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1. The atmospheric blocking data produced for the following publication: Booth, J. F., V. Narinesingh, K. L. Towey, J. Jeyaratnam, 2021: Storm Surge, Blocking, and Cyclones: A Compound Hazards Analysis for the Northeast United States, Journal of Applied Meteorology and Climatology. 60(11), 1531-1544.

   https://doi.org/10.7910/DVN/Z0SYUG  

   Booth, James (January 2024, Harvard Dataverse)

   Each netcdf (.nc) file contains the location of atmospheric blocks over North America and its surrounding oceans. The data are gridded in latitude and longitude. The block data is simply a mask, with 1s in locations where a block has been detected and zeros elsewhere. A full description of the calculations used in generating the data is in the related journal article. If you would like the data in a different file type, contact James Booth. Contact: jbooth@ccny.cuny.edu 

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2. Extratropical Cyclone Tracks used in the following publication: Booth, J. F., V. Narinesingh, K. L. Towey, J. Jeyaratnam, 2021: Storm Surge, Blocking, and Cyclones: A Compound Hazards Analysis for the Northeast United States, Journal of Applied Meteorology and Climatology. 60(11), 1531-1544.

   https://doi.org/10.7910/DVN/YC3STT  

   Booth, James (January 2024, Harvard Dataverse)

   The datafiles in this dataset contain tropical or extratropical cyclone track information. Each datafile name includes the name of city. The datafile contains track information (location and time) for all cyclones that pass within 1000 km of the named city. Each datafile contains a single variable, which is a datatype called: structure in Matlab (or, dictionary). Each entry in the dictionary is an cyclone track. The entries in the dictionary are in chronological order, based on the starting date of the track. The relevant fields in the dictionary are the center latitude, center longitude, and date. The extratropical tracks were identified by applying Mike Bauer’s MCMS tracking tool to ERA5 reanalysis data. The tropical cyclone tracks are from HURDAT2. Full details can be found in the Booth et al. (2021) article referenced with this dataset. For more information contact James Booth: jbooth@ccny.cuny.edu 

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3. Tropical cyclone storm surge probabilities for the east coast of the United States: a cyclone-based perspective

   https://doi.org/10.5194/nhess-22-1287-2022  

   Towey, Katherine L.; Booth, James F.; Rodriguez Enriquez, Alejandra; Wahl, Thomas (January 2022, Natural Hazards and Earth System Sciences)

   Abstract. To improve our understanding of the influence of tropicalcyclones (TCs) on coastal flooding, the relationships between storm surgeand TC characteristics are analyzed for 12 sites along the east coast of theUnited States. This analysis offers a unique perspective by first examiningthe relationship between the characteristics of TCs and their resultingstorm surge and then determining the probabilities of storm surge associatedwith TCs based on exceeding certain TC characteristic thresholds. Usingobservational data, the statistical dependencies of storm surge on TCs areexamined for these characteristics: TC proximity, intensity, path angle, andpropagation speed, by applying both exponential and linear fits to the data.At each tide gauge along the east coast of the United States, storm surge isinfluenced differently by these TC characteristics, with some locations morestrongly influenced by TC intensity and others by TC proximity. Thecorrelation for individual and combined TC characteristics increases whenconditional sorting is applied to isolate strong TCs close to a location.The probabilities of TCs generating surge exceeding specific return levels(RLs) are then analyzed for TCs passing within 500 km of a tide gauge, wherebetween 6 % and 28 % of TCs were found to cause surge exceeding the1-year RL. If only the closest and strongest TCs are considered, thepercentage of TCs that generate surge exceeding the 1-year RL is between 30 % and 70 % at sites north of Sewell's Point, VA, and over 65 % atalmost all sites south of Charleston, SC. When examining storm surgeproduced by TCs, single-variable regression provides a good fit, whilemulti-variable regression improves the fit, particularly when focusing on TCproximity and intensity, which are, probabilistically, the two mostinfluential TC characteristics on storm surge. 

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4. Storm Surge, Blocking, and Cyclones: A Compound Hazards Analysis for the Northeast United States

   https://doi.org/10.1175/JAMC-D-21-0062.1  

   Booth, James F.; Narinesingh, Veeshan; Towey, Katherine L.; Jeyaratnam, Jeyavinoth (October 2021, Journal of Applied Meteorology and Climatology)

   Abstract Storm surge is a weather hazard that can generate dangerous flooding and is not fully understood in terms of timing and atmospheric forcing. Using observations along the Northeast United States, surge is sorted based on duration and intensity to reveal distinct time-evolving behavior. Long-duration surge events slowly recede, while strong, short-duration events often involve negative surge in quick succession after the maximum. Using Lagrangian track information, the tropical and extratropical cyclones and atmospheric blocks that generate the surge events are identified. There is a linear correlation between surge duration and surge maximum, and the relationship is stronger for surge caused by extratropical cyclones as compared to those events caused by tropical cyclones. For the extremes based on duration, the shortest-duration strong surge events are caused by tropical cyclones, while the longest-duration events are most often caused by extratropical cyclones. At least half of long-duration surge events involve anomalously strong atmospheric blocking poleward of the cyclone, while strong, short-duration events are most often caused by cyclones in the absence of blocking. The dynamical influence of the blocks leads to slow-moving cyclones that take meandering paths. In contrast, for strong, short-duration events, cyclones travel faster and take a more meridional path. These unique dynamical scenarios provide better insight for interpreting the threat of surge in medium-range forecasts. 

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5. Probabilistic tropical cyclone surge hazard under future sea-level rise scenarios: A case study in the Chesapeake Bay region

   https://doi.org/10.1061/9780784484852.117  

   K. Kim, J.-W. Lee (January 2023, 2023 World Environmental & Water Resources Congress)

   Storm surge flooding caused by tropical cyclones is a devastating threat to coastal regions, and this threat is growing due to sea-level rise (SLR). Therefore, accurate and rapid projection of the storm surge hazard is critical for coastal communities. This study focuses on developing a new framework that can rapidly predict storm surges under SLR scenarios for any random synthetic storms of interest and assign a probability to its likelihood. The framework leverages the Joint Probability Method with Response Surfaces (JPM-RS) for probabilistic hazard characterization, a storm surge machine learning model, and a SLR model. The JPM probabilities are based on historical tropical cyclone track observations. The storm surge machine learning model was trained based on high-fidelity storm surge simulations provided by the U.S. Army Corps of Engineers (USACE). The SLR was considered by adding the product of the normalized nonlinearity, arising from surge-SLR interaction, and the sea-level change from 1992 to the target year, where nonlinearities are based on high-fidelity storm surge simulations and subsequent analysis by USACE. In this study, this framework was applied to the Chesapeake Bay region of the U.S. and used to estimate the SLR-adjusted probabilistic tropical cyclone flood hazard in two areas: One is an urban Virginia site, and the other is a rural Maryland site. This new framework has the potential to aid in reducing future coastal storm risks in coastal communities by providing robust and rapid hazard assessment that accounts for future sea-level rise. 

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