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Tropical cyclones are one of the most powerful and destructive natural phenomena on earth. Tropical storms and heavy rains can cause floods, which lead to human lives and economic loss. Devastating winds accompanying cyclones heavily affect…
The objective of this paper is to employ machine learning (ML) and deep learning (DL) techniques to obtain from input data (storm features) available in or derived from the HURDAT2 database models capable of simulating important hurricane…
Anthropogenic influences have been linked to tropical cyclone (TC) poleward migration, TC extreme precipitation, and an increased proportion of major hurricanes [1, 2, 3, 4]. Understanding past TC trends and variability is critical for…
Recent extreme tropical cyclones caused extensive damages to critical civil infrastructure globally. To better capture the unique hurricane wind characteristics, a large eddy simulation (LES) Hurricane Boundary Layer (HBL) model is…
This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently…
Hurricanes are cyclones circulating about a defined center whose closed wind speeds exceed 75 mph originating over tropical and subtropical waters. At landfall, hurricanes can result in severe disasters. The accuracy of predicting their…
The near universal references to the above paper by most of the major US media outlets and blogs since Katrina and Rita made US landfall requires a response from a few of us who study hurricanes. Having been involved with hurricane research…
Direct computer simulation of intense tropical cyclones (TCs) in weather models is limited by computational expense. Intense TCs are rare and have small-scale structures, making it difficult to produce large ensembles of storms at high…
Accurate cyclone forecasting is essential for minimizing loss of life, infrastructure damage, and economic disruption. Traditional numerical weather prediction models, though effective, are computationally intensive and prone to error due…
Analyzing big geophysical observational data collected by multiple advanced sensors on various satellite platforms promotes our understanding of the geophysical system. For instance, convolutional neural networks (CNN) have achieved great…
Tropical cyclones can be of varied intensity and cause a huge loss of lives and property if the intensity is high enough. Therefore, the prediction of the intensity of tropical cyclones advance in time is of utmost importance. We propose a…
The radius of maximum wind ($R_{max}$) in a hurricane governs the footprint of hazards, particularly damaging wind and rainfall. However, $R_{max}$ is noisy to observe directly and is poorly resolved in reanalyses and climate models. In…
Based on realistic estimates of geophysical conditions it is demonstrated that by practical means; (1) the intensity of a hurricane can be diminished before making landfall; (2) and other circumstances, a potential hurricane might be…
The rising number of extreme climate events in the past decades has motivated the need for a thorough consideration of tropical cyclone genesis and intensity, given the sea-surface temperature (SST). In this paper, we present an analysis of…
Minimum central pressure ($P_{min}$) is an integrated measure of the tropical cyclone wind field and is known to be a useful indicator of storm damage potential. A simple model that predicts $P_{min}$ from routinely-estimated quantities,…
Traditional methods for enhancing tropical cyclone (TC) intensity from climate model outputs or projections have primarily relied on either dynamical or statistical downscaling. With recent advances in deep learning (DL) techniques, a…
Air-sea drag governs the momentum transfer between the atmosphere and the ocean, and remains largely unknown in hurricane winds. We revisit the momentum budget and eddy-covariance methods to estimate the surface drag coefficient in the…
Wind shear measured by Doppler tracking of the Huygens probe is evaluated, and found to be within the range anticipated by pre-flight assessments (namely less than two times the Brunt-Vaisala frequency). The strongest large-scale shear…
Many empirical hurricane economic loss models consider only wind speed and neglect storm size. These models may be inadequate in accurately predicting the losses of super-sized storms, such as Hurricane Sandy in 2012. In this study, we…
Particularly important to hurricane risk assessment for coastal regions is finding accurate approximations of return probabilities of maximum windspeeds. Since extremes in maximum windspeed have a direct relationship to minimums in the…