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Finger‐like km‐scale features have been observed along the inner‐edge of the eyewall of intense hurricanes. But due to the limited availability of observations, many important aspects of these features remain unknown. In this study, we aim to offer insights on the nature of these phenomena based on a four‐day‐duration O(100 m) grid spacing simulation that covers the inner‐core region of an idealized hurricane. The simulation successfully captured the finger‐like features, which closely resemble observed ones. We propose that these features are formed due to the shear instability associated with vertical distribution of the tangential wind in the inner‐core region. This proposed mechanism offers insights on several key characteristics of the features of interest, including their emergence time, frequency, radial location and vertical extent. Our study also demonstrates the feasibility of using multi‐level nesting for O(100 m) grid spacing hurricane simulations and predictions, aligning with the goals for next generation hurricane models. Plain Language Summary: The inner core region of hurricanes harbors complex dynamical features, including small‐scale clouds characterized by finger‐like appearances pointing toward the hurricane eye. These features have been frequently observed in intense hurricanes. However, many basic aspects of these features remain unknown, particularly regarding what controls their occurrence and location. We conduct a numerical simulation with a very fine (about 100 m) horizontal grid spacing to investigate the nature of these features. Our proposed mechanism explains several key characteristics of these features. Key Points: We conduct a O(100 m) grid spacing simulation that captures the finger‐like features along the inner edge of the hurricane eyewallWe propose a mechanism that links the finger cloud formation and hurricane‐scale dynamicsThis proposed mechanism explains the emergence time, frequency, radial location and vertical extent of the finger features [ABSTRACT FROM AUTHOR]

When making travel decisions, the news is a key source of weather information for tourists, particularly when there is a perceived risk of holiday disruption. Misrepresentation and sensationalist media coverage have been attributed to amplifying perceptions of climatic risk amongst the public. As the most tourism-reliant region in the world, how the media constructs and communicates hurricane events in the Caribbean can influence tourism demand, with implications for millions who rely on the sector for their livelihoods. Through a content analysis of global news articles published over the last 40 years (n = 635), this paper examines the attribute agenda setting and framing of hurricanes and Caribbean tourism. Over 60% of the articles omitted critical information (e.g., hurricane path, location of strike, category), with 11 of the 13 attributes negative in tone (i.e., risk amplifying). Four frames (victim, apocalyptic, disruptive, and business-as-usual) were identified, with most articles (66%) framing tourists and their vacation experiences as inevitable casualties of a hurricane event. The findings can aid regional stakeholder decisions on communication and marketing strategies during and following hurricane events to minimize negative impacts on tourism demand, particularly in unaffected destinations. [ABSTRACT FROM AUTHOR]

Better understanding the effects of hurricane waves and storm surges on barrier-island breaching is important for both scientific research and coastal hazard mitigations. In this study, the 2D non-hydrostatic Xbeach model has been applied to investigate interactions of hurricane wave, storm surge, and morphological processes in the case study of St. Joseph Peninsula during Category 5 Hurricane Michael. Model validations show a 2.45% average error and the 0.88 skill score between modeled and observed high water marks and bed elevations, respectively. Analysis of spatial distributions of currents and water levels indicates that a narrow area was overtopped at peak storm surge and wave. The gap was then quickly enlarged as the breaching area by wave-surge actions. By investigating foredune and peak dune along the central axis of breaching area, it shows that the foredune erosion on the ocean-side by wave-surge-current directly lead to the breach of the peak dune area in the barrier island. The Froude number shows a strong correlation with quick erosion of the barrier, indicating wave-surge supercritical flow is one of the major factors causing the barrier breaching. Results of cross sections of bed elevations and instantaneous surge-wave profiles at different storm surge stages reveal the evolution of the barrier-island breach. Results from this study provide valuable references for coastal hazard mitigation and resilience communities. Highlights: Application of non-hydrostatic Xbeach model reveals barrier-island breaching process and wave-surge-barrier interactions. Model validations show a 2.45% average error and the 0.88 skill score for high water marks and bed elevations, respectively. The breach started with a narrow gap overtopped in the peak of storm surge and was then quickly enlarged by wave-surge actions. Wave-surge induced supercritical flow is one of the major factors accelerating the barrier breaching. Foredune erosion by wave-surge-current is another factor that accelerates the breach of the barrier island. [ABSTRACT FROM AUTHOR]

Previous work identified hurricane deposits in the back-barrier lagoon of Laguna Playa Grande (LPG) in Vieques, Puerto Rico, dating back over 5000 years, with periods of increased storm-induced overwash activity attributed to variability in regional hurricane climatology. In 2017, Hurricane Maria made direct landfall on LPG just below category 5 strength, providing the opportunity to revisit the site to improve upon interpretations of storm-induced deposition. Maria caused widespread wave-induced overwash of the barrier beach and extensive mangrove mortality with roughly a 40% reduction in vegetative cover along the barrier. Sediment trapping and overwash deposition occurred within ripped up and broken mangrove debris during the hurricane, which prevented sediments from being carried further landward into the lagoon. Thus, no measurable overwash deposition was observed in the larger, western portion of the lagoon where previous hurricane reconstructions are derived. Significant overwash deposition (1–27 cm thick) was observed in the smaller, eastern portion of the lagoon where human cut paths through the mangroves allowed for unobstructed flow. Early historical photos support 53% reduced vegetation in 1936 followed by revegetation towards present day that prevented sandy overwash deposits from Maria in the western side, explaining the discrepancy between previously observed hurricane overwash record compared to restricted deposition solely to locations with modern footpaths for Maria. Hurricanes occurring during the recovery-revegetation phase of such storms likely result in greater overwash deposition in back-barrier lagoons due to less barrier vegetation relative to periods with robust mangrove vegetation cover. Results highlight the important role of fringing mangrove forests in flood mitigation, and the vulnerability of back-barrier environments to enhanced flooding following both anthropogenic and event-driven vegetation loss. [ABSTRACT FROM AUTHOR]

The Province of Nova Scotia has a broad range of responsibilities during provincial emergencies. Hurricane Fiona had significant impacts on citizens and government services for a considerable time due to widespread telecommunication and power outages. The spring wildfires caused widespread destruction, presented coordination and logistical problems, and resulted in mental health impacts for affected communities and responders. This paper describes the challenges faced by provincial emergency management and business continuity response teams: 1) mental health matters — the next evolution in business continuity requires building confidence in people executing the plan under stressful and uncertain conditions for extended periods; 2) senior leadership support is vital to balance business continuity priorities with ongoing operational business demands; and 3) the best plans are as effective as the relationships that make them work. The importance and value of individual and team relationships during planning, preparedness and response, therefore, cannot be underestimated. [ABSTRACT FROM AUTHOR]

Hurricane Harvey caused widespread flooding along the Texas Gulf Coast in August 2017; some areas of Houston received >150 cm of rainfall within a few days. Due to concerns over fecal contamination of floodwaters, surface water samples were collected at six locations in the southeastern Houston area immediately before and after the hurricane and then every 1 to 2 weeks thereafter over a 2-month period. Total E. coli was enumerated using the IDEXX Quanti-Tray/2000 system. DNA extracted from water samples was analyzed via quantitative real-time PCR (qPCR) for general and source-specific total Bacteroidales and human Bacteroidales markers, and digital PCR (dPCR) for antibiotic resistance genes (ARG) and a plasmid (pBI143) associated with human waste. SourceTracker2 was used to determine human source contributions based on metagenomic analysis of PCR-amplified 16S rRNA gene fragments. Samples collected immediately after the hurricane had elevated levels of E. coli, ranging from 488 to 1,733 CFU 100 ml−1. After 1 week, E. coli levels decreased to <100 MPN 100 ml−1. Total Bacteroidales numbers were elevated immediately following the hurricane and remained high for 12 days. Human-source contributions, as assessed by PCR methods and metagenomic analysis, peaked within 12 days after the hurricane consistently across all sampling sites. Multiple regression analysis of environmental parameters, copies of ARG and pBI143, and metagenomic data confirmed that human waste caused the dramatic, short-term, high levels of fecal contamination of floodwaters generated by Hurricane Harvey. Fecal indicators approached normal background levels approximately 3 weeks after the rainfall ended. [ABSTRACT FROM AUTHOR]

Coastal mangrove forests provide numerous ecosystem services, which can be disrupted by natural disturbances, mainly hurricanes. Canopy height (CH) is a key parameter for estimating carbon storage. Airborne Light Detection and Ranging (LiDAR) is widely viewed as the most accurate method for estimating CH but data are often limited in spatial coverage and are not readily available for rapid impact assessment after hurricane events. Hence, we evaluated the use of systematically acquired space-based Synthetic Aperture Radar (SAR) and optical observations with airborne LiDAR to predict CH across expansive mangrove areas in South Florida that were severely impacted by Category 3 Hurricane Irma in 2017. We used pre- and post-Irma LiDAR-derived canopy height models (CHMs) to train Random Forest regression models that used features of Sentinel-1 SAR time series, Landsat-8 optical, and classified mangrove maps. We evaluated (1) spatial transfer learning to predict regional CH for both time periods and (2) temporal transfer learning coupled with species-specific error correction models to predict post-Irma CH using models trained by pre-Irma data. Model performance of SAR and optical data differed with time period and across height classes. For spatial transfer, SAR data models achieved higher accuracy than optical models for post-Irma, while the opposite was the case for the pre-Irma period. For temporal transfer, SAR models were more accurate for tall trees (>10 m) but optical models were more accurate for short trees. By fusing data of both sensors, spatial and temporal transfer learning achieved the root mean square errors (RMSEs) of 1.9 m and 1.7 m, respectively, for absolute CH. Predicted CH losses were comparable with LiDAR-derived reference values across height and species classes. Spatial and temporal transfer learning techniques applied to readily available spaceborne satellite data can enable conservation managers to assess the impacts of disturbances on regional coastal ecosystems efficiently and within a practical timeframe after a disturbance event. [ABSTRACT FROM AUTHOR]

In the 2023 hurricane season, the Hurricane Analysis and Forecast System (HAFS) based Ensemble Prediction System (EPS) was being ported to the Amazon Web Service cloud. This relocation aimed to provide real-time hurricane probabilistic forecast guidance for National Hurricane Center (NHC) forecasters. The system comprises Stochastically Perturbed Physics Tendencies (SPPT), Stochastically Kinetic Energy Backscatter (SKEB), and Stochastically Perturbed PBL Humidity (SHUM). Initial and boundary conditions are derived from the National Centers for Environmental Prediction (NCEP) operational Global Ensemble Forecast System (GEFS) 21-member forecast data. The performance of HAFS-EPS for 2023 Atlantic hurricane forecasts was compared with the global GEFS, global ECMWF ensemble, and operational HAFS-A/B forecasts. This comparison highlighted the advantages of higher-resolution regional ensemble forecasts for hurricane track, intensity, Rapid Intensification (RI) probability, and various hazards, including wind, wave, and storm surge probability guidance. [ABSTRACT FROM AUTHOR]

Tropical cyclones models have long used nesting to achieve higher resolution of the inner core than was feasible for entire model domains. These high resolution nests have been shown to better capture storm structures and improve forecast accuracy. The Hurricane Analysis and Forecast System (HAFS) is the new-generation numerical model embedded within NOAA's Unified Forecast System (UFS). The document highlights the importance of high horizontal resolution (2 km or finer) in accurately simulating the small-scale features of tropical cyclones, such as the eyewall and eye. To meet this need, HAFS was developed by NOAA leveraging a high-resolution, storm-following nest. This nest moves with the cyclone, allowing better representation of small-scale features and more accurate feedback between the cyclone's inner core and the larger environment. This hurricane following nest capability, implemented in the Finite-Volume Cubed-Sphere (FV3) dynamical core within the UFS framework, can be run both within the regional as well as global forecast systems. A regional version of HAFS with a single moving nest went into operations in 2023. HAFS also includes the first ever moving nest implemented within a global model which is currently being used for research. In this document we provide details of the implementation of moving nests and provide some of the results from both global and regional simulations. For the first time NOAA P3 flight data was used to evaluate the inner core structure from the global run. [ABSTRACT FROM AUTHOR]

The purpose of this study was to explore the relationships between trauma and adherence to gender roles among a sample of Hurricane Harvey survivors (N = 225). Posttraumatic stress disorder (PTSD) resulting from natural disasters has been associated with an increased risk of negative psychological outcomes. Previous studies have found increased risks of PTSD resulting from hurricane exposure, and there also appears to be a correlation between PTSD and gender. Specifically, women have been found to be more likely to endorse PTSD symptoms. However, this relationship has not yet been examined specifically among survivors of trauma from natural disasters. The Hayes Process Macro (v3.5) was utilized to test for the potential moderating effect of adherence to traditional gender roles on the relationship between trauma exposure and PTSD symptoms, as well as trauma exposure and depression. Results indicated that adherence to traditional female gender roles significantly moderated the relationships both between trauma severity and PTSD symptoms and between trauma severity and depression. Those with more androgynous gender roles have been found to have an increased ability to utilize more situation-specific coping skills. Future research could explore the specific aspects of traditional female and male gender roles that increase or decrease ability to cope with trauma. [ABSTRACT FROM AUTHOR]

As coastal populations surge, the devastation caused by hurricanes becomes more catastrophic. Understanding the extent of the damage is essential as this knowledge helps shape our plans and decisions to reduce the effects of hurricanes. While community and property-level damage post-hurricane damage assessments are common, evaluations at the building component level, such as roofs, windows, and walls, are rarely conducted. This scarcity is attributed to the challenges inherent in automating precise object detections. Moreover, a significant disconnection exists between manual damage assessments, typically logged-in spreadsheets, and images of the damaged buildings. Extracting historical damage insights from these datasets becomes arduous without a digital linkage. This study introduces an innovative workflow anchored in state-of-the-art deep learning models to address these gaps. The methodology offers enhanced image annotation capabilities by leveraging large-scale pre-trained instance segmentation models and accurate damaged building component segmentation from transformer-based fine-tuning detection models. Coupled with a novel data repository structure, this study merges the segmentation mask of hurricane-affected components with manual damage assessment data, heralding a transformative approach to hurricane-induced building damage assessments and visualization. [ABSTRACT FROM AUTHOR]

Estuarine phytoplankton communities are acclimated to environmental parameters that change seasonally. With climate change, they are having to respond to extreme weather events that create dramatic alterations to ecosystem function(s) on the scale of days. Herein, we examined the short term (<1 month) shifts in phytoplankton communities associated with four pulse disturbances (Tax Day Flood in 2016, Hurricane Harvey in 2017, Tropical Storm Imelda in 2019, and Winter Storm Uri in 2021) that occurred in Galveston Bay (TX, USA). Water samples collected daily were processed using an Imaging FlowCytobot (IFCB), along with concurrent measurements of temperature, salinity, and chlorophyll-a. Stronger storm events with localized heavy precipitation and flooding had greater impacts on community composition, increasing diversity (Shannon–Weiner and Simpson Indices) while a cold wave event lowered it. Diatoms and dinoflagellates accounted for the largest fraction of the community, cyanobacteria and chlorophytes varied mostly with salinity, while euglenoids, cryptophytes, and raphidophytes, albeit at lower densities, fluctuated greatly. The unconstrained variance of the redundancy analysis models pointed to additional environmental processes than those measured being responsible for the changes observed. These findings provide insights into the impact of pulse disturbances of different magnitudes, durations, and timings on phytoplankton communities. [ABSTRACT FROM AUTHOR]

To improve preparedness for natural disasters, it is imperative to understand the factors that enable individual risk‐reduction actions. This study offers such insights using innovative real‐time (N = 871) and repeated (N = 255) surveys of a sample of coastal residents in Florida regarding flood preparations and their drivers during an imminent threat posed by Hurricane Dorian and its aftermath. Compared with commonly employed cross‐sectional surveys, our methodology better represents relationships between preparedness actions undertaken during the disaster threat and their drivers derived from an extended version of Protection Motivation Theory (PMT). The repeated survey allows for examining temporal dynamics in these drivers. Our results confirm the importance of coping appraisals and show that risk perceptions relate more strongly to emergency protection decisions made during the period of the disaster threat than to decisions made well before. Moreover, we find that several personal characteristics that we add to the standard PMT framework significantly relate to undertaking preparedness actions, especially locus of control and social norms. Significant changes in key explanatory variables occur following the disaster threat, including a decline in risk perception, a potential learning effect in coping appraisals, and a decline in risk aversion. Our results confirm the advantage of the real‐time and repeated survey approach in understanding both short‐ and long‐term disaster preparedness actions. [ABSTRACT FROM AUTHOR]

This study addresses the limitations of and the common challenges faced by one-dimensional river-routing methods in hydrological models, including the National Water Model (NWM), in accurately representing coastal regions. We developed a two-way coupling between the NWM and the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM). The approach demonstrated improvements in modeling coastal river dynamics, particularly during extreme events like Hurricane Matthew. The coupled model successfully captured tidal influences, storm surge effects, and complex river–river interactions that the standalone NWM missed. The approach revealed more accurate representations of peak discharge timing and magnitude as well as water storage and release in coastal floodplains. However, we also identified challenges in reconciling variable representations between hydrological and hydraulic models. This work not only enhances the understanding of coastal–riverine interactions but also provides valuable insights for the development of next-generation hydrological models. The improved modeling capabilities have implications for flood forecasting, coastal management, and climate change adaptation in vulnerable coastal areas. [ABSTRACT FROM AUTHOR]

Hurricane Harvey was the second costliest storm to impact the U.S. More research is needed to understand the mental health consequences of these extreme events in children and adolescents extending beyond the acute recovery period. Daily anonymized Crisis Text Line (CTL) conversations were used to understand patterns in crisis responses for youth one year before and after Harvey's landfall. A quasi-experimental difference-in-differences analysis compared changes in texts for stress/anxiety, depression, thoughts of suicide, and self-harm following Harvey between exposed and unexposed youth in Texas. CTL users with Texas-based area codes (N = 23,016) were compriesd largely of youth who self-identified as female (78.1 %), 14–17 year old (50.4 %), white (38.9 %), and LGBTQ+ (51.2 %). We observed parallel increases in crisis texts for depression and thoughts of suicide in most months following Harvey among exposed and unexposed youth. However, non-impacted youth had significantly larger increases in texts for depression up to three months post-Harvey and thoughts of suicide one year after Harvey compared to directly impacted communities. Sample size was restricted to texters who completed the post-conversation demographics survey, who may fundamentally differ from those who declined to respond. Harvey exposure was determined using texter area code and county-level disaster declarations, limiting our ability to guarantee individual-level exposure. Texas youth traditionally considered unexposed experienced nearly identical increases in concerns of depression and thoughts of suicide to those directly exposed. Findings suggest spillover effects (e.g., economic concerns, media exposure) may contribute to statewide impacts on youth mental health after natural disasters. • More research on mental health impacts of climate disasters in youth is needed. • Causal difference-in-difference model measured crisis response in youth post-Harvey. • Depression and thoughts of suicide increased statewide in Texas youth after Harvey. • Findings suggest need for broader dissemination of crisis support services in youth. [ABSTRACT FROM AUTHOR]

This study presents the implementation and evaluation of scale‐dependent localization (SDL) in the hurricane analysis and forecast system 4D Ensemble Variational (4DEnVar) Data Assimilation (DA) system. The SDL capability is compared with the traditional Single‐Scale Localization (SSL) method to assess its benefits and necessity for hurricane prediction. The experiments focus on Hurricane Laura (2020) and involve single observation experiments as well as real observation DA cycling experiments. The results indicate that the SDL experiment, which incorporates the Fast Almost Gaussian Filtering approach for scale decomposition, consistently outperforms the corresponding SSL configurations in almost all aspects. Further diagnostics show that due to its multiscale nature, the SDL approach demonstrates better track prediction over small‐scale SSL due to improved environmental analysis and better analyzed vortex position and structure, and superior intensity prediction during the rapid intensification over both the large‐scale SSL and the small‐scale SSL owing to enhanced inner‐core thermodynamic analysis. Plain Language Summary: This study focuses on improving hurricane forecasting through a simultaneous multiscale data assimilation (DA) technique called scale‐dependent localization (SDL). We compare SDL to the traditional method called Single‐Scale Localization (SSL) using single observation experiments and real observation DA cycling experiments during Hurricane Laura (2020). Our findings show that compared to SSL, SDL significantly outperforms the corresponding SSL in various aspects. It provides more accurate prediction of the hurricane's track by improving the analysis of the storm's surrounding environment and the initial vortex position. Additionally, SDL enhances intensity prediction during the storm's rapid intensification stages by analyzing the inner‐core structure more effectively. Key Points: Scale dependent localization (SDL) is studied for hurricane data assimilation in a convection allowing model for the first timeBenefiting from the small‐scale localization, SDL outperforms single large‐scale localization in the intensity predictionBenefiting from the large‐scale localization, SDL outperforms single small‐scale localization in both the intensity and track prediction [ABSTRACT FROM AUTHOR]

Geostationary Operational Environmental Satellite-16 (GOES-16) Advanced Baseline Imager (ABI) observations of brightness temperature (TB) are used to examine the temporal evolutions of convection-affected structures of Hurricane Irma (2017) during its rapid intensification (RI) period from 0600 to 1800 UTC 4 September 2017. The ABI observations reveal that both an elliptical eye and a spiral rainband that originated from Irma's eyewall obviously exhibit wavenumber-2 TB asymmetries. The elliptical eye underwent a counterclockwise rotation at a mean speed of a wavenumber-2 vortex Rossby edge wave from 0815 to 1005 UTC 4 September. In the following about 2 hours (1025–1255 UTC 4 September), an inner spiral rainband originated from the eyewall and propagated at a phase speed that approximates the vortex Rossby wave (VRW) phase speed calculated from the aircraft reconnaissance data. During the RI period of Irma, ABI TB observations show an on–off occurrence of low TB intrusions into the eye, accompanying a phase lock of eyewall TB asymmetries of wavenumbers 1 and 2 and an outward propagation of VRW-like inner spiral rainbands from the eyewall. The phase lock leads to an energy growth of Irma's eyewall asymmetries. Although the eye remained clear from 1415 to 1725 UTC 4 September, an inner spiral rainband that originated from a large convective area also had a VRW-like outward propagation, which is probably due to a vertical tilt of Irma. This study suggests a potential link between convection sensitive GOES imager observations and hurricane dynamics. [ABSTRACT FROM AUTHOR]

Abstract Finger‐like km‐scale features have been observed along the inner‐edge of the eyewall of intense hurricanes. But due to the limited availability of observations, many important aspects of these features remain unknown. In this study, we aim to offer insights on the nature of these phenomena based on a four‐day‐duration O(100 m) grid spacing simulation that covers the inner‐core region of an idealized hurricane. The simulation successfully captured the finger‐like features, which closely resemble observed ones. We propose that these features are formed due to the shear instability associated with vertical distribution of the tangential wind in the inner‐core region. This proposed mechanism offers insights on several key characteristics of the features of interest, including their emergence time, frequency, radial location and vertical extent. Our study also demonstrates the feasibility of using multi‐level nesting for O(100 m) grid spacing hurricane simulations and predictions, aligning with the goals for next generation hurricane models.

Hurricane Harvey caused widespread flooding along the Texas Gulf Coast in August 2017; some areas of Houston received >150 cm of rainfall within a few days. Due to concerns over fecal contamination of floodwaters, surface water samples were collected at six locations in the southeastern Houston area immediately before and after the hurricane and then every 1 to 2 weeks thereafter over a 2-month period. Total E. coli was enumerated using the IDEXX Quanti-Tray/2000 system. DNA extracted from water samples was analyzed via quantitative real-time PCR (qPCR) for general and source-specific total Bacteroidales and human Bacteroidales markers, and digital PCR (dPCR) for antibiotic resistance genes (ARG) and a plasmid (pBI143) associated with human waste. SourceTracker2 was used to determine human source contributions based on metagenomic analysis of PCR-amplified 16S rRNA gene fragments. Samples collected immediately after the hurricane had elevated levels of E. coli, ranging from 488 to 1,733 CFU 100 ml−1. After 1 week, E. coli levels decreased to

Devastation from hurricanes can cause multiple problems for turfgrass managers, sod producers, and homeowners. Damage may include visible factors such as prolonged periods of standing water, uprooted trees, shading and obstruction from demolished structures, deposition of silt or mud, and damage to irrigation systems. Less visible problems may include damage to root systems resulting from compacted soil from heavy equipment used during cleanup and recovery, reduced drainage, and injury resulting from effects of saltwater intrusion. Suggested guidelines for correcting these problems are included in the text.

Tropical cyclones models have long used nesting to achieve higher resolution of the inner core than was feasible for entire model domains. These high resolution nests have been shown to better capture storm structures and improve forecast accuracy. The Hurricane Analysis and Forecast System (HAFS) is the new-generation numerical model embedded within NOAA’s Unified Forecast System (UFS). The document highlights the importance of high horizontal resolution (2 km or finer) in accurately simulating the small-scale features of tropical cyclones, such as the eyewall and eye. To meet this need, HAFS was developed by NOAA leveraging a high-resolution, storm-following nest. This nest moves with the cyclone, allowing better representation of small-scale features and more accurate feedback between the cyclone’s inner core and the larger environment. This hurricane following nest capability, implemented in the Finite-Volume Cubed-Sphere (FV3) dynamical core within the UFS framework, can be run both within the regional as well as global forecast systems. A regional version of HAFS with a single moving nest went into operations in 2023. HAFS also includes the first ever moving nest implemented within a global model which is currently being used for research. In this document we provide details of the implementation of moving nests and provide some of the results from both global and regional simulations. For the first time NOAA P3 flight data was used to evaluate the inner core structure from the global run.

Background: Food insecurity is traditionally defined as a chronic condition linked to insufficient income, but in post-disaster contexts the condition may differ significantly, often with sudden onset and temporary nature. There is no specific definition for post-disaster food insecurity, making accurate data collection and development of effective interventions difficult. Methods: To explore this issue, we performed a pilot survey study (n = 22) in Puerto Rico after Hurricane Maria to evaluate food insecurity status, duration, and causes. Results: Over half of respondents self-reported experiencing food insecurity following Hurricane Maria. In general, food insecure respondents experienced more specific food group shortages that lasted longer than food secure respondents. The duration of food shortages significantly influenced participants' perceptions of food insecurity. The primary causes of food insecurity were identified as grocery store closures, a lack of food in stores, or other reasons not listed in the survey. These findings support what is known about the post-disaster setting with supply chain issues being a prominent mechanism of food insecurity as well as there being multiple mechanisms difficult to describe due to the complexity of the situation. Conclusions: Results highlight the need for refined definitions and assessments of post-disaster food insecurity that account for the temporal aspects of food shortages and the complexity of post-disaster settings. As climate change exacerbates the frequency and severity of natural disasters, addressing the nuances of post-disaster food insecurity is increasingly urgent to mitigate associated health risks.

Florida citrus production from 1980-2021 was examined and modeled to determine the impacts associated with weather events and disease introductions.  Specifically, the study examined the effects of North Atlantic hurricanes, freezes events and two disease introductions -- Asiatic citrus canker (ACC), and Huanglongbing (HLB) -- on productions levels and on the structure of the Florida citrus industry.  The models estimated quantified the effects on production associated with the weather events and disease introductions.  Using the deterministic model generated, forecasts were generated to identify future implications of HLB on Florida citrus production.  Theses generated forecasts were compared to actual production levels and the USDA Crop forecast to test and validate the model.  Whereas testing indicated a significant structural change in the Florida citrus industry resulting from adverse weather events and disease introductions, published economic impact studies were examined and reviewed to gage the resulting reduction in total economic impact.

An often-overlooked question of the biodiversity crisis is how natural hazards contribute to species extinction risk. To address this issue, we explored how four natural hazards, earthquakes, hurricanes, tsunamis, and volcanoes, overlapped with the distribution ranges of amphibians, birds, mammals, and reptiles that have either narrow distributions or populations with few mature individuals. To assess which species are at risk from these natural hazards, we combined the frequency and magnitude of each natural hazard to estimate their impact. We considered species at risk if they overlapped with regions where any of the four natural hazards historically occurred (n = 3,722). Those species with at least a quarter of their range subjected to a high relative impact were considered at high risk (n = 2,001) of extinction due to natural hazards. In total, 834 reptiles, 617 amphibians, 302 birds, and 248 mammals were at high risk and they were mainly distributed on islands and in the tropics. Hurricanes (n = 983) and earthquakes (n = 868) affected most species, while tsunamis (n = 272), and volcanoes (n = 171) affected considerably fewer. The region with the highest number of species at high risk was the Pacific Ring of Fire, especially due to volcanoes, earthquakes, and tsunamis, while hurricane-related high-risk species were concentrated in the Caribbean Sea, Gulf of Mexico, and northwestern Pacific Ocean. Our study provides important information regarding the species at risk due to natural hazards and can help guide conservation attention and efforts to safeguard their survival. [ABSTRACT FROM AUTHOR]

Leaf litter decomposition is a key ecosystem process in headwater streams, influenced by physical fragmentation, microbial degradation and feeding activity by stream biota. In some tropical streams, feeding by freshwater shrimps can exert strong top-down control on leaf litter decomposition, however, variation in shrimp macroconsumer effects across small spatial scales or among years is not well-known. We ran 50-day macroconsumer exclusion experiments to measure shrimp effects on leaf decomposition in two adjacent headwater streams in Puerto Rico, in 2017 (immediately prior to two Category 4 and 5 hurricanes) and again in 2018 and 2019, to assess shrimp effects in the context of post-hurricane conditions that included reduced canopy cover and higher shrimp (Atya and Xiphocaris) counts. Leaf decomposition was faster when shrimp had access to leaf packs, but only in the study stream with larger pools, which also had higher overall shrimp counts. However, increased shrimp abundances following the hurricanes did not result in faster decomposition, potentially because shrimp diets shifted toward algae post-hurricanes when canopies were more open. We conclude that shrimp effects on leaf litter breakdown may vary between adjacent streams that differ in habitat conditions and that increasing local shrimp abundances may fail to accelerate decomposition. [ABSTRACT FROM AUTHOR]

Hurricanes are extreme climatic events frequently affecting tropical regions such as the tropical dry forests (TDFs) in Mexico, where its frequency/intensity is expected to increase toward the year 2100. To answer how resistant is a Mexican tropical dry forest to a high-intensity hurricane, and if its degree of resistance was mediated by its conservation degree, we evaluated the effect of a category 4 hurricane over the tree community, soil nutrients, and soil enzymatic activity in two contrasting TDF ecosystems: Old-Growth Forest (OGF) and Secondary Forest (SF). In general, vegetation richness and diversity showed very high resistance one year after the hurricane, but several structural attributes did not, especially in the OGF where the tree mortality related to vegetation structure and spatial distribution of individuals was higher. Then, in the short term, SF vegetation appeared to be more resistant, whereas the OGF, with more biomass to lose, appeared to be more vulnerable. Conversely, most soil attributes showed low resistance in both stages, but especially in SF which could face more severe nutrient limitations. The response of TDF to high-intensity hurricanes, in terms of above- and belowground processes, was in part dependent on its disturbance level. Moreover, an increase in the intensity/frequency of hurricanes could lead this TDF toward a high nutrient limitation (especially by phosphorus) for the plants and consequently toward a loss of soil functioning, especially in the SF. This eventually could produce a severe degradation in fundamental attributes and functions of the ecosystem. [ABSTRACT FROM AUTHOR]

The insurance industry has used parametric solutions to transfer catastrophe risks since the 1990s. Instead of relying on a lengthy process to assess a claim, these products pay the insured a pre-agreed amount if the physical characteristics of the event fulfill pre-defined conditions. Cat-in-a-box or cat-in-a-circle triggers, commonly used tools for tropical cyclone risk transfer, provide a payout to the insured if the track of a hurricane crosses the perimeter of a geographic area defined by a polygon or a circle with a certain intensity. Cat-in-a-grid solutions are novel and more sophisticated. They rely on a set of multiple cat-in-a-box triggers arranged on an orthogonal grid. The consideration of multiple geographic domains instead of a single box or circle is helpful to reduce basis risk, i.e., the difference between the parametric loss estimate and the target loss. In the case study for Miami presented here, for instance, a cat-in-a-grid solution showed 18.5% less basis risk than a typical cat-in-a-box alternative. To organize the different types of triggers within a common framework, we classify the existing alternatives based on whether they use a single geographic domain (like a box or a circle) or multiple domains (like a grid). We discuss their advantages and disadvantages and describe the process required to calibrate any one solution with the help of a catastrophe-risk model. We focus, in particular, on the analysis and construction of cat-in-a-grid triggers, the alternative that we believe offers the greatest potential for global standardization and adoption. [ABSTRACT FROM AUTHOR]

The current study explores the relationship between solar variability and tropical cyclone (TC) activity using sunspot number (SSN) and TC best‐track data as respective proxies. We have considered six regions of the globe, for example, EP: Eastern Pacific, NA: North Atlantic, NI: North Indian, SI: South Indian, SP: South Pacific, and WP: Western Pacific. The results show strong anti‐correlation between yearly TC activity and yearly SSN while considering their 11‐year moving averages. This behavior is consistent for TC counts as well as accumulated cyclone energy. However, this is true only for the North Atlantic region. Overall, when we consider all regions together, more TCs (in terms of counts) are observed during lower solar activity periods (SSN < 50) as compared to higher solar activity conditions (SSN > 100). However, the yearly rates remain more or less similar. On the other hand, extreme TC events with a maximum wind speed of 137 knots and higher (category 5) are most likely to occur during the declining phase of a solar cycle and least likely to occur during the ascending phase or the maximum phase. Although solar activity levels are similar during the declining and ascending phases, the yearly occurrence rate is nearly double in the declining phase (1.123) as compared to that in the ascending phase (0.625). Key Points: Tropical cyclones (TCs) occurrences and solar activity show strong anti‐correlation in the North Atlantic sectorExtreme TCs are most likely to occur during the declining phase of a solar cycle and least likely to occur during the maximum phaseThe yearly occurrence rate of extreme TCs is nearly double in the declining phase of a solar cycle as compared to that in the ascending phase [ABSTRACT FROM AUTHOR]

Coastal zone compound flooding (CF) can be caused by the interactive fluvial and oceanic processes, particularly when coastal backwater propagates upstream and interacts with high river discharge. The modeling of CF is limited in existing Earth System Models (ESMs) due to coarse mesh resolutions and one‐way coupled river‐ocean components. In this study, we present a novel multi‐scale coupling framework within the Energy Exascale Earth System Model (E3SM), integrating global atmosphere and land with interactively coupled river and ocean models using different meshes with refined resolutions near the coastline. To evaluate this framework, we conducted ensemble simulations of a CF event (Hurricane Irene in 2011) in a Mid‐Atlantic estuary. The results demonstrate that the novel E3SM configuration can reasonably reproduce river discharge and sea surface height variations. The two‐way river‐ocean coupling improves the representation of coastal backwater effects at the terrestrial‐aquatic interface that are caused by the combined actions of tide and storm surge during the CF event, thus providing a valuable modeling tool for better understanding the river‐estuary‐ocean dynamics in extreme events under climate change. Notably, our results show that the most significant CF impacts occur when the highest storm surge generated by a tropical cyclone meets with a moderate river discharge. This study highlights the state‐of‐the‐art advancements developed within E3SM for simulating multi‐scale coastal processes. Plain Language Summary: Compound flooding (CF) happens when rivers and oceans interact in the coastal zone. There are limitations in current models to accurately simulate these processes because of the insufficient resolutions in the computational meshes and lack of details on how rivers and oceans are connected. This study creates a comprehensive framework for the Energy Exascale Earth System Model (E3SM) and demonstrates its ability to simulate a specific compound flooding event in a Mid‐Atlantic estuary. Our framework combines models of the atmosphere, land, river, and ocean, each with its own level of detail near the coastline to account for their different physical processes. Our results show that the E3SM framework can reproduce the river discharge and sea level variations reasonably well. By simulating the interaction between river and ocean, we can better understand the effects of coastal water on river discharge forced by tides and storm surges during the CF event. Our simulation reveals that CF is most significant when a tropical cyclone produces the highest storm surge but moderate river discharge. This study demonstrates the capability of the E3SM model to accurately simulate the detailed coastal processes. Key Points: An Energy Exascale Earth System Model (E3SM) configuration is developed to integrate variable‐resolution meshes with component advancementsThe two‐way river‐ocean coupling scheme developed in E3SM significantly improves the representation of river‐ocean interactionsThe new coupled E3SM configuration provides insights into the nonlinear interactions between storm surge and river discharge during compound flooding [ABSTRACT FROM AUTHOR]

Major precipitation events from destructive hurricanes can change the hydrodynamics of estuaries. Indeed, water rather than wind was the major destructive force behind Hurricane Harvey, the category 4 storm that stalled over Texas in August 2017. Herein, we present a unique examination of the phytoplankton community as it responded to the changing hydrodynamic conditions in Galveston Bay. We used traditional (primary productivity, pigments, Fluorescence Induction and Relaxation System, pulse-amplitude modulation fluorometer), and modern (Imagining FlowCytobot [IFCB], 16S rRNA, and 18S rRNA gene sequencing) approaches to characterize the phytoplankton community and their physiological response. Phytoplankton communities transitioned from pre-storm estuarine to freshwater species and then began to transition back to an estuarine community with similar but not identical structure to that present before the storm. Further, phytoplankton communities were initially displaced by the flood waters, which lowered biomass and productivity despite the high nutrient concentrations and dissolved inorganic nitrogen to phosphate ratios of ~ 16. We found for cyanobacteria, factors such as time (i.e., sampling event after the storm), temperature (°C), salinity, NH4+ (µM), NO3− (µM), and turbidity (Secchi disk depths and total suspended solids) were the most critical environmental factors that explained the variation in community composition relative to the major eukaryotic groups. Only factors such as time, followed by salinity, and NO3− played a crucial role for eukaryotes, with time having the most impact on diatoms. The results herein provide a greater understanding of the succession and natural variability of phytoplankton species following large flood events in subtropical estuaries, which are predicted to occur more frequently with increasing effects of climate change. [ABSTRACT FROM AUTHOR]

The impact of surface wave assimilation on hurricane track and intensity forecasts has been investigated using a fully coupled air-ocean-wave tropical cyclone data assimilation and forecast modeling system. A new 3DVAR wave assimilation method in the Navy Coupled Ocean Data Assimilation system (NCODA) maps the 1D wave energy spectra from buoys to 2D directional wave energy spectra using the maximum likelihood method (MLM) and corrects the wave model forecast component directional wave energy spectra. The Coupled Ocean/Atmosphere Mesoscale Prediction System for Tropical Cyclone Prediction (COAMPS-TC) is used to conduct three Hurricane Harvey (2017) airocean-wave coupled data assimilation and forecasting experiments with and without the wave data assimilation. Hurricane Harvey traversed through the Western Gulf of Mexico from 24 August to 1 September, 2017 and made landfall in the Texas and Louisiana coast. Validation of track, maximum wind speed, significant wave height, and mean absolute wave periods show wave assimilation of the 1D wave energy spectra from 13 National Data Buoy Center (NDBC) buoys reduced the forecast errors of these parameters compared to experiments without the wave assimilation. In spite of this positive outcome, the wave assimilation is unable to reduce Harvey's 0-120 h forecast mean wave direction errors and correlation compared to the NDBC buoy time series. [ABSTRACT FROM AUTHOR]

Parasites and infectious diseases constitute important challenges particularly for group-living animals. Social contact and shared space can both increase parasite transmission risk, while individual differences in social capital can help prevent infections. For example, high social status individuals and those with more or stronger affiliative partnerships may have better immunity and, thus, lower parasitic burden. To test for health trade-offs in the costs and benefits of sociality, we quantified how parasitic load varied with an individual's social status, as well as with their affiliative relationships with weakly and strongly bonded partners, in a free-ranging population of rhesus macaques, Macaca mulatta. We found that high status was associated with a lower risk of protozoa infection at older ages compared to younger and low-status animals. Social resources can also be protective against infection under environmentally challenging situations, such as natural disasters. Using cross-sectional data, we additionally examined the impact of a major hurricane on the sociality - parasite relationship in this system and found that the hurricane influenced the prevalence of specific parasites independent of sociality. Overall, our study adds to the growing evidence for social status as a strong predictor of infection risk and highlights how extreme environmental events could shape vulnerability and resistance to infection. • Old high-status animals had lower risk of protozoa infection. • Social status did not buffer infection risk after an acute natural disaster. • More connected individuals had lower infection risk. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Gaining timely and reliable situation awareness after hazard events such as a hurricane is crucial to emergency managers and first responders. One effective way to achieve that goal is through damage assessment. Recently, disaster researchers have been utilizing imagery captured through satellites or drones to quantify the number of flooded/damaged buildings. In this paper, we propose a mixed‐data approach, which leverages publicly available satellite imagery and geolocation features of the affected area to identify damaged buildings after a hurricane. The method demonstrated significant improvement from performing a similar task using only imagery features, based on a case study of Hurricane Harvey affecting Greater Houston area in 2017. This result opens door to a wide range of possibilities to unify the advancement in computer vision algorithms such as convolutional neural networks and traditional methods in damage assessment, for example, using flood depth or bare‐earth topology. In this work, a creative choice of the geolocation features was made to provide extra information to the imagery features, but it is up to the users to decide which other features can be included to model the physical behavior of the events, depending on their domain knowledge and the type of disaster. The data set curated in this work is made openly available (DOI: 10.17603/ds2‐3cca‐f398). [ABSTRACT FROM AUTHOR]

Recent advancements in technology and infrastructure have greatly improved the capabilities of the natural hazards community to collect robust samples of building performance following hazard events and make them available to the research community for broad reuse purposes. Yet, there lacks standardized, open access data sets that combine reconnaissance data from multiple individual hazard events into unified, living testbeds that can grow through community participation. The objectives of this study are to (1) synthesize and present a unified, multievent windstorm performance data set (WiSPD) and (2) summarize common damage patterns observed in the WiSPD. The WiSPD currently consists of four hurricanes (occurring between 2017 and 2020) and four tornadoes (occurring between 2011 and 2020), all of which struck the United States. Each event's building performance assessments were collected with similar methodologies and contain details such as location, physical address, basic building attributes, estimated 3 s gust wind speed, basic wind speeds, and component-level damage percentage with a precision of ±5%. In combination, the testbed reveals that roof cover damage dominates in windstorms, regardless of the year of construction or building code enforced. Additionally, tornadoes tend to produce higher damage rates than hurricanes, specifically in fenestration and roof structure damage. Fragility functions for hurricanes exhibit a nonmonotonic relationship between wind speed and damage, potentially evidencing the strong influence of other confounding variables. Ultimately, the unified data set promises to be a rich testbed for further knowledge discovery and model validation by the research community. [ABSTRACT FROM AUTHOR]

Natural disasters can cause physical damage and provide information about flood risk. We find that the prices of one to three family homes in New York City hit by high storm surges during Hurricane Sandy dropped by 16% and remained 12% lower than pre‐storm levels 6 years after the storm. Effects were concentrated in areas outside of pre‐existing flood zones, where risks were less salient, and they were more persistent in lower income areas. Finally, flooding may have changed neighborhood demographic trends, as post‐Sandy homebuyers in hard‐hit areas had lower incomes and were less likely to be white. [ABSTRACT FROM AUTHOR]

Summary: Many trees exhibit masting – where reproduction is temporally variable and synchronous over large areas. Several dominant masting species occur in tropical cyclone (TC)‐prone regions, but it is unknown whether TCs correlate with mast seeding.We analyzed long‐term data (1958–2022) to test the hypothesis that TCs influence cone production in longleaf pine (Pinus palustris). We integrate field observations, weather data, satellite imagery, and hurricane models to test whether TCs influence cone production via: increased precipitation; canopy density reduction; and/or mechanical stress from wind.Cone production was 31% higher 1 yr after hurricanes and 71% higher after 2 yr, before returning to baseline levels. Cyclone‐associated precipitation was correlated with increased cone production in wet years and cone production increased after low‐intensity winds (≤ 25 m s−1) but not with high‐intensity winds (> 25 m s−1).Tropical cyclones may stimulate cone production via precipitation addition, but high‐intensity winds may offset any gains. Our study is the first to support the direct influence of TCs on reproduction, suggesting a previously unknown environmental correlate of masting, which may occur in hurricane‐prone forests world‐wide. See also the Commentary on this article by Pearse & Wion, 242: 8–9. [ABSTRACT FROM AUTHOR]

Tropical cyclone rapid intensification (RI) prediction still remains a big international challenge in numerical weather prediction. Hurricane Matthew (2016) underwent extreme and non-classic RI, intensifying from a Category 1 storm to a Category 5 hurricane within 24 h under a strong vertical shear environment. However, most models failed to capture this RI, and limited or no inner core, and outflow observations were assimilated in the NWS operational HWRF Model before the onset of RI for Matthew (2016). The goals of the study are to (1) explore the best way to assimilate the High-Density Observations (HDOB, including FL and SFMR) and AMV data; (2) study the impact of assimilating these observations on the analysis of both the inner-core and outflow structures; and (3) examine the impact of assimilating these data on the prediction of RI for Matthew. The main results are as follows: (1) With proper pre-processing of the HDOB observations and by using a 4DEnVar method, the inner-core structure analysis was improved. And the RI prediction is more consistent with the best track without spin-down for the first 24 h. Assimilating CIMMS AMV observations on top of the HDOB observations further improves both the track and intensity forecasts. Specifically, both the magnitude and timing of the peak intensity are further improved. (2) Diagnostics are conducted to understand how the assimilation of these different types of observations impacts RI prediction. Without assimilating HODB and AMV data, baseline experimentover-predict the intensification rate during the first 18 h, but under-predict RI after 18 h. However, the assimilation of FL and SFMR and CIMMS AMV correctly weakens the upper-level outflow and improves the shear-relative structure of the inner-core vortex, such as reducing the low-level moisture in the downshear left quadrant. The deep convection on the downshear side is weaker than baseline for the first 18 h but keeps enhancing, later moving cyclonically to the USL quadrant, and then causes more subsidence warming, maximizing in the USL quadrant and the maximum wind increases faster. Moreover, the rapid intensification rate is much more consistent with the best track and the forecast skill of RI is improved. Therefore, 4DEnVar assimilation with proper pre-processing of the high-density observations can indeed correct the shear-relative moisture and structural distributions of both the inner core and environment for TCs imbedded in the stronger shear, which is important for shear-TC RI prediction. [ABSTRACT FROM AUTHOR]

Remote sensing techniques have emerged as an essential tool for conducting damage assessments and are commonly used to improve disaster recovery planning and community resilience policies. The objective of this study was to use aerial imagery data and LiDAR to identify the hardest hit areas, quantify the extent of damages, and compare pre- and post-storm beach morphology conditions in Estero Island, Florida, relating to Hurricane Ian in 2022. This study identified >2400 structures that were impacted by Hurricane Ian, with 170 structures suffering extensive damage. Clustering of heavily damaged buildings was observed on the northern and central portions of the island, with lower levels of damage clustered on the southern part. Among the 'severely damaged' and 'destroyed' structures were seven mobile home subdivisions. The total assessed value of the heavily damaged structures was estimated at over USD 200 million. The results also indicated substantial post-storm debris and sand deposition across the entire island. Remote sensing provides advanced techniques that can help prioritize emergency response efforts after catastrophic impacts from a natural disaster. [ABSTRACT FROM AUTHOR]

This study explores the resilience and damage dynamics of agroforests, a critically important yet understudied agroecological system, in the aftermath of Category-5 Cyclone Winston in Fiji. As agroforestry gains prominence globally as a versatile production system able to support agrobiodiversity and food security for climate resilience, understanding the characteristics that contribute to its resistance and resilience to disturbance becomes increasingly important. Here we examine the effects of individual and species-specific traits, and management (planted and fallow vs forest areas) on the probability of tree stem survival and damage, and discuss the resistant and resilient qualities of trees and management actions in these systems. We found that the probability of post-cyclone survival increased as a function of wood density, irrespective of management type. Damage severity increased with tree size (diameter at breast height). Some of the species with the highest wood density were native trees, emphasizing the role of native species in agroforests, and the value of agroforests to conservation. Overall, agroforest trees experienced relatively low stem mortality (12.2%), suggesting that these agroforests may resist extreme disturbances despite their potential vulnerabilities such as landscape edge effects and altered species compositions. Our study provides insight into the potential of agroforests as resilient agroecological systems capable of withstanding escalating cyclone intensities, and the role of effective management strategies for fostering resilience amid a rapidly changing climate. [ABSTRACT FROM AUTHOR]

Spaceborne synthetic aperture radar (SAR) Doppler shift measurements have been used for remote sensing of ocean surface currents during nonstorm events. However, mapping strong currents under storm conditions is still a challenging and unsolved issue. In this study, we attempt to retrieve radial current velocities from Sentinel-1A SAR Doppler shifts acquired over the outer core regions of Hurricane Maria for the first time. In these areas, the maximum wind speed is 28.7 m/s. Doppler shifts arising from the scalloping effect are first calculated using a linear fitting method. The nonzero Doppler shift measurements over the land within SAR scenes are then used to estimate Doppler shifts caused by antenna electronic mispointing and residual error. Finally, we compute sea-state-induced Doppler shifts (wave Doppler) based on our recently dual copolarization Doppler velocity (DPDop) model. The retrieved radial current velocities are compared with collocated high-frequency radar measurements, and show a bias of 0.02 m/s and a root-mean-square error of 0.19 m/s. These results suggest that it is possible to retrieve reliable radial current velocities under high wind conditions, as the contributions of nongeophysical terms and sea state to the Doppler shifts can be accurately estimated and removed.

While tropical cyclone regimes are shifting with climate change, the mechanisms underpinning the resistance (ability to withstand disturbance-induced change) and resilience (capacity to return to pre-disturbance reference) of tropical forest litterfall to cyclones remain largely unexplored pantropically. Single-site studies in Australia and Hawaii suggest that litterfall on low-phosphorus (P) soils is more resistant and less resilient to cyclones. We conducted a meta-analysis to investigate the pantropical importance of total soil P in mediating forest litterfall resistance and resilience to 22 tropical cyclones. We evaluated cyclone-induced and post-cyclone litterfall mass (g/m2 /day), and P and nitrogen (N) fluxes (mg/m2 /day) and concentrations (mg/g), all indicators of ecosystem function and essential for nutrient cycling. Across 73 case studies in Australia, Guadeloupe, Hawaii, Mexico, Puerto Rico, and Taiwan, total litterfall mass flux increased from ~2.5 ± 0.3 to 22.5 ± 3 g/m2 /day due to cyclones, with large variation among studies. Litterfall P and N fluxes post-cyclone represented ~5% and 10% of the average annual fluxes, respectively. Post-cyclone leaf litterfall N and P concentrations were 21.6 ± 1.2% and 58.6 ± 2.3% higher than pre-cyclone means. Mixed-effects models determined that soil P negatively moderated the pantropical litterfall resistance to cyclones, with a 100 mg P/kg increase in soil P corresponding to a 32% to 38% decrease in resistance. Based on 33% of the resistance case studies, total litterfall mass flux reached pre-disturbance levels within one-year post-disturbance. A GAMM indicated that soil P, gale wind duration and time post-cyclone jointly moderate the short-term resilience of total litterfall, with the nature of the relationship between resilience and soil P contingent on time and wind duration. Across pantropical forests observed to date, our results indicate that litterfall resistance and resilience in the face of intensifying cyclones will be partially determined by total soil P.

17O‐excess is a relatively new water isotope parameter that could potentially provide useful information about the hydrological cycle. Previous works focusing on 17O‐excess in polar regions suggest that it primarily tracks moisture source relative humidity, but little is known about how to interpret 17O‐excess data in lower latitudes. Here we present quasi‐hourly triple oxygen isotope data of precipitation collected from two tropical cyclones in Texas and Louisiana in 2020 to understand the impacts of environmental and meteorological processes on the 17O‐excess of low‐to mid‐latitude precipitation. We find that at both hourly timescales and the event scale, 17O‐excess is strongly correlated to changes in on‐site rainfall intensity and relative humidity, which is consistent with the theory that the isotopic fractionation associated with rain re‐evaporation lowers the 17O‐excess of the remaining droplet. In addition, although evaporative conditions at the moisture source region may also influence 17O‐excess of water vapor transported to the precipitation site, their impacts are likely overprinted by the post‐condensation rain re‐evaporation processes. Our results thus suggest that 17O‐excess can be used as a proxy for local rather than source region evaporative conditions during tropical cyclones. Plain Language Summary: The stable isotopic composition of water can be used to track the hydrologic cycle. 17O‐excess measures the relative abundance of the two less abundant stable isotope species of oxygen (17O and 18O), and is known to contain unique information about how humid the air is where the water comes from. However, most of such observations were done in polar regions. In order to understand how to use 17O‐excess in (sub‐) tropical regions correctly, we collected rainwater from two tropical cyclone storms in the southern United States in 2020. We discovered that during these storms, it was actually the humidity at the site of precipitation, rather than where the water came from, that drove changes in 17O‐excess values of precipitation. This is because rainwater tends to evaporate back into the air when the local condition is drier, making 17O‐excess values lower. Our study suggests that 17O‐excess should be interpreted as local humidity instead of the humidity at the moisture source in (sub‐) tropical regions. Key Points: Intra‐event 17O‐excess data is reported for two tropical cyclonesRe‐evaporation is the dominant control on 17O‐excess variability in the tropical cyclones observed in this study17O‐excess may be used as a proxy for assessing rain re‐evaporation in low‐to mid‐latitudes [ABSTRACT FROM AUTHOR]