Hurricane Storm Surge: An Ensemble Forecasting Approach Using Global Atmospheric Model Data." Water 16, no. 11 (May 25, 2024): 1523. http://dx.doi.org/10.3390/w16111523.
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Providing storm surge risk information at multi-day lead times is critical for hurricane evacuation decisions, but predictability of storm surge inundation at these lead times is limited. This study develops a method to parameterize and adjust tropical cyclones derived from global atmospheric model data, for use in storm surge research and prediction. We implement the method to generate storm tide (surge + tide) ensemble forecasts for Hurricane Michael (2018) at five initialization times, using archived operational ECMWF ensemble forecasts and the dynamical storm surge model ADCIRC. The results elucidate the potential for extending hurricane storm surge prediction to several-day lead times, along with the challenges of predicting the details of storm surge inundation even 18 h before landfall. They also indicate that accurately predicting Hurricane Michael’s rapid intensification was not needed to predict the storm surge risk. In addition, the analysis illustrates how this approach can help identify situationally and physically realistic scenarios that pose greater storm surge risk. From a practical perspective, the study suggests potential approaches for improving real-time probabilistic storm surge prediction. The method can also be useful for other applications of atmospheric model data in storm surge research, forecasting, and risk analysis, across weather and climate time scales.
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Ian, Vai-Kei, Su-Kit Tang, and Giovanni Pau. "Assessing the Risk of Extreme Storm Surges from Tropical Cyclones under Climate Change Using Bidirectional Attention-Based LSTM for Improved Prediction." Atmosphere 14, no. 12 (November 28, 2023): 1749. http://dx.doi.org/10.3390/atmos14121749.
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Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical and real-time data, BALSSA significantly enhances prediction accuracy. Utilizing a bidirectional attention-based LSTM framework, it captures complex, non-linear relationships and long-term dependencies, improving the accuracy of storm surge predictions. The enhanced model, D-BALSSA, further amplifies predictive capability through a doubled bidirectional attention-based structure. Training and evaluation involve a comprehensive dataset from over 70 typhoon incidents in Macao between 2017 and 2022. The results showcase the outstanding performance of BALSSA, delivering highly accurate storm surge forecasts with a lead time of up to 72 h. Notably, the model exhibits a low Mean Absolute Error (MAE) of 0.0287 m and Root Mean Squared Error (RMSE) of 0.0357 m, crucial indicators measuring the accuracy of storm surge predictions in water level anomalies. These metrics comprehensively evaluate the model’s accuracy within the specified timeframe, enabling timely evacuation and early warnings for effective disaster mitigation. An adaptive system, integrating real-time alerts, tropical cyclone (TC) chaser, and prospective visualizations of meteorological and tidal measurements, enhances BALSSA’s capabilities for improved storm surge prediction. Positioned as a comprehensive tool for risk management, BALSSA supports decision makers, civil protection agencies, and governments involved in disaster preparedness and response. By leveraging advanced machine learning techniques and extensive data, BALSSA enables precise and timely predictions, empowering coastal communities to proactively prepare and respond to extreme weather events. This enhanced accuracy strengthens the resilience of coastal communities and protects lives and infrastructure from the escalating threats of climate change.
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Froude, Lizzie S. R. "Regional Differences in the Prediction of Extratropical Cyclones by the ECMWF Ensemble Prediction System." Monthly Weather Review 137, no. 3 (March 1, 2009): 893–911. http://dx.doi.org/10.1175/2008mwr2610.1.
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Abstract A regional study of the prediction of extratropical cyclones by the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) has been performed. An objective feature-tracking method has been used to identify and track the cyclones along the forecast trajectories. Forecast error statistics have then been produced for the position, intensity, and propagation speed of the storms. In previous work, data limitations meant it was only possible to present the diagnostics for the entire Northern Hemisphere (NH) or Southern Hemisphere. A larger data sample has allowed the diagnostics to be computed separately for smaller regions around the globe and has made it possible to explore the regional differences in the prediction of storms by the EPS. Results show that in the NH there is a larger ensemble mean error in the position of storms over the Atlantic Ocean. Further analysis revealed that this is mainly due to errors in the prediction of storm propagation speed rather than in direction. Forecast storms propagate too slowly in all regions, but the bias is about 2 times as large in the NH Atlantic region. The results show that storm intensity is generally overpredicted over the ocean and underpredicted over the land and that the absolute error in intensity is larger over the ocean than over the land. In the NH, large errors occur in the prediction of the intensity of storms that originate as tropical cyclones but then move into the extratropics. The ensemble is underdispersive for the intensity of cyclones (i.e., the spread is smaller than the mean error) in all regions. The spatial patterns of the ensemble mean error and ensemble spread are very different for the intensity of cyclones. Spatial distributions of the ensemble mean error suggest that large errors occur during the growth phase of storm development, but this is not indicated by the spatial distributions of the ensemble spread. In the NH there are further differences. First, the large errors in the prediction of the intensity of cyclones that originate in the tropics are not indicated by the spread. Second, the ensemble mean error is larger over the Pacific Ocean than over the Atlantic, whereas the opposite is true for the spread. The use of a storm-tracking approach, to both weather forecasters and developers of forecast systems, is also discussed.
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Wang, Ding, Boyang Liu, Pang-Ning Tan, and Lifeng Luo. "OMuLeT: Online Multi-Lead Time Location Prediction for Hurricane Trajectory Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 963–70. http://dx.doi.org/10.1609/aaai.v34i01.5444.
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Hurricanes are powerful tropical cyclones with sustained wind speeds ranging from at least 74 mph (for category 1 storms) to more than 157 mph (for category 5 storms). Accurate prediction of the storm tracks is essential for hurricane preparedness and mitigation of storm impacts. In this paper, we cast the hurricane trajectory forecasting task as an online multi-lead time location prediction problem and present a framework called OMuLeT to improve path prediction by combining the 6-hourly and 12-hourly forecasts generated from an ensemble of dynamical (physical) hurricane models. OMuLeT employs an online learning with restart strategy to incrementally update the weights of the ensemble model combination as new observation data become available. It can also handle the varying dynamical models available for predicting the trajectories of different hurricanes. Experimental results using the Atlantic and Eastern Pacific hurricane data showed that OMuLeT significantly outperforms various baseline methods, including the official forecasts produced by the U.S. National Hurricane Center (NHC), by more than 10% in terms of its 48-hour lead time forecasts.
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McGovern, Amy, Christopher D. Karstens, Travis Smith, and Ryan Lagerquist. "Quasi-Operational Testing of Real-Time Storm-Longevity Prediction via Machine Learning." Weather and Forecasting 34, no. 5 (September 20, 2019): 1437–51. http://dx.doi.org/10.1175/waf-d-18-0141.1.
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Abstract Real-time prediction of storm longevity is a critical challenge for National Weather Service (NWS) forecasters. These predictions can guide forecasters when they issue warnings and implicitly inform them about the potential severity of a storm. This paper presents a machine-learning (ML) system that was used for real-time prediction of storm longevity in the Probabilistic Hazard Information (PHI) tool, making it a Research-to-Operations (R2O) project. Currently, PHI provides forecasters with real-time storm variables and severity predictions from the ProbSevere system, but these predictions do not include storm longevity. We specifically designed our system to be tested in PHI during the 2016 and 2017 Hazardous Weather Testbed (HWT) experiments, which are a quasi-operational naturalistic environment. We considered three ML methods that have proven in prior work to be strong predictors for many weather prediction tasks: elastic nets, random forests, and gradient-boosted regression trees. We present experiments comparing the three ML methods with different types of input data, discuss trade-offs between forecast quality and requirements for real-time deployment, and present both subjective (human-based) and objective evaluation of real-time deployment in the HWT. Results demonstrate that the ML system has lower error than human forecasters, which suggests that it could be used to guide future storm-based warnings, enabling forecasters to focus on other aspects of the warning system.
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Yang, Qidong, Chia-Ying Lee, and Michael K. Tippett. "A Long Short-Term Memory Model for Global Rapid Intensification Prediction." Weather and Forecasting 35, no. 4 (August 1, 2020): 1203–20. http://dx.doi.org/10.1175/waf-d-19-0199.1.
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ABSTRACTRapid intensification (RI) is an outstanding source of error in tropical cyclone (TC) intensity predictions. RI is generally defined as a 24-h increase in TC maximum sustained surface wind speed greater than some threshold, typically 25, 30, or 35 kt (1 kt ≈ 0.51 m s−1). Here, a long short-term memory (LSTM) model for probabilistic RI predictions is developed and evaluated. The variables (features) of the model include storm characteristics (e.g., storm intensity) and environmental variables (e.g., vertical shear) over the previous 48 h. A basin-aware RI prediction model is trained (1981–2009), validated (2010–13), and tested (2014–17) on global data. Models are trained on overlapping 48-h data, which allows multiple training examples for each storm. A challenge is that the data are highly unbalanced in the sense that there are many more non-RI cases than RI cases. To cope with this data imbalance, the synthetic minority-oversampling technique (SMOTE) is used to balance the training data by generating artificial RI cases. Model ensembling is also applied to improve prediction skill further. The model’s Brier skill scores in the Atlantic and eastern North Pacific are higher than those of operational predictions for RI thresholds of 25 and 30 kt and comparable for 35 kt on the independent test data. Composites of the features associated with RI and non-RI situations provide physical insights for how the model discriminates between RI and non-RI cases. Prediction case studies are presented for some recent storms.
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Watson, Peter L., Marika Koukoula, and Emmanouil Anagnostou. "Influence of the Characteristics of Weather Information in a Thunderstorm-Related Power Outage Prediction System." Forecasting 3, no. 3 (August 5, 2021): 541–60. http://dx.doi.org/10.3390/forecast3030034.
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Thunderstorms are one of the most damaging weather phenomena in the United States, but they are also one of the least predictable. This unpredictable nature can make it especially challenging for emergency responders, infrastructure managers, and power utilities to be able to prepare and react to these types of events when they occur. Predictive analytical methods could be used to help power utilities adapt to these types of storms, but there are uncertainties inherent in the predictability of convective storms that pose a challenge to the accurate prediction of storm-related outages. Describing the strength and localized effects of thunderstorms remains a major technical challenge for meteorologists and weather modelers, and any predictive system for storm impacts will be limited by the quality of the data used to create it. We investigate how the quality of thunderstorm simulations affects power outage models by conducting a comparative analysis, using two different numerical weather prediction systems with different levels of data assimilation. We find that limitations in the weather simulations propagate into the outage model in specific and quantifiable ways, which has implications on how convective storms should be represented to these types of data-driven impact models in the future.
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Jiang, Haiyan, Jeffrey B. Halverson, Joanne Simpson, and Edward J. Zipser. "Hurricane “Rainfall Potential” Derived from Satellite Observations Aids Overland Rainfall Prediction." Journal of Applied Meteorology and Climatology 47, no. 4 (April 1, 2008): 944–59. http://dx.doi.org/10.1175/2007jamc1619.1.
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Abstract The Tropical Rainfall Measuring Mission–based National Aeronautics and Space Administration Goddard Multisatellite Precipitation Analysis (MPA) product is used to quantify the rainfall distribution in tropical cyclones that made landfall in the United States during 1998–2004. A total of 37 tropical cyclones (TC) are examined, including 2680 three-hourly MPA precipitation observations. Rainfall distributions for overland and overocean observations are compared. It is found that the TC rainfall over ocean bears a strong relationship with the TC maximum wind, whereas the relationship for overland conditions is much weaker. The rainfall potential is defined by using the satellite-derived rain rate, the satellite-derived storm size, and the storm translation speed. This study examines the capability of the overocean rainfall potential to predict a storm’s likelihood of producing heavy rain over land. High correlations between rain potentials before landfall and the maximum storm total rain over land are found using the dataset of the 37 landfalling TCs. Correlations are higher with the average rain potential on the day prior to landfall than with averages over any other time period. A TC overland rainfall index is introduced based on the rainfall potential study. This index can be used to predict the storm peak rainfall accumulation over land. Six landfalling storms during the 2005 Atlantic Ocean hurricane season are examined to verify the capability of using this index to forecast the maximum storm total rain over land in the United States. The range of the maximum storm overland rain forecast error for these six storms is between 2.5% and 24.8%.
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Renggli, Dominik, Gregor C. Leckebusch, Uwe Ulbrich, Stephanie N. Gleixner, and Eberhard Faust. "The Skill of Seasonal Ensemble Prediction Systems to Forecast Wintertime Windstorm Frequency over the North Atlantic and Europe." Monthly Weather Review 139, no. 9 (September 2011): 3052–68. http://dx.doi.org/10.1175/2011mwr3518.1.
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The science of seasonal predictions has advanced considerably in the last decade. Today, operational predictions are generated by several institutions, especially for variables such as (sea) surface temperatures and precipitation. In contrast, few studies have been conducted on the seasonal predictability of extreme meteorological events such as European windstorms in winter. In this study, the predictive skill of extratropical wintertime windstorms in the North Atlantic/European region is explored in sets of seasonal hindcast ensembles from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) and the ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) projects. The observed temporal and spatial climatological distributions of these windstorms are reasonably well reproduced in the hindcast data. Using hindcasts starting on 1 November, significant predictive skill is found for the December–February windstorm frequency in the period 1980–2001, but also for the January–April storm frequency. Specifically, the model suite run at Météo France shows consistently high skill. Some aspects of the variability of skill are discussed. Predictive skill in the 1980–2001 period is usually higher than for the 1960–2001 period. Furthermore, the level of skill turns out to be related to the storm frequency of a given winter. Generally, winters with high storm frequency are better predicted than winters with medium storm frequency. Physical mechanisms potentially leading to such a variability of skill are discussed.
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Frifra, A., M. Maanan, H. Rhinane, and M. Maanan. "AN ARTIFICIAL INTELLIGENCE APPROACH TO PREDICTION OF EXTREME EVENTS: THE CASE OF STORMS IN WESTERN FRANCE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W3-2021 (January 10, 2022): 115–22. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w3-2021-115-2022.
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Abstract. Storms represent an increased source of risk that affects human life, property, and the environment. Prediction of these events, however, is challenging due to their low frequency of occurrence. This paper proposed an artificial intelligence approach to address this challenge and predict storm characteristics and occurrence using a gated recurrent unit (GRU) neural network and a support vector machine (SVM). Historical weather and marine measurements collected from buoy data, as well as a database of storms containing all the extreme events that occurred in Brittany and Pays de la Loire regions, Western France, since 1996, were used. Firstly, GRU was used to predict the characteristics of storms (wind speed, pressure, humidity, temperature, and wave height). Then, SVM was introduced to identify storm-specific patterns and predict storm occurrence. The approach adopted leads to the prediction of storms and their characteristics, which could be used widely to reduce the awful consequences of these natural disasters by taking preventive measures.
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Froude, Lizzie. "Storm tracking and eScience." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1890 (December 16, 2008): 905–11. http://dx.doi.org/10.1098/rsta.2008.0183.
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A new ‘storm-tracking approach’ to analysing the prediction of storms by different forecast systems has recently been developed. This paper provides a brief illustration of the type of results/information that can be obtained using the approach. It also describes in detail how eScience methodologies have been used to help apply the storm-tracking approach to very large datasets.
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Chao, Wei-Ting, Chih-Chieh Young, Tai-Wen Hsu, Wen-Cheng Liu, and Chian-Yi Liu. "Long-Lead-Time Prediction of Storm Surge Using Artificial Neural Networks and Effective Typhoon Parameters: Revisit and Deeper Insight." Water 12, no. 9 (August 26, 2020): 2394. http://dx.doi.org/10.3390/w12092394.
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Storm surge induced by severe typhoons has caused many catastrophic tragedies to coastal communities over past decades. Accurate and efficient prediction/assessment of storm surge is still an important task in order to achieve coastal disaster mitigation especially under the influence of climate change. This study revisits storm surge predictions using artificial neural networks (ANN) and effective typhoon parameters. Recent progress of storm surge modeling and some remaining unresolved issues are reviewed. In this paper, we chose the northeastern region of Taiwan as the study area, where the largest storm surge record (over 1.8 m) has been observed. To develop the ANN-based storm surge model for various lead-times (from 1 to 12 h), typhoon parameters are carefully examined and selected by analogy with the physical modeling approach. A knowledge extraction method (KEM) with backward tracking and forward exploration procedures is also proposed to analyze the roles of hidden neurons and typhoon parameters in storm surge prediction, as well as to reveal the abundant, useful information covered in the fully-trained artificial brain. Finally, the capability of ANN model for long-lead-time predictions and influences in controlling parameters are investigated. Overall, excellent agreement with observations (i.e., the coefficient of efficiency CE > 0.95 for training and CE > 0.90 for validation) is achieved in one-hour-ahead prediction. When the typhoon affects coastal waters, contributions of wind speed, central pressure deficit, and relative angle are clarified via influential hidden neurons. A general pattern of maximum storm surge under various scenarios is also obtained. Moreover, satisfactory accuracy is successfully extended to a much longer lead time (i.e., CE > 0.85 for training and CE > 0.75 for validation in 12-h-ahead prediction). Possible reasons for further accuracy improvement compared to earlier works are addressed.
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Yang, Jaemo, Marina Astitha, Emmanouil N. Anagnostou, and Brian M. Hartman. "Using a Bayesian Regression Approach on Dual-Model Windstorm Simulations to Improve Wind Speed Prediction." Journal of Applied Meteorology and Climatology 56, no. 4 (April 2017): 1155–74. http://dx.doi.org/10.1175/jamc-d-16-0206.1.
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AbstractWeather prediction accuracy is very important given the devastating effects of extreme-weather events in recent years. Numerical weather prediction systems are used to build strategies to prevent catastrophic losses of human lives and the environment and have evolved with the use of multimodel or single-model ensembles and data-assimilation techniques in an attempt to improve the forecast skill. These techniques require increased computational power (thousands of CPUs) because of the number of model simulations and ingestion of observational data from a wide variety of sources. In this study, the combination of predictions from two state-of-the-science atmospheric models [WRF and RAMS/Integrated Community Limited Area Modeling System (ICLAMS)] using Bayesian and simple linear regression techniques is examined, and wind speed prediction for the northeastern United States is improved using regression techniques. Retrospective simulations of 17 storms that affected the northeastern United States during the period 2004–13 are performed and utilized. Optimal variances are estimated for the 13 training storms by minimizing the root-mean-square error and are applied to four out-of-sample storms [Hurricane Irene (2011), Hurricane Sandy (2012), a November 2012 winter storm, and a February 2013 blizzard]. The results show a 20%–30% improvement in the systematic and random error of 10-m wind speed over all stations and storms, using various storm combinations for the training dataset. This study indicates that 10–13 storms in the training dataset are sufficient to reduce the errors in the prediction, and a selection that is based on occurrence (chronological sequence) is also considered to be efficient.
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Gagne, David John, Amy McGovern, Sue Ellen Haupt, Ryan A. Sobash, John K. Williams, and Ming Xue. "Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles." Weather and Forecasting 32, no. 5 (September 22, 2017): 1819–40. http://dx.doi.org/10.1175/waf-d-17-0010.1.
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Abstract Forecasting severe hail accurately requires predicting how well atmospheric conditions support the development of thunderstorms, the growth of large hail, and the minimal loss of hail mass to melting before reaching the surface. Existing hail forecasting techniques incorporate information about these processes from proximity soundings and numerical weather prediction models, but they make many simplifying assumptions, are sensitive to differences in numerical model configuration, and are often not calibrated to observations. In this paper a storm-based probabilistic machine learning hail forecasting method is developed to overcome the deficiencies of existing methods. An object identification and tracking algorithm locates potential hailstorms in convection-allowing model output and gridded radar data. Forecast storms are matched with observed storms to determine hail occurrence and the parameters of the radar-estimated hail size distribution. The database of forecast storms contains information about storm properties and the conditions of the prestorm environment. Machine learning models are used to synthesize that information to predict the probability of a storm producing hail and the radar-estimated hail size distribution parameters for each forecast storm. Forecasts from the machine learning models are produced using two convection-allowing ensemble systems and the results are compared to other hail forecasting methods. The machine learning forecasts have a higher critical success index (CSI) at most probability thresholds and greater reliability for predicting both severe and significant hail.
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Zhang, Fan, Ming Li, Andrew C. Ross, Serena Blyth Lee, and Da-Lin Zhang. "Sensitivity Analysis of Hurricane Arthur (2014) Storm Surge Forecasts to WRF Physics Parameterizations and Model Configurations." Weather and Forecasting 32, no. 5 (September 19, 2017): 1745–64. http://dx.doi.org/10.1175/waf-d-16-0218.1.
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Abstract Through a case study of Hurricane Arthur (2014), the Weather Research and Forecasting (WRF) Model and the Finite Volume Coastal Ocean Model (FVCOM) are used to investigate the sensitivity of storm surge forecasts to physics parameterizations and configurations of the initial and boundary conditions in WRF. The turbulence closure scheme in the planetary boundary layer affects the prediction of the storm intensity: the local closure scheme produces lower equivalent potential temperature than the nonlocal closure schemes, leading to significant reductions in the maximum surface wind speed and surge heights. On the other hand, higher-class cloud microphysics schemes overpredict the wind speed, resulting in large overpredictions of storm surge at some coastal locations. Without cumulus parameterization in the outermost domain, both the wind speed and storm surge are grossly underpredicted as a result of large precipitation decreases in the storm center. None of the choices for the WRF physics parameterization schemes significantly affect the prediction of Arthur’s track. Sea surface temperature affects the latent heat release from the ocean surface and thus storm intensity and storm surge predictions. The large-scale atmospheric circulation models provide the initial and boundary conditions for WRF, and influence both the track and intensity predictions, thereby changing the spatial distribution of storm surge along the coastline. These sensitivity analyses underline the need to use an ensemble modeling approach to improve the storm surge forecasts.
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DATTATRI, J., and P. VIJAYA KUMAR. "Wave Prediction for the east coast of India under storm conditions in the Bay of Bengal." MAUSAM 25, no. 2 (February 7, 2022): 211–22. http://dx.doi.org/10.54302/mausam.v25i2.5195.
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Cyclonic storms are frequent in the Bay of Bengal particularly during the NE monsoon period. Some of these storms are severe and generate high waves which cause havoc in the coastal regions. This paper presents an analysis of the cyclonic storm which hit coastal Andhra Pradesh on 7 November 1969.
Wave prediction under storm conditions involves an analysis of moving fetches and variable wind speeds. Wilson's graphical method incorporating the latest available wave prediction relations was used for wave predicted waves which are deep water waves, were modified to account for refraction, shoaling and bottom friction effects as they enter shallower waters. The predicted waves were compared with the waves observed by the Visakhapatnam outer harbour authorities.
The results of the analysis suggest that (i) Wilson's graphical method can be applied for wave prediction for Indian coasts under storm conditions, (ii) the recommended value of bottom friction factor appears to be low and (iii) waves of considerable height are experienced even in areas not in the direct path of the cyclone.
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Burke, Amanda, Nathan Snook, David John Gagne II, Sarah McCorkle, and Amy McGovern. "Calibration of Machine Learning–Based Probabilistic Hail Predictions for Operational Forecasting." Weather and Forecasting 35, no. 1 (January 23, 2020): 149–68. http://dx.doi.org/10.1175/waf-d-19-0105.1.
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Abstract In this study, we use machine learning (ML) to improve hail prediction by postprocessing numerical weather prediction (NWP) data from the new High-Resolution Ensemble Forecast system, version 2 (HREFv2). Multiple operational models and ensembles currently predict hail, however ML models are more computationally efficient and do not require the physical assumptions associated with explicit predictions. Calibrating the ML-based predictions toward familiar forecaster output allows for a combination of higher skill associated with ML models and increased forecaster trust in the output. The observational dataset used to train and verify the random forest model is the Maximum Estimated Size of Hail (MESH), a Multi-Radar Multi-Sensor (MRMS) product. To build trust in the predictions, the ML-based hail predictions are calibrated using isotonic regression. The target datasets for isotonic regression include the local storm reports and Storm Prediction Center (SPC) practically perfect data. Verification of the ML predictions indicates that the probability magnitudes output from the calibrated models closely resemble the day-1 SPC outlook and practically perfect data. The ML model calibrated toward the local storm reports exhibited better or similar skill to the uncalibrated predictions, while decreasing model bias. Increases in reliability and skill after calibration may increase forecaster trust in the automated hail predictions.
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Snook, Nathan, Youngsun Jung, Jerald Brotzge, Bryan Putnam, and Ming Xue. "Prediction and Ensemble Forecast Verification of Hail in the Supercell Storms of 20 May 2013." Weather and Forecasting 31, no. 3 (May 20, 2016): 811–25. http://dx.doi.org/10.1175/waf-d-15-0152.1.
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Abstract Despite recent advances in storm-scale ensemble NWP, short-term (0–90 min) explicit forecasts of severe hail remain a major challenge as a result of the fast evolution and short time scales of hail-producing convective storms and the substantial uncertainty associated with the microphysical representation of hail. In this study, 0–90-min ensemble hail forecasts for the supercell storms of 20 May 2013 over central Oklahoma are examined and verified, with the goals of 1) evaluating ensemble forecast performance, 2) comparing the advantages and limitations of different forecast fields potentially suitable for the prediction of hail and severe hail in a Warn-on-Forecast setting, and 3) evaluating the use of dual-polarization radar observations for hail forecast validation. To address the challenges of hail prediction and to produce skillful forecasts, the ensemble uses a two-moment microphysics scheme that explicitly predicts a hail-like rimed-ice category and is run with a grid spacing of 500 m. Radar reflectivity factor and radial velocity, along with surface observations, are assimilated every 5 min for 1 h as the storms were developing to maturity, followed by a 90-min ensemble forecast. Several methods of hail prediction and hail forecast verification are then examined, including the prediction of the maximum hail size compared to Storm Prediction Center (SPC) and Meteorological Phenomena Identification Near the Ground (mPING) hail observations, and verification of model data against single- and dual-polarization radar-derived fields including hydrometeor classification algorithm (HCA) output and the maximum estimated size of hail (MESH). The 0–90-min ensemble hail predictions are found to be marginally to moderately skillful depending on the verification method used.
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Ren, Xiaochen, Biqiang Zhao, Zhipeng Ren, and Bo Xiong. "Ionospheric TEC Prediction in China during Storm Periods Based on Deep Learning: Mixed CNN-BiLSTM Method." Remote Sensing 16, no. 17 (August 27, 2024): 3160. http://dx.doi.org/10.3390/rs16173160.
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Applying deep learning to high-precision ionospheric parameter prediction is a significant and growing field within the realm of space weather research. This paper proposes an improved model, Mixed Convolutional Neural Network (CNN)—Bidirectional Long Short-Term Memory (BiLSTM), for predicting the Total Electron Content (TEC) in China. This model was trained using the longest available Global Ionospheric Maps (GIM)-TEC from 1998 to 2023 in China, and underwent an interpretability analysis and accuracy evaluation. The results indicate that historical TEC maps play the most critical role, followed by Kp, ap, AE, F10.7, and time factor. The contributions of Dst and Disturbance Index (DI) to improving accuracy are relatively small but still essential. In long-term predictions, the contributions of the geomagnetic index, solar activity index, and time factor are higher. In addition, the model performs well in short-term predictions, accurately capturing the occurrence, evolution, and classification of ionospheric storms. However, as the predicted length increases, the accuracy gradually decreases, and some erroneous predictions may occur. The northeast region exhibits lower accuracy but a higher F1 score, which may be attributed to the frequency of ionospheric storm occurrences in different locations. Overall, the model effectively predicts the trends and evolution processes of ionospheric storms.
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Tao, Tianyou, Peng Shi, Hao Wang, Lin Yuan, and Sheng Wang. "Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds." Applied Sciences 11, no. 20 (October 11, 2021): 9441. http://dx.doi.org/10.3390/app11209441.
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Wind-sensitive structures usually suffer from violent vibrations or severe damages under the action of tropical storms. It is of great significance to forecast tropical-storm winds in advance for the sake of reducing or avoiding consequent losses. The model used for forecasting becomes a primary concern in engineering applications. This paper presents a performance evaluation of linear and nonlinear models for the short-term forecasting of tropical storms. Five extensively employed models are adopted to forecast wind speeds using measured samples from the tropical storm Rumbia, which facilitates a comparison of the predicting performances of different models. The analytical results indicate that the autoregressive integrated moving average (ARIMA) model outperforms the other models in the one-step ahead prediction and presents the least forecasting errors in both the mean and maximum wind speeds. However, the support vector regression (SVR) model has the worst performance on the selected dataset. When it comes to the multi-step ahead forecasting, the prediction error of each model increases as the number of steps expands. Although each model shows an insufficient ability to capture the variation of future wind speed, the ARIMA model still appears to have the least forecasting errors. Hence, the ARIMA model can offer effective short-term forecasting of tropical-storm winds in both one-step and multi-step scenarios.
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Krieger, Daniel, Sebastian Brune, Patrick Pieper, Ralf Weisse, and Johanna Baehr. "Skillful decadal prediction of German Bight storm activity." Natural Hazards and Earth System Sciences 22, no. 12 (December 14, 2022): 3993–4009. http://dx.doi.org/10.5194/nhess-22-3993-2022.
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Abstract. We evaluate the prediction skill of the Max Planck Institute Earth System Model (MPI-ESM) decadal hindcast system for German Bight storm activity (GBSA) on a multiannual to decadal scale. We define GBSA every year via the most extreme 3-hourly geostrophic wind speeds, which are derived from mean sea-level pressure (MSLP) data. Our 64-member ensemble of annually initialized hindcast simulations spans the time period 1960–2018. For this period, we compare deterministically and probabilistically predicted winter MSLP anomalies and annual GBSA with a lead time of up to 10 years against observations. The model produces poor deterministic predictions of GBSA and winter MSLP anomalies for individual years but fair predictions for longer averaging periods. A similar but smaller skill difference between short and long averaging periods also emerges for probabilistic predictions of high storm activity. At long averaging periods (longer than 5 years), the model is more skillful than persistence- and climatology-based predictions. For short aggregation periods (4 years and less), probabilistic predictions are more skillful than persistence but insignificantly differ from climatological predictions. We therefore conclude that, for the German Bight, probabilistic decadal predictions (based on a large ensemble) of high storm activity are skillful for averaging periods longer than 5 years. Notably, a differentiation between low, moderate, and high storm activity is necessary to expose this skill.
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Cai, Huaqing, and Robert E. Dumais. "Object-Based Evaluation of a Numerical Weather Prediction Model’s Performance through Forecast Storm Characteristic Analysis." Weather and Forecasting 30, no. 6 (November 18, 2015): 1451–68. http://dx.doi.org/10.1175/waf-d-15-0008.1.
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Abstract Traditional pixel-versus-pixel forecast evaluation scores such as the critical success index (CSI) provide a simple way to compare the performances of different forecasts; however, they offer little information on how to improve a particular forecast. This paper strives to demonstrate what additional information an object-based forecast evaluation tool such as the Method for Object-Based Diagnostic Evaluation (MODE) can provide in terms of assessing numerical weather prediction models’ convective storm forecasts. Forecast storm attributes evaluated by MODE in this paper include storm size, intensity, orientation, aspect ratio, complexity, and number of storms. Three weeks of the High Resolution Rapid Refresh (HRRR) model’s precipitation forecasts during the summer of 2010 over the eastern two-thirds of the contiguous United States were evaluated as an example to demonstrate the methodology. It is found that the HRRR model was able to forecast convective storm characteristics rather well either as a function of time of day or as a function of storm size, although significant bias does exist, especially in terms of storm number and storm size. Another interesting finding is that the model’s ability of forecasting new storm initiation varies substantially by regions, probably as a result of its different skills in forecasting convection driven by different forcing mechanisms (i.e., diurnal heating vs synoptic-scale frontal systems).
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RAO, Y. R., P. CHITTIBABU, S. K. DUBE, A. D. RAO, and P. C. SINHA. "Storm surge prediction and frequency analysis for Andhra coast of India." MAUSAM 48, no. 4 (November 24, 2021): 555–66. http://dx.doi.org/10.54302/mausam.v48i4.4322.
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Storm surges associated with severe cyclonic storms are common occurrences along the east coast of India. The coastal districts of Andhra Pradesh have experienced major surges in the past. Storm surges and the rains associated with cyclones are major causes for coastal flooding in this region. An attempt has been made, in this paper, to simulate surges along the Andhra coast that would have occurred due to severe cyclones during 1891-1996. Inland inundation due to surges is also estimated by using an empirical formula. The computed results are validated with the available observations. The comparison using post-storm survey reports, appears reasonably good to assert that the model is capable of predicting the peak surge amplitude and its location. Frequency of occurrence relationships is obtained for various zones along the coastal region for the purpose of risk analysis.
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Lukens, Katherine E., and Ernesto Hugo Berbery. "Winter Storm Tracks and Related Weather in the NCEP Climate Forecast System Weeks 3–4 Reforecasts for North America." Weather and Forecasting 34, no. 3 (June 1, 2019): 751–72. http://dx.doi.org/10.1175/waf-d-18-0113.1.
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Abstract This article examines to what extent the NCEP Climate Forecast System (CFS) weeks 3–4 reforecasts reproduce the CFS Reanalysis (CFSR) storm-track properties, and if so, whether the storm-track behavior can contribute to the prediction of related winter weather in North America. The storm tracks are described by objectively tracking isentropic potential vorticity (PV) anomalies for two periods (base, 1983–2002; validation, 2003–10) to assess their value in a more realistic forecast mode. Statistically significant positive PV biases are found in the storm-track reforecasts. Removal of systematic errors is found to improve general storm-track features. CFSR and Reforecast (CFSRR) reproduces well the observed intensity and spatial distributions of storm-track-related near-surface winds, with small yet significant biases found in the storm-track regions. Removal of the mean wind bias further reduces the error on average by 12%. The spatial distributions of the reforecast precipitation correspond well with the reanalysis, although significant positive biases are found across the contiguous United States. Removal of the precipitation bias reduces the error on average by 25%. The bias-corrected fields better depict the observed variability and exhibit additional improvements in the representation of winter weather associated with strong-storm tracks (the storms with more intense PV). Additionally, the reforecasts reproduce the characteristic intensity and frequency of hazardous strong-storm winds. The findings suggest a potential use of storm-track statistics in the advancement of subseasonal-to-seasonal weather prediction in North America.
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Melby, Jeffrey A., Fatima Diop, Norberto Nadal-Caraballo, Alex Taflanidis, and Victor Gonzalez. "HURRICANE WATER LEVEL PREDICTION USING SURROGATE MODELING." Coastal Engineering Proceedings, no. 36 (December 30, 2018): 57. http://dx.doi.org/10.9753/icce.v36.currents.57.
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For this study, the surrogate was constructed using kriging (Jia et al. 2015). The high fidelity coupled surge and wave numerical modelling for the Gulf of Mexico was used as the training set. The numerical model was either ADCIRC and STWAVE or ADCIRC and SWAN in the nearshore. The surrogate models were trained using tropical storm parameters (latitude, longitude, central pressure, radius to maximum wind speed, storm heading, and forward speed) at a specific location as inputs and individual responses (e.g. surge) as outputs. Tide was computed separately using ADCIRC and linearly superimposed with surge to get total water level. The regional surrogates accurately reproduced both peaks and time series of water levels for historical storms. An extensive validation was conducted to determine the optimal application of the kriging approach. In this paper we will report the efficient design-of-experiments approach, surrogate training and validation.
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Yang, Jaemo, Marina Astitha, Luca Delle Monache, and Stefano Alessandrini. "An Analog Technique to Improve Storm Wind Speed Prediction Using a Dual NWP Model Approach." Monthly Weather Review 146, no. 12 (November 14, 2018): 4057–77. http://dx.doi.org/10.1175/mwr-d-17-0198.1.
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Abstract This study presents a new implementation of the analog ensemble method (AnEn) to improve the prediction of wind speed for 146 storms that have impacted the northeast United States in the period 2005–16. The AnEn approach builds an ensemble by using a set of past observations that correspond to the best analogs of numerical weather prediction (NWP). Unlike previous studies, dual-predictor combinations are used to generate AnEn members, which include wind speed, wind direction, and 2-m temperature, simulated by two state-of-the-science atmospheric models [the Weather Research and Forecasting (WRF) Model and the Regional Atmospheric Modeling System–Integrated Community Limited Area Modeling System (RAMS–ICLAMS)]. Bias correction is also applied to each analog to gain additional benefits in predicting wind speed. Both AnEn and the bias-corrected analog ensemble (BCAnEn) are tested with a weighting strategy, which optimizes the predictor combination with root-mean-square error (RMSE) minimization. A leave-one-out cross validation is implemented, that is, each storm is predicted using the remaining 145 as the training dataset, with modeled and observed values over 80 stations in the northeast United States. The results show improvements of 9%–42% and 1%–29% with respect to original WRF and ICLAMS simulations, as measured by the RMSE of individual storms. Moreover, for two high-impact tropical storms (Irene and Sandy), BCAnEn significantly reduces the error of raw prediction (average RMSE reduction of 22% for Irene and 26% for Sandy). The AnEn and BCAnEn techniques demonstrate their potential to combine different NWP models to improve storm wind speed prediction, compared to the use of a single NWP.
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Tsai, C. P., C. Y. You, and C. Y. Chen. "Storm-surge prediction at the Tanshui estuary: development model for maximum storm surges." Natural Hazards and Earth System Sciences Discussions 1, no. 6 (December 10, 2013): 7333–56. http://dx.doi.org/10.5194/nhessd-1-7333-2013.
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Abstract. This study applies artificial networks, including both the supervised multilayer perception neural network and the radial basis function neural network to the prediction of storm-surges at the Tanshui estuary in Taiwan. The optimum parameters for the prediction of the maximum storm-surges based on 22 previous sets of data are discussed. Two different neural network methods are adopted to build models for the prediction of storm surges and the importance of each factor is also discussed. The factors relevant to the maximum storm surges, including the pressure difference, maximum wind speed and wind direction at the Tanshui Estuary and the flow rate at the upstream station, are all investigated. These good results can further be applied to build a neural network model for prediction of storm surges with time series data.
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Snyder, Andrew, Zhaoxia Pu, and Carolyn A. Reynolds. "Impact of Stochastic Convection on Ensemble Forecasts of Tropical Cyclone Development." Monthly Weather Review 139, no. 2 (February 1, 2011): 620–26. http://dx.doi.org/10.1175/2010mwr3341.1.
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Abstract Two versions of the Navy Operational Global Atmospheric Prediction System (NOGAPS) global ensemble, with and without a stochastic convection scheme, are compared regarding their performance in predicting the development and evolution of tropical cyclones. Forecasts of four typhoons, one tropical storm, and two selected nondeveloping tropical systems from The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign and Tropical Cyclone Structure 2008 (T-PARC/TCS-08) field program during August and September 2008 are evaluated. It is found that stochastic convection substantially increases the spread in ensemble storm tracks and in the vorticity and height fields in the vicinity of the storm. Stochastic convection also has an impact on the number of ensemble members predicting genesis. One day prior to the system being declared a tropical depression, on average, 31% of the ensemble members predict storm development when the ensemble includes initial perturbations only. When stochastic convection is included, this percentage increases to 50%, but the number of “false alarms” for two nondeveloping systems also increases. However, the increase in false alarms is smaller than the increase in correct development predictions, indicating that stochastic convection may have the potential for improving tropical cyclone forecasting.
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Feng, Xiao-Chen, and Hang Xu. "Accurate storm surge prediction using a multi-recurrent neural network structure." Physics of Fluids 35, no. 3 (March 2023): 037108. http://dx.doi.org/10.1063/5.0137792.
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This paper considers storm surge prediction using a neural network and considering multiple physical characteristics. Based on the factors that influence storm surges and historical observation data, we divide the input to the neural network into time features extracted from the prediction target and the auxiliary features that affect storm surges, and construct a feature gate within multiple recurrent neural network (RNN) cells. Historical hurricane data are used to assess the effectiveness and accuracy of the proposed model. Comparative analysis against a long short-term memory (LSTM) storm surge prediction model is conducted to verify the prediction performance of the proposed method. The comparison results show that the multi-RNN model is superior to the LSTM model in terms of four evaluation metrics and for all lead times. In particular, the multi-RNN model accurately predicts the maximum storm surge water level, and the prediction results are more consistent with the rise and fall of the water. A comparison of the storm surge forecasts using inputs from different time intervals under different evaluation indices confirms the generalization and stability of our proposed model. The experiments of storm surge prediction at six stations further confirm the wide applicability of the model.
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Srivastava, N. "Predicting the occurrence of super-storms." Annales Geophysicae 23, no. 9 (November 22, 2005): 2989–95. http://dx.doi.org/10.5194/angeo-23-2989-2005.
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Abstract. A comparative study of five super-storms (Dst<-300 nT) of the current solar cycle after the launch of SoHO, to identify solar and interplanetary variables that influence the magnitude of resulting geomagnetic storms, is described. Amongst solar variables, the initial speed of a CME is considered the most reliable predictor of the strength of the associated geomagnetic storm because fast mass ejections are responsible for building up the ram pressure at the Earth's magnetosphere. However, although most of the super-storms studied were associated with high speed CMEs, the Dst index of the resulting geomagnetic storms varied between -300 to -472 nT. The most intense storm of 20 November 2003, (Dst ~ -472 nT) had its source in a comparatively smaller active region and was associated with a relatively weaker, M-class flare while all other super-storms had their origins in large active regions and were associated with strong X-class flares. However, this superstorm did not show any associated extraordinary solar and interplanetary characteristics. The study also reveals the challenge in the reliable prediction of the magnitude of a geomagnetic storm from solar and interplanetary variables.
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Keith, Elinor, and Lian Xie. "Predicting Atlantic Tropical Cyclone Seasonal Activity in April." Weather and Forecasting 24, no. 2 (April 1, 2009): 436–55. http://dx.doi.org/10.1175/2008waf2222139.1.
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Abstract Seasonal hurricane forecasts are continuing to develop skill, although they are still subject to large uncertainties. This study uses a new methodology of cross-correlating variables against empirical orthogonal functions (EOFs) of the hurricane track density function (HTDF) to select predictors. These predictors are used in a regression model for forecasting seasonal named storm, hurricane, and major hurricane activity in the entire Atlantic, the Caribbean Sea, and the Gulf of Mexico. In addition, a scheme for predicting landfalling tropical systems along the U.S. Gulf of Mexico, southeastern, and northeastern coastlines is developed, but predicting landfalling storms adds an extra layer of uncertainty to an already complex problem, and on the whole these predictions do not perform as well. The model performs well in the basin-wide predictions over the entire Atlantic and Caribbean, with the predictions showing an improvement over climatology and random chance at a 95% confidence level. Over the Gulf of Mexico, only named storms showed that level of predictability. Predicting landfalls proves more difficult, and only the prediction of named storms along the U.S. southeastern and Gulf coasts shows an improvement over random chance at the 95% confidence level. Tropical cyclone activity along the U.S. northeastern coast is found to be unpredictable in this model; with the rarity of events, the model is unstable.
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Osinski, R., P. Lorenz, T. Kruschke, M. Voigt, U. Ulbrich, G. C. Leckebusch, E. Faust, T. Hofherr, and D. Majewski. "An approach to build an event set of European wind storms based on ECMWF EPS." Natural Hazards and Earth System Sciences Discussions 3, no. 2 (February 9, 2015): 1231–68. http://dx.doi.org/10.5194/nhessd-3-1231-2015.
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Abstract. The properties of European wind storms under present climate conditions are estimated on the basis of surface wind forecasts from the European Center of Medium-Range Weather Forecast (ECMWF) Ensemble Prediction System (EPS). While the EPS is designed to provide forecast information of the range of possible weather developments starting from the observed state of weather, we use its archive in a climatological context. It provides a large number of modifications of observed storm events, and includes storms that did not occur in reality. Thus it is possible to create a large sample of storm events, which entirely originate from a physically consistent model, whose ensemble spread represents feasible alternative storm realizations of the covered period. This paper shows that the huge amount of identifiable events in the EPS is applicable to reduce uncertainties in a wide range of fields of research focusing on winter storms. Wind storms are identified and tracked in this study over their lifetime using an algorithm, based on the local exceedance of the 98th percentile of instantaneous 10 m wind speed, calculating a storm severity measure. After removing inhomogeneities in the dataset arising from major modifications of the operational system, the distributions of storm severity, storm size and storm duration are computed. The overall principal properties of the homogenized EPS storm data set are in good agreement with storms from the ERA-Interim dataset, making it suitable for climatological investigations of these extreme events. A demonstrated benefit in the climatological context by the EPS is presented. It gives a clear evidence of a linear increase of maximum storm intensity and wind field size with storm duration. This relation is not recognizable from a sparse ERA-Interim sample for long lasting events, as the number of events in the reanalysis is not sufficient to represent these characteristics.
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Alpay, Berk A., David Wanik, Peter Watson, Diego Cerrai, Guannan Liang, and Emmanouil Anagnostou. "Dynamic Modeling of Power Outages Caused by Thunderstorms." Forecasting 2, no. 2 (May 22, 2020): 151–62. http://dx.doi.org/10.3390/forecast2020008.
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Thunderstorms are complex weather phenomena that cause substantial power outages in a short period. This makes thunderstorm outage prediction challenging using eventwise outage prediction models (OPMs), which summarize the storm dynamics over the entire course of the storm into a limited number of parameters. We developed a new, temporally sensitive outage prediction framework designed for models to learn the hourly dynamics of thunderstorm-caused outages directly from weather forecasts. Validation of several models built on this hour-by-hour prediction framework and comparison with a baseline model show abilities to accurately report temporal and storm-wide outage characteristics, which are vital for planning utility responses to storm-caused power grid damage.
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Tang, Xu, Jia, Luo, and Shao. "Estimating Errors in Sizing LID Device and Overflow Prediction Using the Intensity-Duration-Frequency Method." Water 11, no. 9 (September 5, 2019): 1853. http://dx.doi.org/10.3390/w11091853.
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Low impact development (LID) devices or green infrastructures have been advocated for urban stormwater management worldwide. Currently, the design and evaluation of LID devices adopt the Intensity-Duration-Frequency (IDF) method, which employs the average rainfall intensity. However, due to variations of rainfall intensity during a storm event, using average rainfall intensity may generate certain errors when designing a LID device. This paper presents an analytical study to calculate the magnitude of such errors with respect to LID device design and associated device performance evaluation. The normal distribution rainfall (NDR) with different standard deviations was employed to represent realistic rainfall processes. Compared with NDR method, the error in sizing the LID device was determined using the IDF method. Moreover, the overflow difference calculated using the IDF method was evaluated. We employed a programmed hydrological model to simulate different design scenarios. Using storm data from 31 regions with different climatic conditions in continental China, the results showed that different rainfall distributions (as represented by standard deviations (σ) of 5, 3, and 2) have little influence on the design depth of LID devices in most regions. The relative difference in design depth using IDF method was less than 1.00% in humid areas, −0.61% to 3.97% in semi-humid areas, and the significant error was 46.13% in arid areas. The maximum absolute difference in design depth resulting from the IDF method was 2.8 cm. For a LID device designed for storms with a 2-year recurrence interval, when meeting for the 5-year storm, the relative differences in calculated overflow volume using IDF method ranged from 19.8% to 95.3%, while those for the 20-year storm ranged from 7.4% to 40.5%. The average relative difference of the estimated overflow volume was 29.9% under a 5-year storm, and 12.0% under a 20-year storm. The relative difference in calculated overflow volumes using IDF method showed a decreasing tendency from northwest to southeast. Findings from this study suggest that the existing IDF method is adequate for use in sizing LID devices when the design storm is not usually very intense. However, accurate rainfall process data are required to estimate the overflow volume under large storms.