To meet the Biden-Harris administration's goal of deploying 30 GW of offshore wind power by 2030 and 110 GW by 2050, expansion of wind energy into U.S. territorial waters prone to tropical cyclones (TCs) and extratropical cyclones (ETCs) is essential. This requires a deeper understanding of cyclone-related risks and the development of robust, resilient offshore wind energy systems. This paper provides a comprehensive review of state-of-the-science measurement and modeling capabilities for studying TCs and ETCs, and their impacts across various spatial and temporal scales. We explore measurement capabilities for environments influenced by TCs and ETCs, including near-surface and vertical profiles of critical variables that characterize these cyclones. The capabilities and limitations of Earth system and mesoscale models are assessed for their effectiveness in capturing atmosphere–ocean–wave interactions that influence TC/ETC-induced risks under a changing climate. Additionally, we discuss microscale modeling capabilities designed to bridge scale gaps from the weather scale (a few kilometers) to the turbine scale (dozens to a few meters). We also review machine learning (ML)-based, data-driven models for simulating TC/ETC events at both weather and wind turbine scales. Special attention is given to extreme metocean conditions like extreme wind gusts, rapid wind direction changes, and high waves, which pose threats to offshore wind energy infrastructure. Finally, the paper outlines the research challenges and future directions needed to enhance the resilience and design of next-generation offshore wind turbines against extreme weather conditions.

Empirical-statistical downscaling (ESD) can be a computationally advantageous alternative to dynamical downscaling in representing a high-resolution regional climate. Two distinct strategies of ESD are employed here to reconstruct near-surface winds in a region of rugged terrain. ESD is used to reconstruct the innermost grid of a multiply nested mesoscale model framework for regional climate downscaling. An analog ensemble (AnEn) and a convolutional neural network (CNN) are compared in their ability to represent near-surface winds in the innermost grid in lieu of dynamical downscaling. Downscaling for a 30 year climatology of 10 m April winds is performed for southern MO, USA. Five years of training suffices for producing low mean absolute error and bias for both ESD techniques. However, root-mean-squared error is not significantly reduced by either scheme. In the case of the AnEn, this is due to a minority of cases not producing a satisfactory representation of high-resolution wind, accentuating the root-mean-squared error in spite of a small mean absolute error. Homogeneous comparison shows that the AnEn produces smaller errors than the CNN. Though further tuning may improve results, the ESD techniques considered here show that they can produce a reliable, computationally inexpensive method for reconstructing high-resolution 10 m winds over complex terrain.

To better understand the life cycles of convective elements in numerical model simulations, a convective updraft tracking algorithm, dubbed the Statistical and Programmable Objective Updraft Tracker (SPOUT), is developed. SPOUT identifies updraft cores in horizontal slices through a given model data set, links these cores in three dimensions to form vertically coherent updrafts, and then tracks updrafts in time via estimates of the mean local advection. SPOUT is evaluated in idealized hurricane simulations from both the Regional Atmospheric Modeling System (RAMS) and Weather Research and Forecasting (WRF). In both simulations, SPOUT indicates most updrafts live less than 60 min, but some can persist for over 2 h. Updrafts are typically manifested as shallow cumulus, cumulus congestus, and deep cumulonimbus. Overall, there is reasonable agreement between the vigor of convection and the amount of localized conditional convective instability. Also, the majority of updrafts attain a tilted structure that is in accordance with the localized vertical wind shear. Nonetheless, there are striking differences between models, with the higher-resolution WRF simulation producing around twice as many updrafts as RAMS. Moreover, the WRF updrafts are significantly weaker. This paper highlights the utility of SPOUT for studying convective life cycles in high-resolution numerical simulations and provides some basic features of convection in two hurricane simulations.

Populated urban areas along many coastal regions are vulnerable to landfalling tropical cyclones (TCs). To the detriment of surface parameterizations in mesoscale models, the complexities of turbulence at high TC wind speeds in urban canopies are presently poorly understood. Thus, this study explores the impacts of urban morphology on TC-strength winds and boundary layer turbulence in landfalling TCs. To better quantify how urban structures interact with TC winds, large-eddy simulations (LESs) are conducted with the Cloud Model 1 (CM1). This implementation of CM1 includes immersed boundary conditions (IBCs) to represent buildings and eddy recycling to maintain realistic turbulent flow perturbations. Within the IBCs, an idealized coastal city with varying scales is introduced. TC winds impinge perpendicularly to the urbanized coastline. Numerical experiments show that buildings generate distinct, intricate flow patterns that vary significantly as the city structure is varied. Urban IBCs produce much stronger turbulent kinetic energy than is produced by conventional surface parameterizations. Strong effective eddy viscosity due to resolved eddy mixing is displayed in the wake of buildings within the urban canopy, while deep and enhanced effective eddy viscosity is present downstream. Such effects are not seen in a comparison LES using a simple surface parameterization with high roughness values. Wind tunneling effects in streamwise canyons enhance pedestrian-level winds well beyond what is possible without buildings. In the arena of regional mesoscale modeling, this type of LES framework with IBCs can be used to improve parameters in surface and boundary layer schemes to more accurately represent the drag coefficient and the eddy viscosity in landfalling TC boundary layers. Significance Statement: This is among the first large-eddy simulation model studies to examine the impacts of tropical cyclone–like winds around explicitly resolved buildings. This work is a step forward in bridging the gap between engineering studies that use computational fluid dynamics models or laboratory experiments for flow through cities and mesoscale model simulations of landfalling tropical cyclones that use surface parameterizations specialized for urban land use.

A minimal modeling system for understanding tropical cyclone intensity and wind structure changes is introduced: Shallow Water Axisymmetric Model for Intensity (SWAMI). The forced, balanced, axisymmetric shallow water equations are reduced to a canonical potential vorticity (PV) production and inversion problem, whereby PV is produced through a mass sink (related to the diabatic heating) and inverted through a PV/absolute–angular–momentum invertibility principle. Because the invertibility principle is nonlinear, a Newton–Krylov method is used to iteratively obtain a numerical solution to the discrete problem. Two versions of the model are described: a physical radius version which neglects radial PV advection (SWAMI-r) and a potential radius version that naturally includes the advection in the quasi-Lagrangian coordinate (SWAMI-R). In idealized numerical simulations, SWAMI-R produces a thinner and more intense PV ring than SWAMI-r, demonstrating the role of axisymmetric radial PV advection in eyewall evolution. SWAMI-R always has lower intensification rates than SWAMI-r because the reduction in PV footprint effect dominates the peak magnitude increase effect. SWAMI-r is next demonstrated as a potentially useful short-term wind structure forecasting tool using the newly added FLIGHT+ Dataset azimuthal means for initialization and forcing on three example cases: a slowly intensifying event, a rapid intensification event, and a secondary wind maximum formation event. Then, SWAMI-r is evaluated using 63 intensifying cases. Even though the model is minimal, it is shown to have some skill in short-term intensity prediction, highlighting the known critical roles of the relationship between the radial structures of the vortex inertial stability and diabatic heating rate. Because of the simplicity of the models, SWAMI simulations are completed in seconds. Therefore, they may be of some use for hurricane nowcasting to short-term (less than 24 h) intensity and structure forecasting. Due to its favorable assumptions for tropical cyclone intensification, a potential use of SWAMI is a reasonable short-term upper-bound intensity forecast if the storm intensifies.

Accurate, reliable estimates of tropical cyclone (TC) intensity are a crucial element in the warning and forecast process worldwide, and for the better part of 50 years, estimates made from geostationary satellite observations have been indispensable to forecasters for this purpose. One such method, the Advanced Dvorak Technique (ADT), was used to develop analog ensemble (AnEn) techniques that provide more precise estimates of TC intensity with instant access to information on the reliability of the estimate. The resulting methods, ADT-AnEn and ADT-based Error Analog Ensemble (ADTE-AnEn), were trained and tested using seventeen years of historical ADT intensity estimates using k-fold cross-validation with 10 folds. Using only two predictors, ADT-estimated current intensity (maximum wind speed) and TC center latitude, both AnEn techniques produced significant reductions in mean absolute error and bias for all TC intensity classes in the North Atlantic and for most intensity classes in the Eastern Pacific. The ADTE-AnEn performed better for extreme intensities in both basins (significantly so in the Eastern Pacific) and will be incorporated in the University of Wisconsin’s Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) workflow for further testing during operations in 2021.