Hurricane Simulation Research Articles

Unveiling the Pivotal Influence of Sea Spray Heat Fluxes on Hurricane Rapid Intensification

Abstract Predicting rapid hurricane intensification remains a challenge, partially due to neglected factors like sea spray-mediated heat flux. To shed light on the specific roles of spray-mediated sensible and latent heat fluxes, we conducted sensitivity experiments with heat flux parameterizations. Our results demonstrate that the inclusion and variation of the spray-mediated sensible heat significantly reduce model errors when compared against dropsonde data. These findings uniquely quantify the pivotal role of spray-mediated sensible heat flux in accurately predicting hurricane rapid intensification compared to previous studies. Without sea spray processes, ocean-coupled model simulations could not reproduce the steep intensification rate observed in multi-case studies of four high-impact hurricanes. This study also highlights that dropsonde data, as well as directly observed flux, is useful in minimizing uncertainty in the flux parameterization used for hurricane simulations. In this paper, we show how spray-mediated heat flux affects hurricane energetics through turbulent heat exchange and subsequent humid air inflow through primary and secondary circulations. Our findings provide new insights into the transformative role of sea spray in turbulent heat exchange that drives rapid hurricane intensification.

Improving Hurricane Intensity and Streamflow Forecasts in Coupled Hydrometeorological Simulations by Analyzing Precipitation and Boundary Layer Schemes

Abstract Hurricanes have been the most destructive and expensive hydrometeorological event in U.S. history, causing catastrophic winds and floods. Hurricane dynamics can significantly impact the amount and spatial extent of storm precipitation. However, the complex interactions of hurricane intensity and precipitation and the impacts of improving hurricane dynamics on streamflow forecasts are not well established yet. This paper addresses these gaps by comprehensively characterizing the role of vertical diffusion in improving hurricane intensity and streamflow forecasts under different planetary boundary layer, microphysics, and cumulus parameterizations. To this end, the Weather Research and Forecasting (WRF) atmospheric model is coupled with the WRF hydrological (WRF-Hydro) model to simulate four major hurricanes landfalling in three hurricane-prone regions in the United States. First, a stepwise calibration is carried out in WRF-Hydro, which remarkably reduces streamflow forecast errors compared to the U.S. Geological Survey (USGS) gauges. Then, 60 coupled hydrometeorological simulations were conducted to evaluate the performance of current weather parameterizations. All schemes were shown to underestimate the observed intensity of the considered major hurricanes since their diffusion is overdissipative for hurricane flow simulations. By reducing the vertical diffusion, hurricane intensity forecasts were improved by ∼39.5% on average compared to the default models. These intensified hurricanes generated more intense and localized precipitation forcing. This enhancement in intensity led to ∼16% and ∼34% improvements in hurricane streamflow bias and correlation forecasts, respectively. The research underscores the role of improved hurricane dynamics in enhancing flood predictions and provides new insights into the impacts of vertical diffusion on hurricane intensity and streamflow forecasts. Significance Statement Despite significant recent improvements, numerical weather prediction models struggle to accurately forecast hurricane intensity and track due to many reasons such as inaccurate physical parameterization for hurricane flows. Furthermore, the performance of existing physics schemes is not well studied for hurricane flood forecasting. This study bridges these knowledge gaps by extensively evaluating different physical parameterizations for hurricane track, intensity, and flood forecasts using an atmospheric model coupled with a hydrological model. Then, a reduced diffusion boundary layer scheme is developed, making remarkable improvements in hurricane intensity forecasts due to the overdissipative nature of the considered schemes for major hurricane simulations. This reduced diffusion model is shown to significantly enhance hurricane flood forecasts, indicating the significance of hurricane dynamics on its induced precipitation.

A Hurricane Initialization Scheme with 4DEnVAR Satellite Ozone and Bogus Data Assimilation (SOBDA) and Its Application: Case Study

The aim of this study is to joint assimilate the ozone product from the satellite Atmospheric Infrared Sounder (AIRS) and bogus data using the four-dimensional ensemble-variational (4DEnVar) method, and demonstrate the potential benefits of this initialization technique in improving hurricane forecasting through a case study. Firstly, the quality control scheme is employed to enhance the ozone product quality from the satellite AIRS; a bogus sea level pressure (SLP) at the hurricane center is constructed simultaneously based on Fujita’s mathematical model for subsequent assimilation. Secondly, a 4DEnVar satellite ozone and bogus data assimilation (SOBDA) model is established, incorporating an observation operator of satellite ozone that utilizes the relationship between satellite ozone and potential vorticity (PV) from the lower level of 400 hPa to the upper level of 50 hPa. Finally, several comparative experiments are performed to assess the influence of assimilating satellite ozone and/or bogus data, the 4DEnVAR method and four-dimensional variational (4D-Var) method, and ensemble size on hurricane prediction. It is found that assimilating satellite ozone and bogus data with the 4DEnVar method concurrently brings about significant alterations to the initial conditions (ICs) of the hurricane vortex, resulting in a more homogeneous and deeper vortex with a larger, warmer, and more humid core as opposed to assimilating only one type of data. As the duration of integration increases, the initial perturbations in the upper levels gradually propagate downwards, giving rise to significant disparities in the hurricane prediction when satellite ozone and/or bogus information is incorporated. The results demonstrate that utilizing the 4DEnVar approach to assimilate both satellite ozone and bogus data leads to the maximum enhancement in reducing track error and central SLP error of hurricane simulation throughout the entire 72 h forecasting period, compared to assimilating a single dataset. Furthermore, comparative experiments have indicated that the performance of 4DEnVar SOBDA in hurricane forecasting is influenced by the ensemble size. Generally, selecting an appropriate number of ensemble members can not only effectively improve the accuracy of hurricane prediction but can also significantly reduce the demand for computational resources relative to the 4D-Var method. This study can also serve as an advantageous technical reference for numerical applications of ozone products from other satellites and hurricane initialization.

A data-driven physics-informed stochastic framework for hurricane-induced risk estimation of transmission tower-line systems under a changing climate

Hurricanes are one of the most devastating natural hazards which result in significant damage to civil infrastructure. The transmission tower-line system is specifically highly vulnerable to hurricane effects. Recent studies have indicated that the collapse of the transmission tower-line system during hurricane events is mainly due to the joint occurrence of wind and rain. Therefore, it is important to assess the failure probabilities of these structures under wind and rain-induced loads. With climate change, those probabilities are expected to significantly change where the brunt of the damage and economic losses would be disproportionately borne by coastal communities. This study proposes a hybrid risk-responsive, data-driven and physics-informed framework which evaluates the hurricane-induced damage to transmission lines in vulnerable regions and communities under several climate scenarios. The framework is founded upon an advanced physics informed hurricane simulation model followed by a Gaussian kernel density technique to efficiently estimate the intensity measures and quantify the associated uncertainties accurately. The generated hazard intensities are coupled with fragility surfaces expressed in terms of wind speed and rain intensity. Then, the failure probabilities are obtained for the transmission tower-line system under the joint excitation and future climate scenario. The framework is applied to several coastal cities along the US east coast. It is shown that, due to the involved system nonlinearity, the obtained failure probabilities change drastically with the future climate scenario. In addition, rainfall-induced loads which significantly impact the failure probabilities of the transmission tower-line systems are not only function of the selected sites, but also depend on the considered limit state and climate scenario. Therefore, their consideration is important for the structural design and estimation of the system performance.

Fault chain risk expectation-based resilient transmission hardening planning against hurricane

Hardening planning is an effective way to enhance the ability of the transmission system against hurricane attacks and further improve the resilience of power systems. However, the existing hardening method gives little consideration to the risk of multiple failures under hurricanes which may underestimate the real post-disaster damage. To fill the research gap, this paper proposes a resilience index, i.e. fault chain risk expectation (FCRE), which can measure the risk of multiple failures under hurricanes. Then the FCRE-based resilient transmission hardening planning (RTHP) model is proposed to minimize hardening and load shedding penalty costs under different hurricane scenarios. In this paper, hurricane simulation is finished by critical parameters firstly, then the failure probability curve is adopted to obtain the spatial–temporal failure probability of different components, and finally, the corresponding contingency scenarios under simulated hurricanes are obtained by Monte Carlo Simulation (MCS) and k-means clustering algorithms. Meanwhile, the set of fault chains is constructed to calculate the FCRE for each line, and the corresponding constraint is formed to ensure that the system resilience reaches a target level. The proposed model is verified on the modified RTS 24-bus system and the results demonstrate the effectiveness of the proposed hardening planning method.

Hurricane Simulation and Nonstationary Extremal Analysis for a Changing Climate

Abstract Particularly important to hurricane risk assessment for coastal regions is finding accurate approximations of return probabilities of maximum wind speeds. Since extremes in maximum wind speed have a direct relationship with minima in the central pressure, accurate wind speed return estimates rely heavily on proper modeling of the central pressure minima. Using the HURDAT2 database, we show that the central pressure minima of hurricane events can be appropriately modeled by a nonstationary extreme value distribution. We also provide and validate a Poisson distribution with a nonstationary rate parameter to model returns of hurricane events. Using our nonstationary models and numerical simulation techniques from established literature, we perform a simulation study to model returns of maximum wind speeds of hurricane events along the North Atlantic coast. We show that our revised model agrees with current data and results in an expectation of higher maximum wind speeds for all regions along the coast, with the highest maximum wind speeds occurring along the northeast seaboard.

The basic wind characteristics of idealized hurricanes of different intensity levels

In the current study, we conducted four idealized hurricane simulation cases to investigate the basic wind characteristics of hurricanes at different sea surface temperatures. First, the relationship of the minimum of sea surface pressure and the maximum of time-averaged wind speed shows good agreement with observational data and theoretical considerations. Then, the tangential, radial, and vertical wind field are analyzed, both the value and the height of the maximum tangential wind increase with increasing sea surface temperature. The peak value and the height of radial inflow also increase with sea surface temperature increases. An important feature is for the depth of the inflow layer, which always increases for weak hurricanes and beginning increases then decreases for intense hurricanes locates from hurricane eye to three times radius of maximum wind, this can also be seen in hurricane observation data. More importantly, this is the first study that found it begins to increase then becomes steady for medium-strength hurricanes. Furthermore, extreme updrafts are found for intense hurricanes, while not for weak and medium-strength hurricanes, and this also shows good agreement between the observational and simulation data, which is related to coherent subkilometer-scale vortices. Also, the average surface inflow angle is −22.1°, similar to −22.6° ± 2.2° and −23° that are obtained in other studies, and the distribution of surface inflow angle displays good agreement with previous studies. The relationship of tangential velocity and radial velocity is similar to the shape of a tilted infinity symbol. Finally, by analyzing the fluctuating wind characteristics, a 200 m grid size is sufficient except for the weak hurricane, which requires a 62 m grid size for turbulence structures. From the relationship of mean and fluctuating wind characteristics, the hurricane simulation results of different intensity levels converged for 185 m and 62 m grid size.

Simulated hurricane-induced changes in light and nutrient regimes change seedling performance in Everglades forest-dominant species.

Wind damage from cyclones can devastate the forest canopy, altering environmental conditions in the understory that affect seedling growth and plant community regeneration. To investigate the impact of hurricane‐induced increases in light and soil nutrients as a result of canopy defoliation, we conducted a two‐way factorial light and nutrient manipulation in a shadehouse experiment. We measured seedling growth of the dominant canopy species in the four Everglades forest communities: pine rocklands (Pinus elliottii var densa), cypress domes (Taxodium distichum), hardwood hammocks, and tree islands (Quercus virginiana and Bursera simaruba). Light levels were full sun and 50% shade, and nutrient levels coupled with an additional set of individuals that were subjected to a treatment mimicking the sudden effects of canopy opening from hurricane‐induced defoliation and the corresponding nutrient pulse. Seedlings were measured weekly for height growth and photosynthesis, with seedlings being harvested after 16 weeks for biomass, leaf area, and leaf tissue N and 13C isotope ratio. Growth rates and biomass accumulation responded more to differences in soil nutrients than differences in light availability, with largest individuals being in the high nutrient treatments. For B. simaruba and P. elliottii, the highest photosynthetic rates occurred in the high light, high nutrient treatment, while T. distichum and Q. virginiana photosynthetic rates were highest in low light, high nutrient treatment. Tissue biomass allocation patterns remained similar across treatments, except for Q. virginiana, which altered above‐ and belowground biomass allocation to increase capture of limiting soil and light resources. In response to the hurricane simulation treatment, height growth increased rapidly for Q. virginiana and B. simaruba, with nonsignificant increases for the other two species. We show here that ultimately, hurricane‐adapted, tropical species may be more likely to recolonize the forest canopy following a large‐scale hurricane disturbance.

Framework for probabilistic simulation of power transmission network performance under hurricanes

Structures in a power transmission network, such as towers and conductors, are vulnerable to hurricanes. Failures of these structures trip transmission lines and usually result in large scale power outages in the region. This paper presents a technique for the probabilistic simulation of power transmission systems under hurricane events and provides fundamental insights on the modeling and quantification of power system performance and resilience. The study models the power transmission system as a network of connected individual components, which are subjected to wind-induced mechanical failure and power flow constraints. A realistic power transmission network is developed for the study region. The geographical data are obtained for all components in the network based on a data collection and image processing campaign to reflect the realistic properties of the network serving the Lehigh Valley, PA. A hurricane simulator is utilized to generate a hurricane scenario providing time-varying wind intensities and wind directions for the component failure analysis. The spatio-temporal impact of the hurricane is investigated: a pool of component fragilities is generated to effectively incorporate the uncertainties in structural capacities into the analysis; the spatial correlation among structures is modeled efficiently by a random field based technique. At the system level, Monte Carlo simulation is adopted to determine the failure probability of transmission lines. The unmet demand of the system is computed probabilistically, based on the alternate current optimal power flow analysis, capacity constraints and load shedding process of the system. The simulation results can be used to quantify and visualize the power network performance, and help decision makers to identify critical components in the network to optimize the short-term pre-event preparation for an approaching hurricane and long-term retrofit strategy to enhance system resilience.

МНОГОСЕНСОРНАЯ БЕСПРОВОДНАЯ СЕТЕВАЯ СИСТЕМА ДЛЯ МОНИТОРИНГА УРАГАНОВ

A wireless sensor system is described. Pressure sensor measurements are compared with National Weather Service data and wind tunnel test data. It then describes a minivan highway test that was used to evaluate the effect of sensor housing shape on pressure measurements. Computational Fluid Dynamics (CFD) analysis was also performed to complement the wind tunnel and road van tests, as well as to determine optimal mesh shapes and sizes, boundary conditions, and the best turbulence model that would reproduce the measured pressures. (UV) The following describes the Hurricane Simulator tests that were used to evaluate the effect of wind gusts on pressure and automatic/cross-correlations of pressure and velocity between different sensors. A CFD simulation of the UF Hurricane Simulator test was also performed to evaluate the sensitivity of turbulence models in capturing true pressure and velocity changes on the test site due to gusts. Vibration testing using a shaker table to analyze the effect of structural vibration on sensor pressure measurements is described.

Projecting the effects of a warming climate on the hurricane hazard and insured losses: Methodology and case study

This paper summarizes research (conducted previously by the author) to incorporate (1) sea-surface temperature projections, (2) event-based hurricane simulation models, and (3) loss models to assess the impacts of the changing climate on the hurricane hazard and estimated insured losses. The focus of the work is on the US east coast and hence the formation and movement of hurricanes over the Atlantic Basin. As an illustrative case, the area around Charleston, South Carolina is considered. Specifically, this paper presents a methodology to probabilistically estimate regional hurricane loss considering both hurricane intensity and size as well as projected sea surface temperature change as a function of climate change.It is most common to characterize the hurricane wind hazard using a measure of intensity only. Recent studies have demonstrated the importance of accounting for storm size when characterizing the hurricane hazard in order to properly estimate/predict the spatial extent of damage. For purposes of regional loss estimation, therefore, considering maximum wind speed only may not be adequate. A storm with larger size but lower intensity might result in more damage and hence greater total loss than a smaller, more intense storm. Here, the maximum wind speed (intensity) and the radius of maximum winds (spatial extent) are selected as the two dominant indicators (parameters) characterizing the hurricane (event) hazard. The resulting bivariate hazard model is then used as input to a regional loss estimation model to illustrate its potential application. Only hurricane wind damage (and associated losses from both wind damage and water intrusion) is considered in this study. Collateral damage due to flood or storm surge is not addressed here.Simulated events impacting this region are extracted from a synthetic hurricane database (developed by the author and his colleagues) and the joint distribution of intensity and size is established to characterize the hurricane hazard. Regional loss for each simulated hurricane event is then estimated using the loss estimation module in HAZUS_MH. Finally, distributions of regional loss at different (event) hazard levels are generated.A future climate scenario (increasing sea surface temperature) is next considered and the joint distribution of hurricane intensity and size and the resulting loss distribution curve is recomputed. The results are then able to be compared to those under the current climate scenario. Such comparisons can help to inform codes and standards committees, for example, as they consider whether and how to take potential climate change impacts (in the design lifetime) into account. They also can inform ongoing public discourse around mitigation and adaptation, whether hardening of buildings and other infrastructure systems, community resilience planning, construction practices, or related policies.

Comparison of Holland’s Model and Near-Real-Time Predicting System

Hurricanes have affected many tropical and coastal countries for years, but the history of hurricane simulation is short, due to the unpredictable nature of hurricanes. Although several models and methods developed by numerous scientists in the past have substantially enhanced people’s understanding of hurricanes’ formations and features, not all of the errors have been eliminated. To display the improvements of hurricane simulation in the past decades, in this paper, both the advantages and limitations of Holland’s wind model and a new near-real-time prediction model are compared by presenting their key calculations and main ideas, respectively. The modern near-real-time predicting system turns out to be more extensive and exact than the Holland’s wind model. Besides, the idea of the Monte Carlo simulation approach, an accepted hurricane simulation method, is briefly stated for the sake of complete understanding.

Large-scale hurricane modeling using domain decomposition parallelization and implicit scheme implemented in WAVEWATCH III wave model

WAVEWATCH III has been equipped with a new parallelization algorithm, domain decomposition and an optional implicit numerical scheme for coastal application at high spatial resolution with triangular unstructured grids, compatible with community-based coupling infrastructure. We performed a validation study for Hurricane Ike (2008) to prove the accuracy of the updated model against satellite altimeter data and buoy observations on various grids, forced by two sophisticated atmospheric models for hurricane simulation, using different solution schemes and parallelization algorithms. The new implementations for triangular grids are computationally efficient and scalable to be run on a large number of computational nodes, which constitutes a major breakthrough in the context of increasing needs for high-resolution nearshore wave modeling, making WAVEWATCH III (WW3) a powerful tool to simulate the sea state in the nearshore at high resolution and study wave-surge interactions in inner shelf regions.

A new stochastic formulation for synthetic hurricane simulation over the North Atlantic Ocean

Hurricanes are among the most destructive weather phenomena in the United States (US) of America. Accurate modeling of hurricane trajectories, as they land ashore and approach the built environment, is important for public safety. This study presents a new stochastic model for the simulation of hurricane trajectories in the North Atlantic Ocean. Trajectory of the hurricane eye is modeled as a two-dimensional partially-correlated Brownian motion with drift term. Hurricane intensity is modeled as a second-order auto-regressive process, accounting for sea surface temperature.The model is validated by comparing the numerically-generated hurricane tracks against historical records, derived from the HurDat database (NOAA, National Oceanic and Atmospheric Administration). Simulated hurricane arrival and landing rates along the US Eastern coastline are also examined to demonstrate model validity. Finally, performance-based wind engineering (PBWE) application is presented by comparing simulated hurricane wind speeds against ASCE 7-16 recommendations.

Spatial–Temporal Reliability and Damage Assessment of Transmission Networks Under Hurricanes

Hurricanes can damage transmission network structure components, e.g., towers and conductors, and threat the system reliability. To quantify the consequence severity of hurricanes on transmission networks, a spatial–temporal reliability and damage assessment method is proposed to evaluate the effect of a specific hurricane. The hurricane can be a simulated, historical, or forecasting one for different application purpose, where the full track hurricane simulation model, historical records, and forecasting information are used for the event construction, respectively. In the component failure analysis, the time-varying failure probability of transmission towers and conductors are modeled as functions of the time-varying hurricane intensity. In the reliability and damage assessment, the Monte Carlo simulation is adopted. The energy not supplied and power outage cost caused by the hurricane event are used as reliability indices, and the asset damage cost is proposed to quantify the damage of transmission networks. The modified IEEE 24-reliability test system and the two-area IEEE reliability test system-1996 affected by a simulated hurricane, respectively, are used in the case study to validate the proposed method. The simulation results show the method can quantify the hurricane impacts, identify the weak network parts, and provide information for pre-event preparedness for forecasting event application.

Assessing the Multiple Impacts of Extreme Hurricanes in Southern New England, USA

The southern New England coast of the United States is particularly vulnerable to land-falling hurricanes because of its east-west orientation. The impact of two major hurricanes on the city of Providence (Rhode Island, USA) during the middle decades of the 20th century spurred the construction of the Fox Point Hurricane Barrier (FPHB) to protect the city from storm surge flooding. Although the Rhode Island/Narragansett Bay area has not experienced a major hurricane for several decades, increased coastal development along with potentially increased hurricane activity associated with climate change motivates an assessment of the impacts of a major hurricane on the region. The ocean/estuary response to an extreme hurricane is simulated using a high-resolution implementation of the ADvanced CIRCulation (ADCIRC) model coupled to the Precipitation-Runoff Modeling System (PRMS). The storm surge response in ADCIRC is first verified with a simulation of a historical hurricane that made landfall in southern New England. The storm surge and the hydrological models are then forced with winds and rainfall from a hypothetical hurricane dubbed “Rhody”, which has many of the characteristics of historical storms that have impacted the region. Rhody makes landfall just west of Narragansett Bay, and after passing north of the Bay, executes a loop to the east and the south before making a second landfall. Results are presented for three versions of Rhody, varying in the maximum wind speed at landfall. The storm surge resulting from the strongest Rhody version (weak Saffir–Simpson category five) during the first landfall exceeds 7 m in height in Providence at the north end of the Bay. This exceeds the height of the FPHB, resulting in flooding in Providence. A simulation including river inflow computed from the runoff model indicates that if the Barrier remains closed and its pumps fail (for example, because of a power outage or equipment failure), severe flooding occurs north of the FPHB due to impoundment of the river inflow. These results show that northern Narragansett Bay could be particularly vulnerable to both storm surge and rainfall-driven flooding, especially if the FPHB suffers a power outage. They also demonstrate that, for wind-driven storm surge alone under present sea level conditions, the FPHB will protect Providence for hurricanes less intense than category five.

Simulation of Wind, Waves, and Currents During Hurricane Sandy for Planned Assessment of Offshore Wind Turbines

The simulation of wind and wave fields for the evaluation of jacket-supported offshore wind turbines during Hurricane Sandy of 2012 is the focus of this study. For realistic load assessment of offshore wind turbines, it is important that coupled wind, wave, and current fields with appropriate spatial resolution are provided throughout the evolution of the hurricane. A numerical model describing the hurricane track and intensity variation with time is used to generate the coupled wind, wave, and current fields. Time series of turbulent wind fields and coupled wave kinematics are generated using the hurricane simulation output, and a detailed procedure is presented. These coupled wind, wave, and current fields form the inputs for the analysis of a 5-MW offshore wind turbine supported on a jacket platform sited in 50 m of water and assumed located in the path of Hurricane Sandy. The turbine’s and support structure’s performance under the simulated loading conditions is the subject of a separate study.

A Preliminary Impact Study of CYGNSS Ocean Surface Wind Speeds on Numerical Simulations of Hurricanes.

The NASA Cyclone Global Navigation Satellite System (CYGNSS) was launched in December 2016, providing an unprecedented opportunity to obtain ocean surface wind speeds including wind estimates over the hurricane inner‐core region. This study demonstrates the influence of assimilating an early version of CYGNSS observations of ocean surface wind speeds on numerical simulations of two notable landfalling hurricanes, Harvey and Irma (2017). A research version of the National Centers for Environmental Prediction operational Hurricane Weather Research and Forecasting model and the Gridpoint Statistical Interpolation‐based hybrid ensemble three‐dimensional variational data assimilation system are used. It is found that the assimilation of CYGNSS data results in improved track, intensity, and structure forecasts for both hurricane cases, especially for the weak phase of a hurricane, implying potential benefits of using such data for future research and operational applications.

GeoSim 2018 workshop report the 1st ACM SIGSPATIAL international workshop on geospatial simulation

Geospatial simulation is a powerful tool for many disciplines including urban analytics, social science and meteorology to understand, explain, and predict overly complex natural and human-made systems. For instance, hurricane simulation has been used to predict hurricane paths and to make a decisions such as evacuation plans. Traffic simulation have enabled us to forecast traffic congestion in cities around the world.

SFMR Surface Wind Undersampling over the Tropical Cyclone Life Cycle

Surface wind speeds in tropical cyclones are important for defining current intensity and intensification. Traditionally, airborne observations provide the best information about the surface wind speeds, with the Stepped Frequency Microwave Radiometer (SFMR) providing a key role in obtaining such data. However, the flight patterns conducted by hurricane hunter aircraft are limited in their azimuthal coverage of the surface wind field, resulting in an undersampling of the wind field and consequent underestimation of the peak 10-m wind speed. A previous study provided quantitative estimates of the average underestimate for a very strong hurricane. However, no broader guidance on applying a correction based on undersampling has been presented in detail. To accomplish this task, a modified observing system simulation experiment with five hurricane simulations is used to perform a statistical evaluation of the peak wind speed underestimate over different stages of the tropical cyclone life cycle. Analysis of numerous simulated flights highlights prominent relationships between wind speed undersampling and storm size, where size is defined by the radius of maximum wind speed (RMW). For example, an intense hurricane with small RMW needs negligible correction, while a large-RMW tropical storm requires a 16%–19% change. A lookup table of undersampling correction factors as a function of peak SFMR wind speed and RMW is provided to assist the tropical cyclone operations community. Implications for hurricane best track intensity estimates are also discussed using real data from past Atlantic hurricane seasons.