High winds and storm surges associated with torrential rain from tropical cyclones (TCs) cause massive destruction to property and cost the lives of many people. The coastline of the Bay of Bengal (BoB) ranks as one of the most susceptible to storm surges in the world due to low‐lying elevation and a high frequency of TC occurrence. This study uses data from 1885 to 2011 and a bivariate statistical copula to describe the relationship and dependency between empirical TC storm surge and reported wind speed before landfall at the BoB. Among the copulas and their families, an Archimedean, Gumbel copula with margins defined by the empirical distributions is specified as the most appropriate choice for the BoB. The model provides return periods for pairs of TC storm surge and 12‐hour pre‐landfall wind along the BoB coastline. On the shortest time scale, the BoB can expect a TC with 12‐hour pre‐landfall winds of at least 24 m s⁻¹ and surge heights of at least 4.0 m, on average, once every 3.9 years. On the other hand, the long‐term, worst case scenario suggests the BoB can expect 12‐hour pre‐landfall winds of 62 m s⁻¹ and surge heights of at least 8.0 m, on average, once every 311.8 years. Using a copula to model the combined frequency of cyclone wind speeds along with storm surges along the BoB coastline increases the understanding of the dangerous TC characteristics in this region, which can reduce fatalities and monetary losses.

Tropical cyclones, with their nearshore high wind speeds and deep storm surges, frequently strike the United States Gulf of Mexico coastline influencing millions of people and disrupting offshore economic activities. The combined risk of occurrence of tropical cyclone nearshore wind speeds and storm surges is assessed at 22 coastal cities throughout the United States Gulf of Mexico. The models used are extreme value copulas fitted with margins defined by the generalized Pareto distribution or combinations of Weibull, gamma, lognormal, or normal distributions. The statistical relationships between the nearshore wind speed and storm surge are provided for each coastal city prior to the copula model runs using Spearman's rank correlations. The strongest significant relationship between the nearshore wind speed and storm surge exists at Shell Beach, LA (ρ= 0.67), followed by South Padre Island, TX (ρ= 0.64). The extreme value Archimedean copula models for each city then provide return periods for specific nearshore wind speed and storm surge pairs. Of the 22 cities considered, Bay St. Louis, MS, has the shortest return period for a tropical cyclone with at least a 50 ms⁻¹nearshore wind speed and a 3 m surge (19.5 years, 17.1‐23.5). The 90% confidence intervals are created by recalculating the return periods for a fixed set of wind speeds and surge levels using 100 samples of the model parameters. The results of this study can be utilized by policy managers and government officials concerned with coastal populations and economic activity in the Gulf of Mexico.

In its first 2 years of operation, the ground‐based Terrestrial gamma ray flash and Energetic Thunderstorm Rooftop Array (TETRA)‐II array of gamma ray detectors has recorded 22 bursts of gamma rays of millisecond‐scale duration associated with lightning. In this study, we present the TETRA‐II observations detected at the three TETRA‐II ground‐level sites in Louisiana, Puerto Rico, and Panama together with the simultaneous radio frequency signals from the lightning data sets VAISALA Global Lightning Dataset, VAISALA National Lightning Detection Network, Earth Networks Total Lightning Network, and World Wide Lightning Location Network. The relative timing between the gamma ray events and the lightning activity is a key parameter for understanding the production mechanism(s) of the bursts. The gamma ray time profiles and their correlation with radio sferics suggest that the gamma ray events are initiated by lightning leader activity and are produced near the last stage of lightning leader channel development prior to the lightning return stroke.

Consideration of climate-related impacts on coasts is important to ensure readiness for disaster response. Local risk of storm surge and strong winds from hurricanes affecting Galveston, Texas, is quantified using a bivariate copula model fit to observed data. The model uses a two-dimensional Archimedean copula. Parametric uncertainty (5th and 95th percentiles) is quantified using a Monte Carlo procedure. The annual probability of a hurricane producing winds of at least 50 ms⁻¹ and a surge of at least 4 m is 1.7 percent with a 95 percent confidence interval of (1.33, 1.78) percent. The methodology can be extended to include inland flooding and can be applied elsewhere with available information.

Tropical cyclone (TC) rainfall amounts are compared from 1950–2017 for Houston, Texas and Miami, Florida to estimate the risk of TC rain in both cities. Following the wake of Hurricanes Harvey and Irma in 2017, concern has risen over the future of raininess in these locations. Per-event rainfall amounts are aggregated using tracks taken from HURDAT, time-of-rain gathered from National Weather Service daily weather maps, and rainfall totals taken from airport monitoring stations. Risk analysis tools include descriptive statistics, time series, and return frequencies for Houston and Miami, and spatially interpolated surfaces for Hurricanes Harvey and Irma. The season duration is longer in Miami than in Houston. The uppermost rainfall events in the distribution for Houston show a significant increase through time, suggesting the most intense rainfall events are becoming worse for Houston. The expected return frequency for a Harvey-like event (940 mm) in Houston is every 230 years, on average, and the 90th percentile rain of 286 mm is expected once every 17 years (11–29; 90% significance). The expected return frequency for an Irene-like event (261 mm—maximum for location) in Miami is every 173 years, on average, and the 90th percentile rain of 167 mm is expected once every 11 years (7–17; 90% significance). Results show a substantial difference between Houston and Miami TC rainfall climatologies similar to the differences of Hurricanes Harvey and Irma. Though emergency management must be tailored for each TC, management for inland TC rainfall may be more applicable in Houston than in Miami.

Tropical cyclones devastate coastlines around the world. The United States and surrounding areas experienced catastrophic extreme events in recent hurricane seasons. Understanding extreme hurricanes and how they change in a warming ocean environment is of the utmost importance. This study makes use of the historical, positive relationship between average summer sea surface temperatures (SSTs) and maximum hurricane wind speeds across the North Atlantic Basin from 1854–2018. Geographically weighted regression shows how the relationship between hurricane winds and SSTs varies across space. Each localized slope is used to increase historical wind speeds to represent winds in a three-degree Celsius warmer-than-average sea surface. The winds are then used to estimate the maximum intensity of the thirty-year hurricane (one with a 3.3% annual probability of occurrence) across the hexagonal grid using extreme value statistics. Viewing the results spatially allows for geographic patterns to emerge in the overall risk of major hurricane occurrence in warm SST environments. This study showcases the difference in the historical extreme compared to the potential future extreme in the hopes to better inform those charged with making important, life-saving decisions along the U.S. and neighboring coasts.