Saffir-Simpson Research Articles

Upper Pleistocene and Holocene Storm Deposits Eroded from the Granodiorite Coast on Isla San Diego (Baja California Sur, Mexico)

This project examines the role of hurricane-strength events likely to have exceeded 119 km/h in wind speed that entered the Gulf of California from the open Pacific Ocean during Late Pleistocene and Holocene times to impact the granodiorite shoreline on Isla San Diego. Conglomerate dominated by large, ellipsoidal to subspherical boulders at the islands south end were canvassed at six stations. A total of 200 individual cobbles and boulders were systematically measured in three dimensions, providing the database for analyses of variations in clast shape and size. The project’s goal was to apply mathematical equations elaborated after Nott (2003) with subsequent refinements to estimate individual wave heights necessary to lift igneous blocks from the joint-bound and exfoliated coast on Isla San Diego. On average, wave heights on the order of 3 m are calculated as having impacted the Late Pleistocene rocky coastline on Isla San Diego during storms, although the largest boulders more than a meter in diameter are estimated to weigh two metric tons and would have required waves in excess of 10 m for extraction. Described for the first time, a fossil marine biota associated with the boulder beds confirms a littoral-to-very-shallow water setting correlated with Marine Isotope Substage 5e approximately 125,000 years ago. A narrow submarine ridge consisting, in part, of loose cobbles and boulders extends for 1.4 km to the southwest from the island’s tip, suggesting that Holocene storms continued to transport rock debris removed from the shore. The historical record of events registered on the Saffir–Simpson Hurricane Wind Scale in the Gulf of California suggests that major storms with the same intensity struck the island in earlier times.

Regionalization of precipitation associated with tropical cyclones using spatial metrics and satellite precipitation

ABSTRACT Precipitation associated with landfalling tropical cyclones (TCs) poses a significant flood risk to vast regions along and inland of the coasts. Quantifying spatial characteristics of tropical cyclone precipitation (TCP) and defining homogeneous rainfall regions can benefit forecasts and hazard mitigation of TCs. This work aims to evaluate the application of spatial metrics and satellite precipitation data in characterizing precipitation associated with landfalling TCs over the North Atlantic. This study applied an object-based Geographic Information System method to measure rainfall fields associated with North Atlantic landfalling TCs from the satellite-based rain rate estimates from 1998–2014. Eleven spatial metrics measuring the entire rainfall field and the largest rainfall polygon within the rain field were evaluated using a set of non-parametric tests to determine if they could distinguish rainfall patterns among four storm intensity categories. A multivariate clustering method with hotspot analysis was utilized to investigate spatial variations and regionalize the rainfall patterns into nine clusters using selected metrics. Five spatial metrics, namely area, solidity, dispersion, closure, and roundness, were selected since they meet three criteria: 1) having a relatively full range of 0– 1 (besides area); 2) having a significant increasing or decreasing trend with intensity, and 3) showing significant differences between any two storm categories. The clustering results indicate that TC rainfall patterns exhibit significant regional variations when storms are over land as well as over sub-basins, including the Caribbean Sea, Gulf of Mexico, and the North Atlantic. The clustering results reveal that intensity is one of the key factors in determining rainfall patterns and reflect the impact of other factors, such as wind shear, moisture content, and interaction with land.

Climatic Variation of Maximum Intensification Rate for Major Tropical Cyclones over the Western North Pacific

To analyze the dependence of intensification rates of tropical cyclones (TCs) on the variation of environmental conditions, an index is proposed here to measure the lifetime maximum intensification rates (LMIRs) for the Saffir–Simpson scale category 4–5 TCs over the western North Pacific. To quantitatively describe the intensification rate of major TCs, the LMIR is defined as the maximum acceleration in the sustained-wind-speed over a 24-h period of an overwater TC. This new index, LMIR, is generally independent of the indices for RI frequency. The results show that the Pacific Decadal Oscillation (PDO) modulates the inter-annual relationship between the LMIR and El Niño/Southern Oscillation (ENSO). The PDO’s modulation on the ENSO’s effect on the LMIR is explored here by considering the relationship between the LMIR and the environmental conditions in different PDO phases. While the ENSO’s effect on the LMIR for the warm PDO phase is generally by affecting the variations of upper ocean heat content, ENSO mainly influences the variations of zonal wind and vertical wind shear for the cold PDO phase. Our results suggest that fast translating TCs tend to attain strong intensification during the warm PDO phase, while a warm subsurface condition may permit slow-translating TCs also to become strongly intensified during the cooling PDO phase. These findings have an important implication for both prediction of RI and the long-term projection of TC activities in the western North Pacific.

Impact of climate change on intense Bay of Bengal tropical cyclones of the post-monsoon season: a pseudo global warming approach

Tropical cyclones (TCs) that make landfall over India’s east coast are responsible for significant loss of life along affected coastlines. TCs forming over the Bay of Bengal (BoB) region in October, November, and December have, in the past, intensified significantly at the time of landfall. The effects of climate change on TCs of different strengths and their characteristics such as track, intensity, precipitation, and convective available potential energy over the BoB region have not been well studied. This study explores the effects of climate change on two TCs of very severe cyclonic storm (VSCS) category (TC Vardah and TC Madi), and two other TCs of extremely severe cyclonic storm (ESCS) category (TC Hudhud and TC Phailin) formed over BoB, both in the short term (2035) and long term (2075). The high-resolution Weather Research and Forecasting (WRF) model is used to simulate the TCs under current and future climate conditions. The simulated TC track and intensity in the current climate agree well with the observations. To explore the impacts of climate change on TCs, the mean climate change signal, computed from future projections of the Community Climate System Model (CCSM4) in different representative concentration pathway (RCP) scenarios, is added to current climate conditions by using the pseudo global warming method. Results show a climate change-related reduction in TC translation speed, deepening of TC core, increased maximum surface wind, and increased precipitation over land in future RCP (4.5, 6.0, and 8.5) scenarios. The TCs in future RCP scenarios are seen to be more intensified compared to current climate simulations. Results demonstrate that all VSCS and ESCS category TCs considered in this study are likely to further intensify to the next higher category level with respect to their current classification, particularly in the far future RCP 6.0 and in the far future RCP 8.5 scenarios. The cyclone damage potential index of TC Vardah, TC Hudhud, and TC Phailin is projected to increase in a future warming climate.

Impact of Microphysical Parameterizations on Simulated Hurricanes—Using Multi-Parameter Satellite Data to Determine the Particle Size Distributions that Produce Most Realistic Storms

Understanding and forecasting hurricanes remains a challenge for the operational and research communities. To accurately predict the Tropical Cyclone (TC) evolution requires properly reflecting the storm’s inner core dynamics by using: (i) high-resolution models; (ii) realistic physical parameterizations. The microphysical processes and their representation in cloud-permitting models are of crucial importance. In particular, the assumed Particle Size Distribution (PSD) functions affect nearly all formulated microphysical processes and are among the most fundamental assumptions in the bulk microphysics schemes. This paper analyzes the impact of the PSD assumptions on simulated hurricanes and their synthetic radiometric signatures. It determines the most realistic, among the available set of assumptions, based on comparison to multi-parameter satellite observations. Here we simulated 2005′s category-5 Hurricane Rita using the cloud-permitting community Weather Research and Forecasting model (WRF) with two different microphysical schemes and with seven different modifications of the parametrized hydrometeor properties within one of the two schemes. We then used instrument simulators to produce satellite-like observations. The study consisted in evaluating the structure of the different simulated storms by comparing, for each storm, the calculated microwave signatures with actual satellite observations made by (a) the passive microwave radiometer that was carried by the Tropical Rainfall Measuring Mission (TRMM) satellite—the TRMM microwave imager TMI, (b) TRMM’s precipitation radar (PR) and (c) the ocean-wind-vector scatterometer carried by the QuikSCAT satellite. The analysis reveals that the different choices of microphysical parameters do produce significantly different microwave signatures, allowing an objective determination of a “best” parameter combination whose resulting signatures are collectively most consistent with the wind and precipitation observations obtained from the satellites. In particular, we find that assuming PSDs with larger number of smaller hydrometeors produces storms that compare best to observations.

The Great Havana Hurricane of 1846: A Reconstruction of the Storm’s Track, Intensity, and Impacts

This article reconstructed the track, intensity, and societal impacts of the Great Havana Hurricane of October 1846, using all available historical data, which include ship logs, newspapers, diaries, and early instrumental records. Most of the data were extracted from original manuscripts at historical libraries and repositories. Meteorological aspects of the hurricane were analyzed by mapping twice-daily surface synoptic weather maps from geographic information systems methods, estimating central pressures from known wind–pressure relationships derived from modern hurricane studies, and assessing intensity based on damage descriptions from the Saffir-Simpson Hurricane Wind Scale and an inland decay model. The storm was found to be much stronger than previously known. In this study, we clearly define the first known record of a landfalling Category 5 hurricane in the Atlantic Basin, which likely ranks among the top of all those known in the modern (1851–present) Atlantic Basin official hurricane database. Although this storm has been previously referred to as one of the most destructive hurricanes in the history of Cuba, the impacts of the Great Havana Hurricane actually spanned well beyond the Caribbean, tracking from Florida through many major populated cities along the East Coast and into Atlantic Canada. Clearly, such a storm today, with much larger metropolitan areas, would have caused enormous economic damage and should be anticipated in long-term hurricane mitigation, zoning, and worst-case scenarios. This study highlights the importance of examining historical documentary sources for extreme events, providing a framework on how to research case studies of premodern Atlantic hurricanes back in time.

Five degrees of randomness

Randomness is omnipresent, and hence the quantification of randomness is a fundamental necessity across the sciences. As “necessity is the mother of invention”, scientists devised various approaches to quantify randomness: statistics uses standard deviation; statistical physics and information theory use entropies (e.g. Shannon); socioeconomics uses inequality indices (e.g. Gini); and ecology uses diversity indices (e.g. Simpson). Alternative to these approaches – which are all continuous quantifications – Mandelbrot suggested a radically different approach: a digital categorization of randomness. Inspired by Mandelbrot, here we showcase a digital categorization comprising five degrees of randomness — á la the Saffir–Simpson hurricane scale, and á la the DEFCON states of defense readiness. Using the reliability-engineering notion of hazard rates, we present a comprehensive study of the digital categorization. From a scholarly viewpoint, we unveil the categorization’s profound connections to Gibbs measures in statistical physics, and to the following probability-theory notions: heavy tails, long tails, slow variation, regular variation, and rapid variation. From an applicative viewpoint, we demonstrate the categorization’s potency and usability. This paper is relevant to wide audiences: theoreticians and practitioners that are tackling random systems and processes.

Adequately reflecting the severity of tropical cyclones using the new Tropical Cyclone Severity Scale

For decades, meteorologists and governments have been warning communities in coastal areas for an imminent tropical cyclone (TC) using the Saffir-Simpson Hurricane Wind Scale (SSHWS). The SSHWS categorizes a TC based on its maximum wind speed, and is used in defining evacuation strategies and humanitarian response. However, the SSHWS considers only the wind hazard of a TC, whereas a TC can also cause severe conditions through its high storm surges and extreme rainfall, triggering coastal and inland flooding. Consequently, the SSHWS fails to mirror the TC’s total severity. This becomes evident when looking at past events such as Hurricane Harvey (2017), which was classified as a Tropical Storm while it caused widespread flooding in the Houston (TX) area, with precipitation totals exceeding 1.5 m. Without including storm surge and rainfall information, adequate risk communication with the SSHWS can be challenging, as the public can (mistakenly) perceive a low-category TC as a low-risk TC. To overcome this, we propose the new Tropical Cyclone Severity Scale (TCSS) that includes all three major TC hazards in its classification. The new scale preserves the categorization as used in the SSHWS, to maintain familiarity amongst the general public. In addition, we extend the scale with a Category 6, to support communication about the most extreme TCs with multiple hazards. The TCSS is designed to be applied on a local-scale, hereby supporting local-scale risk communication efforts and evacuation strategies prior to a TC landfall. The scale can be used for risk communication on both the total TC risk and on the categories of the separate hazards, which can be valuable especially in cases when one hazard is the predominant risk factor, such as excess rainfall triggering flooding.

Puerto Rico plain pigeon, scaly-naped pigeon and red-tailed hawk: population dynamics and association patterns before and after hurricanes

Since the 1980s, 3 major hurricanes have made landfall on Puerto Rico: Hugo in September 1989 (Saffir-Simpson scale, category 4), Georges in September 1998 (category 3) and María in September 2017 (category 4). María was the most devastating hurricane since the 3 major hurricanes that occurred in 1899-�1932. Major hurricanes can cause severe abundance declines and population bottlenecks by decreasing survival and reproductive rates and increasing predation and competition for limited resources. In April to June 1986-2021, we used distance sampling to estimate abundance and monitor the population dynamics of the endangered Puerto Rico plain pigeonPatagioenas inornata wetmoreiand the abundant scaly-naped pigeonP.squamosaand red-tailed hawkButeo jamaicensis. Here, we fit a Bayesian state-space logistic model with distance sampling abundance estimates to generate posterior estimates of maximum population growth rate and population carrying capacity, and predict abundance in April to June 2020-2030. In addition, we usedN-mixture and 2-species models to assess association patterns in April to June 2015-2019. The scaly-naped pigeon and red-tailed hawk populations did not decline, or recovered faster from their declines than the plain pigeon population after the hurricanes. The association patterns between species were positive but variable for the 2 pigeon species and negative but variable for the plain pigeon and red-tailed hawk. At lowered abundance (i.e. mean ± SE estimatesN̂= 1043 ± 476 island-wide andN̂= 522 ± 157 at the centre of abundance in the east-central region in April to June 2018-2021), the plain pigeon may become extinct if another hurricane with the path and intensity of María makes landfall on the island during the current decade.

Estudo do ciclo de vida do ciclone subtropical Arani ocorrido no Atlântico Sul em março de 2011 através das reanálises ERA-Interim e CFSR

O ciclone Arani ocorreu em março de 2011 no oceano Atlântico Sul e foi classificado inicialmente como depressão subtropical. Ao atingir ventos superiores a 34 nós, passou à categoria de tempestade subtropical, sendo nomeado pela Marinha do Brasil como Arani, que significa tempo furioso em Tupi Guarani. O objetivo deste trabalho foi analisar o ciclone Arani através do Espaço de Fase de Ciclones, construído com dados da reanálise ERA-Interim do European Centre for Medium-Range Weather Forecasts (ECMWF), com 0,7º de resolução espacial, e, também, utilizando campos sinóticos e cortes verticais com os dados da reanálise do Climate Forecast System Reanalisys (CFSR), com 0,5º de resolução. Através dos diagramas de fase foi possível identificar a estrutura subtropical do ciclone Arani e o indício de uma transição de fase extratropical no final de seu ciclo de vida. Os resultados indicaram, também, que a qualidade dos diagramas de fase para ciclones com fraco núcleo quente e pequenas dimensões depende de modelos atmosféricos adequados e, por isso, os valores obtidos devem ser analisados com cautela quando se trata do uso de modelos globais. Neste caso, o estudo qualitativo dos diagramas se mostrou mais útil do que a análise puramente quantitativa. Apesar de discordar da informação de uma transição de fase extratropical no final do ciclo de vida do ciclone Arani, contida nos diagramas de fase empregados no estudo, a análise dos campos e dos perfis sinóticos contribuiu para o entendimento do desenvolvimento do ciclone e para a interpretação dos próprios diagramas de fase.

Tropical Cyclones Downscaled from Simulations of the Last Glacial Maximum

AbstractThe tracks, intensities, and other properties of tropical cyclones downscaled from three models’ simulations of the Last Glacial Maximum (LGM) are analyzed and compared to those of storms downscaled from simulations of the present climate. Globally, the mean maximum intensity of storms generated from each model is lower at LGM, as is the fraction of all storms that reach intensities of category 4 or higher on the Saffir–Simpson hurricane wind scale. The median day of the storm season shifts earlier by an average of one week in all three models in both hemispheres. Two of the three models’ LGM simulations feature a reduction in storm count and global power dissipation index compared to the current climate, but a third shows no significant difference between the two climates. Although each model is forced by the same global changes, differences in the way sea surface temperatures and other large-scale environmental conditions respond in the North Atlantic impart significant differences in the climatology at LGM between models. Our results from the cold LGM provide a novel opportunity to assess how tropical cyclones respond to climate changes.

Historically unprecedented Northern Gulf of Mexico hurricane activity from 650 to 1250 CE

Hurricane Michael (2018) was the first Category 5 storm on record to make landfall on the Florida panhandle since at least 1851 CE (Common Era), and it resulted in the loss of 59 lives and $25 billion in damages across the southeastern U.S. This event placed a spotlight on recent intense (exceeding Category 4 or 5 on the Saffir-Simpson Hurricane Wind Scale) hurricane landfalls, prompting questions about the natural range in variability of hurricane activity that the instrumental record is too short to address. Of particular interest is determining whether the frequency of recent intense hurricane landfalls in the northern Gulf of Mexico (GOM) is within or outside the natural range of intense hurricane activity prior to 1851 CE. In this study, we identify intense hurricane landfalls in northwest Florida during the past 2000 years based on coarse anomaly event detection from two coastal lacustrine sediment archives. We identified a historically unprecedented period of heightened storm activity common to four Florida panhandle localities from 650 to 1250 CE and a shift to a relatively quiescent storm climate in the GOM spanning the past six centuries. Our study provides long-term context for events like Hurricane Michael and suggests that the observational period 1851 CE to present may underrepresent the natural range in landfalling hurricane activity.

The NASA‐JAXA Global Precipitation Measurement mission – part II: New frontiers in precipitation science

The <scp>NASA‐JAXA</scp> Global Precipitation Measurement mission – part <scp>II</scp>: New frontiers in precipitation science

Performance Assessment of Deteriorating Bridge Channels in the Presence of Hurricane Failures: The Competing Risks Scenario

Natural disasters such as hurricanes, earthquakes, and storm surges among other hazards affect communities and large geographic areas of the United States, resulting in negative repercussions on civil infrastructure. The impacts of these hazards on bridges affect the operability and functional integrity of transportation networks. This study focuses on two failure modes, namely the deterioration of bridge channels in the presence of competing hurricane hazards (hurricane-induced failure of channels) and the complex interactions between the two modes of failure, to evaluate the performance of bridge channels. To achieve the aforementioned objective, competing risks models are developed for the already-mentioned failure modes using the product limit survival analysis, cause-specific hazard rates, and the cumulative incidence functions (CIF). Failure time distributions are developed for standard commonly recognized bridge channel elements using legacy inspection data for bridges, annual probabilities of occurrence of hurricanes, and damage matrices from existing hurricane records. The method of maximum likelihood estimation (MLE), weighting techniques, and inference procedures are employed to describe competing risks event data for the analysis. From the results, it is observed that bridge channels deteriorate faster in the urban areas compared with rural areas. The deterioration and hurricane failures generally yield significantly different impacts based on the log-rank chi-square outputs. Also, it was observed that the impact of hurricane Categories 3 and 2 on bridge channels were more significant (based on the hazard ratios) at the coast than noncoastal areas and also generally high for urban bridge channels compared with rural channels.

Revising evidence of hurricane strikes on Abaco Island (The Bahamas) over the last 700\xa0years

The northern Bahamas have experienced more frequent intense-hurricane impacts than almost anywhere else in the Atlantic since 1850 CE. In 2019, category 5 (Saffir-Simpson scale) Hurricane Dorian demonstrated the destructive potential of these natural hazards. Problematically, determining whether high hurricane activity levels remained constant through time is difficult given the short observational record (< 170 years). We present a 700-year long, near-annually resolved stratigraphic record of hurricane passage near Thatchpoint Blue Hole (TPBH) on Abaco Island, The Bahamas. Using longer sediment cores (888 cm) and more reliable age-control, this study revises and temporally expands a previous study from TPBH that underestimated the sedimentation rate. TPBH records at least 13 ≥ category 2 hurricanes per century between 1500 to 1670 CE, which exceeds the 9 ≥ category 2 hurricanes per century within 50 km of TPBH since 1850 CE. The eastern United States also experienced frequent hurricanes from 1500 to 1670 CE, but frequency was depressed elsewhere in the Atlantic Ocean. This suggests that spatial heterogeneity in Atlantic hurricane activity since 1850 CE could have persisted throughout the last millennium. This heterogeneity is impacted by climatic and stochastic forcing, but additional high-resolution paleo-hurricane reconstructions are required to assess the mechanisms that impact regional variability.

Application of the Saffir-Simpson Hurricane Wind Scale to Assess Sand Dune Response to Tropical Storms

Over one-third of the Earth’s population resides or works within 200 km of the coast. The increasing threat of coastal hazards with predicted climate change will impact many global citizens. Coastal dune systems serve as a natural first line of defense against rising sea levels and coastal storms. This study investigated the volumetric changes of two dune systems on Isle of Palms, South Carolina, USA prior to and following Hurricanes Irma (2017) and Florence (2018), which impacted the island as tropical storms with different characteristics. Irma had relatively high significant wave heights and precipitation, resulting in an average 39% volumetric dune loss. During Florence, a storm where precipitation was low and winds were moderate, net volumetric dune loss averaged 3%. The primary driving force causing dune change during Irma was water (precipitation and storm surge), and during Florence, it was wind (aeolian transport). We suggest that the application of the Saffir-Simpson Hurricane Wind Scale classifications should be reconsidered because different geomorphic responses were measured, despite Irma and Florence both being designated as tropical storms. Site-specific pre- and post-storm studies of the dune morphology and site-specific meteorological measurements of the storm (wind characteristics, storm surge, precipitation) are critically needed.

Extreme rainfall and flooding from Hurricane Florence

Hurricane Florence made landfall near Wrightsville Beach, North Carolina, around 1115 UTC 14 September as a weakening Category 1 hurricane (on the Saffir-Simpson Hurricane Wind Scale). Its slow movement through North and South Carolina resulted in extreme rainfall totals and major flooding making it the ninth-most-destructive hurricane to affect the United States. The structure of Florence is examined using microwave data, radiosonde observations and rainfall registrations to identify the processes along with its slow movement which caused the extreme rainfall. Two major processes were identified with initially, intense thunderstorms driven by strong buoyancy with some dynamic uplift, generated such rainfall while following landfall larger scale ascent with winds turned in an anticyclonic direction with height, appeared to be the principal cause of the heavy rainfall.

Why Hurricane Categories Should Be Based on Surface Pressure

Why Hurricane Categories Should Be Based on Surface Pressure

Communication and Hazard Perception Lessons from Category Five Hurricane Michael

Hurricane Michael made landfall on 10 October 2018 as only the third Saffir Simpson Hurricane Wind Scale (SSHWS) category 5 storm in the USA in the named era. The storm’s intensity, rapid intensification, October landfall, high inland winds, and uncommon landfall location all combined to complicate the communication and preparation efforts of emergency managers (EMs) and broadcast meteorologists (BMs), while clouding the comprehension of the public. Interviews were conducted with EMs, BMs, and a small public sample to hear their stories and identify and understand common themes and experiences. This information and previous research was used to inform the creation of questions for a large sample public survey. Results showed that 61% of our sample did not evacuate, and approximately 80% either underestimated the intensity, misinterpreted or did not believe the forecast, or realized the danger too late to evacuate. Hazard perception from a survey of the public revealed that wind followed by tornadoes, and falling trees were the major concerns across the region. According to their counties of residence, participants were divided into Coastal or Inland, and Heavily Impacted or Less Impacted categories. Inland participants expressed a significantly higher concern for wind, tornadoes, falling trees, and rainfall/inland flooding than Coastal participants. Participants from Heavily Impacted counties showed greater concern for storm surge, tornadoes, and falling trees than participants from Less Impacted counties. These results reinforce the continued need for all parties of the weather enterprise to strengthen communication capabilities with EMs and the public for extreme events.

Remote sensing and statistical analysis of the effects of hurricane María on the forests of Puerto Rico

Widely recognized as one of the worst natural disaster in Puerto Rico's history, hurricane María made landfall on September 20, 2017 in southeast Puerto Rico as a high-end category 4 hurricane on the Saffir-Simpson scale causing widespread destruction, fatalities and forest disturbance. This study focused on hurricane María's effect on Puerto Rico's forests as well as the effect of landform and forest characteristics on observed disturbance patterns. We used Google Earth Engine (GEE) to assess the severity of forest disturbance using a disturbance metric based on Landsat 8 satellite data composites with pre and post-hurricane María. Forest structure, tree phenology characteristics, and landforms were obtained from satellite data products, including digital elevation model and global forest canopy height. Our analyses showed that forest structure, and characteristics such as forest age and forest type affected patterns of forest disturbance. Among forest types, highest disturbance values were found in sierra palm, transitional, and tall cloud forests; seasonal evergreen forests with coconut palm; and mangrove forests. For landforms, greatest disturbance metrics was found at high elevations, steeper slopes, and windward surfaces. As expected, high levels of disturbance were also found close to the hurricane track, with disturbance less severe as hurricane María moved inland. Results demonstrated that forest and landform characteristics accounted for 34% of the variation in spatial forest spectral disturbance patterns. This study demonstrated an informative regional approach, combining remote sensing with statistical analyses to investigate factors that result in variability in hurricane effects on forest ecosystems.