Maximum Sustained Wind Research Articles

Satellite based analysis of rapid intensification of Super Cyclone Amphan

Tropical Cyclones (TCs) are formidable natural hazards with destructive impacts on life and property along the tropical belt. TCs are responsible for multiple hazards such as riverine and urban floods, extreme winds, and lightning, storm surge significantly amplifying the threats they pose. Rapid Intensification (RI), defined as a sudden increase in Maximum Sustained Winds (MSWs) by 30 kt or more in 24 h, has been a growing concern. These events, characterized by a swift escalation in MSW are challenging to the forecasters and remain a considerable operational challenge, amplifying risks for coastal communities. Numerical modeling also struggles to predict RI, with limitations in describing inner core convective scale processes cited as a major limitation. Monitoring and assessing TCs heavily rely on satellite-based observations due to the absence of adequate in-situ measurements over the ocean. In the present study, we examine Super Cyclonic Storm (SuCS) Amphan, the first Super Cyclone of this century, that formed over the Bay of Bengal in 2020 using satellite imagery and derived products available in near-real time to monitor and identify the early signatures for the RI. It is observed that the intensification is intricately tied to substantial expansions in both the outer and inner core sizes along with its symmetricity before the RI. Notably, no significant alterations in size are observed between the very severe cyclonic storm (VSCS) and SuCS stages. The convection characteristics of TCs are found to provide crucial markers of their intensification, with a particular emphasis on polar satellite-based passive microwave imagery for analyzing embedded convection and early eye detection. Key observations indicate that inner convective ring patterns, identified through 37Ghz microwave imagery in lower-level convection, act as precursors to rapid intensification. Concurrently, the outer ring pattern observed during the mid-life course is found to permit further intensification of the system.

Spatial Memory of Notable Hurricane Tracks and Their Geophysical Hazards

Previous research has shown that people use a benchmark hurricane as part of their preparation and evacuation decision-making process. While hurricanes are a common occurrence along the Gulf Coast, research on personal memories of past storms is lacking. Particularly, how well do people remember the track and geophysical hazards (wind speed, storm surge, and total rainfall) of past storms? The accurate or inaccurate recollection and perception of previous storm details can influence personal responses to future storms, such as the decision to evacuate or take other life-saving actions. Survey responses of residents in Alabama and Mississippi were studied to determine if people were accurately able to recall a notable storm’s name when seeing an image of the storm’s track. Those who were able to identify the storm by its track were also asked if they could remember the storm’s maximum reported rainfall, maximum sustained winds, and storm surge at landfall. Results showed that there were statistically significant differences between the levels of accurate recall for different storms, with Hurricanes Katrina and Michael having the most correct responses. Regardless of the storm, most people struggled to remember geophysical hazards. The results of this study are important as they can inform broadcast meteorologists and emergency managers on forecast elements of the storm to better emphasize in future communication in comparison to the actual values from historical benchmark storms.

The impact of coupling a dynamic ocean in the Hurricane Analysis and Forecast System

Coupling a three-dimensional ocean circulation model to an atmospheric model can significantly improve forecasting of tropical cyclones (TCs). This is particularly true of forecasts for TC intensity (maximum sustained surface wind and minimum central pressure), but also for structure (e.g., surface wind-field sizes). This study seeks to explore the physical mechanisms by which a dynamic ocean influences TC evolution, using an operational TC model. The authors evaluated impacts of ocean-coupling on TC intensity and structure forecasts from NOAA’s Hurricane Analysis and Forecast System v1.0 B (HFSB), which became operational at the NOAA National Weather Service in 2023. The study compared existing HFSB coupled simulations with simulations using an identical model configuration in which the dynamic ocean coupling was replaced by a simple diurnally varying sea surface temperature model. The authors analyzed TCs of interest from the 2020–2022 Atlantic hurricane seasons, selecting forecast cycles with small coupled track-forecast errors for detailed analysis. The results show the link between the dynamic, coupled ocean response to TCs and coincident TC structural changes directly related to changing intensity and surface wind-field size. These results show the importance of coupling in forecasting slower-moving TCs and those with larger surface wind fields. However, there are unexpected instances where coupling impacts the near-TC atmospheric environment (e.g., mid-level moisture intrusion), ultimately affecting intensity forecasts. These results suggest that, even for more rapidly moving and smaller TCs, the influence of the ocean response to the wind field in the near-TC atmospheric environment is important for TC forecasting. The authors also examined cases where coupling degrades forecast performance. Statistical comparisons of coupled versus uncoupled HFSB further show an interesting tendency: high biases in peak surface winds for the uncoupled forecasts contrast with corresponding low biases, contrary to expectations, in coupled forecasts; the coupled forecasts also show a significant negative bias in the radii of 34 kt winds relative to National Hurricane Center best track estimates. By contrast, coupled forecasts show very small bias in minimum central pressure compared with a strong negative bias in uncoupled. Possible explanations for these discrepancies are discussed. The ultimate goal of this work will be to enable better evaluation and forecast improvement of TC models in future work.

Robust future projections of global spatial distribution of major tropical cyclones and sea level pressure gradients

Despite the profound societal impacts of intense tropical cyclones (TCs), prediction of future changes in their regional occurrence remains challenging owing to climate model limitations and to the infrequent occurrence of such TCs. Here we reveal projected changes in the frequency of major TC occurrence (i.e., maximum sustained wind speed: ≥ 50 m s−1) on the regional scale. Two independent high-resolution climate models projected similar changes in major TC occurrence. Their spatial patterns highlight an increase in the Central Pacific and a reduction in occurrence in the Southern Hemisphere—likely attributable to anthropogenic climate change. Furthermore, this study suggests that major TCs can modify large-scale sea-level pressure fields, potentially leading to the abrupt onset of strong wind speeds even when the storm centers are thousands of kilometers away. This study highlights the amplified risk of storm-related hazards, specifically in the Central Pacific, even when major TCs are far from the populated regions.

A study on radial characteristics of North Indian Ocean tropical cyclones and associated energy indices through numerical modeling

The structural characteristics of North Indian Ocean (NIO) Tropical Cyclones (TCs) are emphasized in this study by adopting Weather Research and Forecasting (WRF) numerical modeling framework. TCs that occurred during 2001–2020, were simulated using WRF model in the CTRL, and scatterometer wind vortices from four satellites were considered for assimilation purpose in the WRFDA experiment. Considering the results from both types of simulations, best-track data sets, and ERA5 global analyses, several aspects including radial parameters, the thermodynamic and dynamic properties, horizontal structure, etc., were analyzed besides developing some robust empirical relationships. In the process, model performance was evaluated too. The study emphasized on sub-basin, sectorial, intensity-based, range-based, and frequency-based analysis for the radial parameters. Also, yearly, and seasonal variations were considered, inter-comparisons were carried out, the inter-relationships with numerous environmental and storm-related characteristics have been explored. The study provides valuable insights into the TC radial characteristics over NIO, and associated thermodynamic and dynamic behavior. It upholds that the relatively greater intensity TCs occurring during October–November months are mostly observed to have genesis over the East Arabian Sea (EAS), Middle Bay of Bengal (MBOB), and North Bay of Bengal (NBOB) sectors. The EAS region exhibits higher values for the energy indices and maximum sustained wind (MSW), compared to the Western Arabian Sea (WAS), while the maximum convective potential values show an opposite trend. The assimilation of scatterometer wind data has a limited impact on model results, although it is more effective over the Arabian Sea (AS) than the Bay of Bengal (BOB). WRFDA could provide a relatively better estimate for most of the TC radial features over both AS and BOB. The extensive analysis of TC radial features facilitated developing several empirical relationships involving radial characteristics, MSW and minimum central pressure, which are valid for NIO region.

Ionospheric responses to the tropical cyclones from different oceanic basins over the globe

The perturbations in the ionosphere due to eight tropical cyclones (TCs), namely Iota, Haima, Harold, Willa, Amphan, Gaja, Vadrah and Bulbul, originated and grown in different oceanic basins, are investigated. Total Electron Content (TEC) data, from Global Positioning System (GPS) TEC receiver in operation at Agartala (AGT) or different International GNSS Service (IGS) stations near the cyclone landfall regions, are used in this study. Despite some differences, the ionosphere responds to all tropical cyclones in an almost similar manner. Though the geomagnetic conditions are quiet and there are no perturbations due to any other geophysical phenomena in the active cyclonic storm stage, in all the cases there is a fall in average vertical total electron content (VTEC) deviations below the monthly mean value either on the landfall day or on the following day or even on just previous day. Decrements in Vertical Total Electron Content are found higher for tropical cyclones over North Indian and South Pacific oceanic basins. Recoveries in vertical total electron content values are slower for cyclones over the North Atlantic and North West Pacific basins. Recoveries in vertical total electron content (VTEC) values are slow for tropical cyclones (TCs) over the North Atlantic and North West Pacific basins. But those over other basins are quick. The longer the track of a tropical cyclone (TC), the higher is the reduction in the vertical total electron content (VTEC) value. A negative correlation exists between the maximum sustained surface wind velocities and the total periods of different TCs and also the difference of lowest average differential VTECs with that on the previous day. The observed anomaly in ionospheric responses might be due to the combined effect of TC-inspired gravity waves, ejection of neutral particles from the terminator of a tropical cyclone (TC) and lightning electric fields. To explain the observed results convective activities during TC, with the help of outgoing long wave radiation (OLR) map, are also taken into account. This study provides the primary results regarding regional characteristics and hence a comparative idea for the responses of the ionosphere to different tropical cyclones (TCs) from different geographical positions on the globe, which needs further comprehensive investigation in future.

Comparative analysis of the variability and impacts of tropical cyclones in flood-prone areas of Zimbabwe

Tropical cyclones (TCs) are extreme meteorological events that cause significant deaths, infrastructure damage, and financial losses around the world. In recent years, the Eastern Highlands of Zimbabwe's have become increasingly vulnerable to TCs caused by Indian Ocean tropical cyclones making landfall more frequently. There is still a limited understanding of the phenomenon and the quantification of its impacts. The aim of this research is to conduct a comparative analysis of the variability in the severity of tropical cyclones by analysing historical storm tracks and mapping the environmental impacts in Zimbabwe's Chimanimani and Chipinge districts. Results indicate that, between 1945 and 2022, the Eastern Highlands of Zimbabwe experienced 5 of the total 865 cyclones in the Southwest Indian Ocean. The maximum sustained winds from the Cyclone Idai in the Eastern Highlands were recorded as 195 km/h. Some of the remote sensing-based indices used to extract spatial information about the condition of vegetation, wetlands, built-up area, and bar land during pre and post cyclonic events included the Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (MNDWI). Analysis of NDVI in the Eastern Highlands revealed that there was a significant decrease in vegetated area because of the cyclone impact, with a decrease of 2.1% and 16.68% for cyclone Japhet and Idai respectively. The MNDWI shows a 10.74% increase in water content after cyclone Eline. Field validation in 2019 confirms the research findings. An Operations Dashboard Disaster Management System was developed in order to disseminate information to the affected stakeholders about the potential risk that the face due to the occurrence of the natural phenomena.

Damage to tropical forests caused by cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics.

Each year, an average of 45 tropical cyclones affect coastal areas and potentially impact forests. The proportion of the most intense cyclones has increased over the past four decades and is predicted to continue to do so. Yet, it remains uncertain how topographical exposure and tree characteristics can mediate the damage caused by increasing wind speed. Here, we compiled empirical data on the damage caused by 11 cyclones occurring over the past 40 years, from 74 forest plots representing tropical regions worldwide, encompassing field data for 22,176 trees and 815 species. We reconstructed the wind structure of those tropical cyclones to estimate the maximum sustained wind speed (MSW) and wind direction at the studied plots. Then, we used a causal inference framework combined with Bayesian generalised linear mixed models to understand and quantify the causal effects of MSW, topographical exposure to wind (EXP), tree size (DBH) and species wood density (ρ) on the proportion of damaged trees at the community level, and on the probability of snapping or uprooting at the tree level. The probability of snapping or uprooting at the tree level and, hence, the proportion of damaged trees at the community level, increased with increasing MSW, and with increasing EXP accentuating the damaging effects of cyclones, in particular at higher wind speeds. Higher ρ decreased the probability of snapping and to a lesser extent of uprooting. Larger trees tended to have lower probabilities of snapping but increased probabilities of uprooting. Importantly, the effect of ρ decreasing the probabilities of snapping was more marked for smaller than larger trees and was further accentuated at higher MSW. Our work emphasises how local topography, tree size and species wood density together mediate cyclone damage to tropical forests, facilitating better predictions of the impacts of such disturbances in an increasingly windier world.

UQAM‐TCW: A Global Hybrid Tropical Cyclone Wind Model Based Upon Statistical and Coupled Climate Models

AbstractTropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a significant methodological challenge. It is therefore of high societal value to synthetically simulate TC tracks and winds to assess potential impacts along with their probability distributions for example, land use planning and financial risk management. A common approach to generate TC tracks is to apply storm detection methodologies to climate model output, but such an approach is sensitive to the method and parameterization used and tends to underestimate intense TCs. We present a global TC model (the UQAM‐TCW model thereafter) that melds statistical modeling, to capture historical risk features, with a climate model large ensemble, to generate large samples of physically coherent TC seasons. Integrating statistical and physical methods, the model is probabilistic and consistent with the physics of how TCs develop. The model includes frequency and location of cyclogenesis, full trajectories with maximum sustained winds and the entire wind structure along each track for the six typical cyclogenesis basins from IBTrACS. Being an important driver of TCs globally, we also integrate ENSO effects in key components of the model. The global TC model thus belongs to a recent strand of literature that combines probabilistic and physical approaches to TC track generation. As an application of the model, we show global hazard maps for direct and indirect hits expressed in terms of return periods. The global TC model can be of interest to climate and environmental scientists, economists and financial risk managers.

A study on the future cyclone characteristics and its associated storm surge along the Bangladesh coast

This study has investigated the surge height along the Bangladesh coast due to the changes in future cyclone characteristics. Cyclone characteristics mainly translation speed, landfall angle, maximum sustained wind radius, genesis behavior etc., were investigated with the present and future cyclone data calibration. We have used the d4PDF, AGCM data, and BMD data for calibration. An equation for a vertical shallow water model was developed and used to determine the water level elevation resulting from the modified cyclone information. The cyclone modification information was made by the calibration of present and past cyclone data. It is found from the study that the west part of Bangladesh will face intense cyclones but the East part may face the dangerous cyclone in the future. We have also seen that changes in the characteristics of storms result in a major change of storm surge.

A new surge index with the incorporation of cyclone track approach angle information for the Bay of Bengal

A novel tropical cyclone surge potential index (TCSPI) for estimating tropical cyclone (TC) induced potential peak surge levels near the landfall locations for the entire coast of the Bay of Bengal (BoB) is presented. The proposed TCSPI incorporates parameters that are often readily available in real-time for BoB: maximum sustained wind speed (Vmax), the radius of given wind (R), translation speed (S), approach angle (θ), coastal geometry (a, b), and bathymetry information (L30). The inclusion of approach angle and associated coastal geometry information led to improvements of the TCSPI to other existing surge indices. A retrospective analysis of TCSPI using the Indian Meteorological Department (IMD) and Joint Typhoon Warning Center (JTWC) tropical cyclone (TC) best track data from 1990 to 2021 for BoB suggests that the index captures historical events reasonably well. In particular, it explains up to ∼90% of observed surge variance and up to ∼92% of those peak surge values obtained from physics-based numerical simulation using the Advanced Circulation (ADCIRC) model. Further, we utilized renowned regression-based supervised machine learning (ML) models on small data samples. The Linear Regression (LR), Regression Tree (TR), Support Vector Machine (SVM), Ensembles of Trees (EnTR), and Gaussian Process Regression (GPR) models are incorporated that can rapidly predict peak surge height across an extensive coastal region of BoB, using the same input parameter information (i.e., Vmax, R, S,L30, and θ) that have been considered in the TCSPI at the landfall time frame. Finally, the observed peak surge values associated with historical TC events in the BoB are compared with those estimated by TCSPI, ADCIRC, and ML models. The errors associated with TCSPI are comparatively less. The consideration of approach angle information in the TCSPI has improved the accuracy of the overall prediction of peak surge height associated with historical TCs by up to 17% and 19% for observed and ADCIRC-simulated peak surges, respectively. The development of such a prediction methodology can reduce the numerical computation requirements to improve the surge hazard maps, and hence, proper implementation based on surge risk is possible.

Impact of Climate Change on Maximum Sustained Wind Radius and its Associated Storm Surge Estimation along the Coast of Bangladesh

This research is a basic study on cyclones. The changing behaviour of storm surge in the Bay of Bengal due to the impact of climatic changes has been analyzed in this study. Certain characteristics of cyclones, such as the maximum sustained wind radius, have been analyzed, and their effect due to climatic change has been determined. The correlation between the maximum sustained wind radius and surge height was observed for this purpose. To accurately determine surge height, the vertically integrated shallow water wave model equation was employed, and it was solved using the semi-implicit finite difference method through the Arakawa-C grid. The surge model was performed by increasing and decreasing its wind radius by 10% to 20%, based on changes in the maximum sustained wind radius due to the effects of climate change. A strong conclusion was reached from the obtained results, indicating a significant effect of the maximum sustained wind radius on storm surge. But if it increases, there is a visible change in storm in some area of the coast of Bangladesh. For example, 1% increase in wind radius, the surge height increases by 0.032m, where the storm strikes. In some areas far away from where the storm hits the rising rate of the surge height is much lower. Finally, it may be stated that the surge height is affected by the maximum sustained wind radius and that it is altered by climatic impacts.

A Comparison of the Impacts of Inner-Core, In-Vortex, and Environmental Dropsondes on Tropical Cyclone Forecasts during the 2017–20 Hurricane Seasons

Abstract This study conducts the first large-sample comparison of the impact of dropsondes in the tropical cyclone (TC) inner core, vortex, and environment on NWP-model TC forecasts. We analyze six observing-system experiments, focusing on four sensitivity experiments that denied dropsonde observations within annuli corresponding with natural breakpoints in reconnaissance sampling. These are evaluated against two other experiments detailed in a recent parallel study: one that assimilated and another that denied dropsonde observations. Experiments used a basin-scale, multistorm configuration of the Hurricane Weather Research and Forecasting (HWRF) Model and covered active periods of the 2017–20 North Atlantic hurricane seasons. Analysis focused on forecasts initialized with dropsondes that used mesoscale error covariance derived from a cycled HWRF ensemble, as these forecasts were where dropsondes had the greatest benefits in the parallel study. Some results generally support findings of previous research, while others are novel. Most notable was that removing dropsondes anywhere, particularly from the vortex, substantially degraded forecasts of maximum sustained winds. Removing in-vortex dropsondes also degraded outer-wind-radii forecasts in many instances. As such, in-vortex dropsondes contribute to a majority of the overall impacts of the dropsonde observing system. Additionally, track forecasts of weak TCs benefited more from environmental sampling, while track forecasts of strong TCs benefited more from in-vortex sampling. Finally, inner-core-only sampling strategies should be avoided, supporting a change made to the U.S. Air Force Reserve’s sampling strategy in 2018 that added dropsondes outside of the inner core. Significance Statement This study uses a regional hurricane model to conduct the most comprehensive assessment to date of the impact of dropsondes at different distances away from the center of a tropical cyclone (TC) on TC forecasts. The main finding is that in-vortex dropsondes are most important for intensity and outer-wind-radii forecasts. Particularly notable is the impact of dropsondes on TC maximum wind speed forecasts, as reducing sampling anywhere would degrade those forecasts.

The Effect of Coastline Concavity on Maximum Storm Surge Height along the US Gulf Coast

Storm surge is the most dangerous component of landfalling tropical cyclones (TCs). The growing coastal population highlights the importance of research regarding the atmospheric and geographic factors influencing the maximum storm surge height (MSSH). To date, few studies have investigated the influence of coastline concavity. Here, we investigate the hypothesis that TCs making landfall on a concave coastline will have a higher MSSH than TCs making landfall on a convex coastline. The Colorado State University extended best track dataset includes the radius of 34 kt winds (R34), landfall minimum mean sea level pressure (MSLP), landfall maximum sustained winds, and forward speed of TCs. The storm surge database for the US Gulf Coast provides the location and MSSH for TCs impacting the U.S. Gulf Coast. From this, eleven TCs that meet specific criteria and represent the larger population of Atlantic TCs are selected. The adjusted degree of coastline concavity (ADoC) is calculated for each TC using the law of cosines and 50, 100, and 200 km radius buffers around the point of MSSH. A Mann Whitney U test does not indicate any significant differences between the mean MSSH of TCs making landfall on each coastline type. Additionally, results from a simple linear regression F-test suggest that none of the included parameters have a significant influence on MSSH despite the findings of previous research. Still, the Spearman’s Rho correlation values suggest a weak positive relationship between the ADoC and MSSH. This relationship is significant at the 100 and 200 km buffers, which is consistent with the hypothesis. Results are limited by the small sample size. Future research should use a larger dataset and investigate how each individual storm characteristic affects MSSH. KEYWORDS: Tropical Cyclones; Hurricanes; Storm Surge; Coastal Geography; Coastline Concavity; Gulf of Mexico; Law of Cosines

Performance of land surface schemes on simulation of land falling tropical cyclones over Bay of Bengal using ARW model

The present study encompasses the performance of Land Surface Model (LSM) physics on simulation of Tropical Cyclones (TCs) key characteristics - track, mean sea level pressure (MSLP), maximum sustained wind (MSW) and rainfall. The impact of four LSM schemes - Thermal Diffusion, Noah, RUC and Noah-MP, is evaluated for the simulation of Severe Cyclonic Storm (SCS) ‘Vardah’ that crossed Tamil Nadu coast, near Chennai on 12 December, 2016 and Extremely Severe Cyclonic Storms (ESCS) ‘Fani’ that crossed Odisha coast, close to Puri on 03 May, 2019. For this purpose, the Advanced Weather Research and Forecasting (ARW) model, configured with a single domain of 9 km horizontal resolution covering the Bay of Bengalis considered. The initial and lateral boundary conditions to the model integration are taken from National Centers for Environmental Prediction (NCEP) Final Analysis (FNL). The model simulated track is verified with India Meteorological Department (IMD) observed track for both the cases. The model simulated MSW and MSLP at the landfall location is validated with IMD best estimation along with fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-analysis (ERA5) products. The rainfall associated with both the cyclones are compared with ERA5 and Global Precipitation Measurement (GPM) rainfall for its validation. The track of TCs Vardah and Fani are well simulated with all the four land surface schemes with reasonable accuracy in landfall position and time of landfall of the systems. The Along Track Error (ATE) and Cross Track Error (CTE) are minimal for the unified Noah LSM scheme. The landfall position error (about 2 km only) is significantly improved with the unified Noah scheme. In case of rainfall forecast, LSMs tend to overestimate the rainfall during landfall of both systems. It is also noticed that overestimation is more towards inland than on the coast. Out of all four LSMs, rainfall estimation from the RUC is closest to the GPM and ERA5 rainfall estimates during landfall. In addition to this, RUC scheme intensifies the cyclones in terms of MSLP and MSW during the landfall of the system as compared to the other parameterization schemes.

Evaluation of Experimental High-Resolution Model Forecasts of Tropical Cyclone Precipitation Using Object-Based Metrics

Abstract Tropical cyclone (TC) precipitation poses serious hazards including freshwater flooding. High-resolution hurricane models predict the location and intensity of TC rainfall, which can influence local evacuation and preparedness policies. This study evaluates 0–72-h precipitation forecasts from two experimental models, the Hurricane Analysis and Forecast System (HAFS) model and the basin-scale Hurricane Weather Research and Forecasting (HWRF-B) Model, for 2020 North Atlantic landfalling TCs. We use an object-based method that quantifies the shape and size of the forecast and observed precipitation. Precipitation objects are then compared for light, moderate, and heavy precipitation using spatial metrics (e.g., area, perimeter, elongation). Results show that both models forecast precipitation that is too connected, too close to the TC center, and too enclosed around the TC center. Collectively, these spatial biases suggest that the model forecasts are too intense even though there is a negative intensity bias for both models, indicating there may be an inconsistency between the precipitation configuration and the maximum sustained winds in the model forecasts. The HAFS model struggles with forecasting stratiform versus convective precipitation and with the representation of lighter (stratiform) precipitation during the first 6 h after initialization. No such spinup issues are seen in the HWRF-B forecasts, which instead exhibit systematic biases at all lead times and systematic issues across all rain-rate thresholds. Future work will investigate spinup issues in the HAFS model forecast and how the microphysics parameterization affects the representation of precipitation in both models.

IDENTIFICATION OF POTENTIAL STORM SURGES DUE TO TYPHOON RAI USING NUMERICAL MODELS

In December 2021, Typhoon Rai (local name: Odette) hit the Visayas region of the Philippines. It had a minimum sea level pressure (MSLP) of 915 hectopascals (hPa) and a maximum sustained wind speed of 54.02 m/s and was classified as a violent typhoon by the Japan Meteorological Agency. Rai caused massive damage to infrastructure and housing (NDRRMC 2022). Rai passed through the vulnerable islands of Bohol and Cebu, where high storm surge heights could be expected from strong typhoons (Lapidez et al., 2015). Field surveys were conducted right after a typhoon event to investigate the condition of the affected areas. However, Rai hit during the pandemic when travel restrictions were effective in the country. Thus, it was difficult to identify the areas hit by potential storm surges immediately. Satellite imagery is one method; however, it is generally difficult to identify if the area was affected by storm surges using images. Numerical modeling of the typhoon event over select areas is one way to solve the problem of disaster information shortage. In this study, Rai is simulated using weather and storm models over the provinces of Bohol and Cebu.

SURVEY OF STORM SURGE DUE TO TYPHOON RAI IN DECEMBER 2021 IN THE PHILIPPINES

Typhoon Rai, the last typhoon in the Western Pacific basin in 2021, entered the Philippine Area of Responsibility (PAR) on the 14th December at 23:00 Philippine Standard Time with a minimum central pressure of 985 hPa and maximum sustained winds of 100 km/h (NDRRMC 2022). The typhoon made its first of nine landfalls at Siargao Island in Surigao del Norte on the 16th December 2021 13:30 PST and continued to bring strong winds and rainfall to the Visayas region. The storm was one of the strongest typhoons to have hit the Philippines in recent times, and the government declared a State of Calamity across 493 cities and municipalities (NDRRMC 2022). The typhoon damaged more than 1.1 million houses in the Central Visayas region, and caused a total of 220 total reported deaths and 546 injuries. In the present work, the authors surveyed the storm surge heights in a variety of coastal areas of Cebu and Bohol islands.

Global short-term mortality risk and burden associated with tropical cyclones from 1980 to 2019: a multi-country time-series study

The global spatiotemporal pattern of mortality risk and burden attributable to tropical cyclones is unclear. We aimed to evaluate the global short-term mortality risk and burden associated with tropical cyclones from 1980 to 2019. The wind speed associated with cyclones from 1980 to 2019 was estimated globally through a parametric wind field model at a grid resolution of 0·5° × 0·5°. A total of 341 locations with daily mortality and temperature data from 14 countries that experienced at least one tropical cyclone day (a day with maximum sustained wind speed associated with cyclones ≥17·5 m/s) during the study period were included. A conditional quasi-Poisson regression with distributed lag non-linear model was applied to assess the tropical cyclone-mortality association. A meta-regression model was fitted to evaluate potential contributing factors and estimate grid cell-specific tropical cyclone effects. Tropical cyclone exposure was associated with an overall 6% (95% CI 4-8) increase in mortality in the first 2 weeks following exposure. Globally, an estimate of 97 430 excess deaths (95% empirical CI [eCI] 71 651-126 438) per decade were observed over the 2 weeks following exposure to tropical cyclones, accounting for 20·7 (95% eCI 15·2-26·9) excess deaths per 100 000 residents (excess death rate) and 3·3 (95% eCI 2·4-4·3) excess deaths per 1000 deaths (excess death ratio) over 1980-2019. The mortality burden exhibited substantial temporal and spatial variation. East Asia and south Asia had the highest number of excess deaths during 1980-2019: 28 744 (95% eCI 16 863-42 188) and 27 267 (21 157-34 058) excess deaths per decade, respectively. In contrast, the regions with the highest excess death ratios and rates were southeast Asia and Latin America and the Caribbean. From 1980-99 to 2000-19, marked increases in tropical cyclone-related excess death numbers were observed globally, especially for Latin America and the Caribbean and south Asia. Grid cell-level and country-level results revealed further heterogeneous spatiotemporal patterns such as the high and increasing tropical cyclone-related mortality burden in Caribbean countries or regions. Globally, short-term exposure to tropical cyclones was associated with a significant mortality burden, with highly heterogeneous spatiotemporal patterns. In-depth exploration of tropical cyclone epidemiology for those countries and regions estimated to have the highest and increasing tropical cyclone-related mortality burdens is urgently needed to help inform the development of targeted actions against the increasing adverse health impacts of tropical cyclones under a changing climate. Australian Research Council and Australian National Health and Medical Research Council.

Accumulated Cyclone Energy-Based Tropical Cyclone Return Periods in Florida

This article introduces an accumulated cyclone energy (ACE) approach for estimating the return period of tropical cyclone (TC) wind risk in Florida. As opposed to calculating return periods directly from maximum sustained wind speed, the ACE-based approach also describes the duration of the strong winds, giving an additional dimension to the assessment of TC wind risks. Because Florida is a peninsula, TCs can move across the state within six hours of landfall, causing an underestimation of the inland wind footprint if only the six-hour reanalysis track points are employed as an input data source. This study uses four different scenarios and an inland exponential decay function to interpolate the wind speed between the six-hour reanalysis track points to feed the ACE-based return period calculation based on a 121-year record from 1900 to 2020. South Florida has the shortest return period (five to ten years) of TCs with an ACE equivalent to one hour of hurricane intensity (≥ 64 kt; 1 kt ∼ 0.51 m s−1) caused by intense historical hurricane strikes, and Polk County in inland central Florida has an equal return period due to frequent and long-duration TC occurrences, acting as an intersection for landfalling TCs in the Florida peninsula.