Hurricane Center Research Articles

The Dynamics of Trust and Trustworthiness Perceptions in a Government Agency: A Longitudinal Perspective

Although trust is widely recognized as key to effective public administration, little is known about trust dynamics, especially in typical governance relationships. We conducted an intensive longitudinal experience sampling study via a weekly survey of Florida residents’ ( N = 97) trust in and perceptions of the trustworthiness of the (US) National Hurricane Center over a 25-week hurricane season. We estimated two-level time series models to examine reciprocal relationships among four constructs: trust, ability, benevolence, and integrity. All four exhibited significant intraindividual variability such that participants demonstrated change in their ratings of the agency over time. Ability perceptions showed the greatest variability over the study duration. In line with classic models of trust, previous-week trustworthiness perceptions were significant predictors of subsequent trust. Our results also provide some support for self-reinforcing accounts of trust by demonstrating that previous-week trust predicted subsequent trustworthiness. Several of the one-week lagged relationships weakened substantially when tested with two-week lags, suggesting that trustworthiness is the stronger driver of trust. Overall, our results demonstrate that trust is not static, even in business-as-usual governance relationships. Results position perceptions of ability, benevolence, and integrity as partially responsible for changes in trust over time. Further, an exploratory analysis suggested that people who reported higher average integrity perceptions tended to have more stable trust. Thus, all the dimensions of trustworthiness serve as short-term levers for administrators seeking to positively manage relationships with the public, and integrity may be most critical for supporting steady levels of trust.

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

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

Comparison of tropical cyclone structures over different ocean basins revealed in COSMIC-2 radio occultation observations

Utilizing three-years COSMIC-2 radio occultation (RO) bending angle (BA) observations from 1 October 2019 to 31 December 2022, composite structures of BA anomalies α−α¯env/α¯env in vertical and radial directions are generated for hurricanes, tropical storms and tropical depressions over the ocean basins of North Atlantic, West Pacific, East Pacific, South Pacific, North Indian, and South Indian. The magnitude and altitude of the maximum BA anomaly within hurricanes, tropical storms and tropical depressions are about (6 km, 14%), (5 km, 11%) and (3.5 km, 9%), respectively. The vertical and radial extents of BA anomaly exceeding 6% are (6 km, 450 km) and (7 km, 500 km) over North Atlantic and West Pacific oceans, respectively. The warm core of hurricane over East Pacific ocean displays the widest radial extent (650 km), while that over North India ocean has the deepest vertical extent (8.5 km). As expected, the BA anomalies of tropical storms and tropical depressions are weaker in magnitude and smaller in size than those of hurricanes. An exception is found for the maximum BA anomaly of hurricanes over South Pacific ocean is not located near hurricane centers but at the 175-km radial distance. Accordingly, the mean altitudes of the atmospheric boundary layer estimated from COSMIC-2 BA observations increase from the 175-km radial distances to both sides for hurricanes over South Pacific ocean, while those over the other five ocean basins increase from eye centers outward.

Are Forecasts of the Tropical Cyclone Radius of Maximum Wind Skillful?

AbstractThe radius of maximum wind (RMW) defines the location of the maximum winds in a tropical cyclone and is critical to understanding intensity change as well as hazard impacts. A comparison between the Hurricane Analysis and Forecast System (HAFS) models and two statistical models based off the National Hurricane Center official forecast is conducted relative to a new baseline climatology to better understand whether models have skill in forecasting the RMW of North Atlantic tropical cyclones. On average, the HAFS models are less skillful than the climatology and persistence baseline and two statistically derived RMW estimates. The performance of the HAFS models is dependent on intensity with better skill for stronger tropical cyclones compared to weaker tropical cyclones. To further improve guidance of tropical cyclone hazards, more work needs to be done to improve forecasts of tropical cyclone structure.

Lifetime Performance of the Operational Hurricane Weather Research and Forecasting Model (HWRF) for North Atlantic Tropical Cyclones

Abstract The Hurricane Weather Research and Forecasting Model (HWRF) was the flagship hurricane model at NOAA’s National Centers for Environmental Prediction for 16 years and a state-of-the-art tool for tropical cyclone (TC) intensity prediction at the National Weather Service and across the globe. HWRF was a joint development between NOAA research and operations, specifically the Environmental Modeling Center and the Atlantic Oceanographic and Meteorological Laboratory. Significant support also came from the National Hurricane Center, Developmental Testbed Center, University Corporation for Atmospheric Research, universities, cooperative institutes, and the TC community. In the North Atlantic basin, where most improvement efforts focused, HWRF intensity forecast errors decreased by 45%–50% at many lead times between 2007 and 2022. These large improvements resulted from increases in horizontal and vertical resolution, as well as advances in model physics and data assimilation. HWRF intensity forecasts performed particularly well over the Gulf of Mexico in recent years, providing useful guidance for a large number of impactful landfalling hurricanes. Such advances were made possible not only by significant gains in computing but also through substantial investment from the Hurricane Forecast Improvement Program.

Evaluation of WRF model configurations for dynamic downscaling of tropical cyclones activity over the North Atlantic basin for Lagrangian moisture tracking analysis in future climate

This study assessed five well-established physics suites of the Weather Research and Forecasting (WRF) model in operational forecasting systems in the North Atlantic (NATL) basin or from previous sensitive experiments for dynamic downscaling tropical cyclone (TC) activity. We performed long-term simulations for the 2020 TC season in the NATL and compared the WRF tracks against the HURDAT2 dataset from the US National Hurricane Center. Among the tested configurations, the analysis revealed that the Kain-Fritsch, Purdue Lin, BouLac and revised MM5 schemes for cumulus, microphysics, planetary boundary layer and surface layer, respectively (hereafter WT), outperformed all four others in terms of TC frequency, track density and intensity and showed good performance in the cyclone accumulated energy and TC landfalling locations. In addition, WT well-captured the spatial distribution of accumulated TC precipitation and moisture uptake patterns, although it overestimated the precipitation maxima. Likewise, it agreed on the relative moisture contribution from fixed moisture sources (i.e., Gulf of Mexico, Caribbean Sea, tropical NATL, and western NATL) in the NATL basin. Overall, this study highlighted the high potential of using the WT physics suite in WRF for downscaling TC activity over the NATL basin, which will be useful for TC Lagrangian moisture sources analysis in future climate.

Influence of Assimilation of NEXRAD-Derived 2D Inner-Core Structure Data from Single Radar on Numerical Simulations of Hurricane Charley (2004) near Its Landfall

This study presents the first research that assimilates the ground-based NEXRAD observations-derived two-dimensional (2D), azimuthally averaged radar radial velocity and reflectivity within 60 km of radius from the hurricane center to examine their influence on the analysis and prediction of a hurricane near and after its landfall. The mesoscale community Weather Research and Forecasting (WRF) model and its four-dimensional variational (4D-VAR) data assimilation system are utilized to conduct data assimilation experiments for Hurricane Charley (2004). Results show that assimilation of the radar inner-core data leads to better forecasts of hurricane tracks, intensity, and precipitation. The improved forecast outcomes imply that the changes in dynamical, thermal, and moisture structures from data assimilations made more reasonable conditions for the hurricane development near and after its landfall. Overall results indicate that the assimilation of the radar-derived 2D inner-core structure could be a feasible way to utilize the radar data for improved hurricane prediction.

Unravelling the role of tropical cyclones in shaping present species distributions.

Driven by climate change, tropical cyclones (TCs) are predicted to change in intensity and frequency through time. Given these forecasted changes, developing an understanding of how TCs impact insular wildlife is of heightened importance. Previous work has shown that extreme weather events may shape species distributions more strongly than climatic averages; however, given the coarse spatial and temporal scales at which TC data are often reported, the influence of TCs on species distributions has yet to be explored. Using TC data from the National Hurricane Center, we developed spatially and temporally explicit species distribution models (SDMs) to examine the role of TCs in shaping present-day distributions of Puerto Rico's 10 Anolis lizard species. We created six predictor variables to represent the intensity and frequency of TCs. For each occurrence of a species, we calculated these variables for TCs that came within 500 km of the center of Puerto Rico and occurred within the 1-year window prior to when that occurrence was recorded. We also included predictor variables related to landcover, climate, topography, canopy cover and geology. We used random forests to assess model performance and variable importance in models with and without TC variables. We found that the inclusion of TC variables improved model performance for the majority of Puerto Rico's 10 anole species. The magnitude of the improvement varied by species, with generalist species that occur throughout the island experiencing the greatest improvements in model performance. Range-restricted species experienced small, almost negligible, improvements but also had more predictive models both with and without the inclusion of TC variables compared to generalist species. Our findings suggest that incorporating data on TCs into SDMs may be important for modeling insular species that are prone to experiencing these types of extreme weather events.

The G-IV Inner Circumnavigation: A Story of Successful Organic Interactions Between Research and Operations at NOAA

Abstract This study describes the research-to-operations process leading to a recent change in tropical cyclone (TC) reconnaissance sampling patterns as well as observing-system experiments that evaluated the impact of that change on numerical weather prediction model forecasts of TCs. A valuable part of this effort was having close, multipronged connections between the TC research and operational TC prediction communities at the National Oceanic and Atmospheric Administration (NOAA). Related to this work, NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML) and National Hurricane Center (NHC) have a long history of close collaboration to improve TC reconnaissance. Similar connections between AOML and NOAA’s Environmental Modeling Center (EMC) also laid a foundation for the observing-system experiments conducted here. More specifically, AOML and NHC collaborated in 2018 to change how NHC uses NOAA’s Gulfstream-IV (G-IV) jet during TC synoptic surveillance missions. That change added a second circumnavigation at approximately 1.5° from TC centers, when possible. Preliminary experiments suggest that the change improved track forecasts, though the intensity results are more mixed. Despite the somewhat small sample size over a 3-yr period, the track improvement does agree with prior work. This effort has led to additional work to more fully examine G-IV sampling strategies.

Assessing Storm Surge Multiscenarios Based on Ensemble Tropical Cyclone Forecasting

AbstractEnsemble forecasting is a promising tool to aid in making informed decisions against risks of coastal storm surges. Although tropical cyclone (TC) ensemble forecasts are commonly used in operational numerical weather prediction systems, their potential for disaster prediction has not been maximized. Here we present a novel, efficient, and practical method to utilize a large ensemble forecast of 1,000 members to analyze storm surge scenarios toward effective decision making such as evacuation planning and issuing surge warnings. We perform the simulation of TC Hagibis (2019) using the Japan Meteorological Agency's (JMA) nonhydrostatic model. The simulated atmospheric predictions were utilized as inputs for a statistical surge model named the Storm Surge Hazard Potential Index to estimate peak surge heights along the central coast of Japan. We show that Pareto‐optimized solutions from an ensemble storm surge forecast can describe potential worst (maximum) and optimum (minimum) storm surge scenarios. These solutions exemplify a diversity of trade‐off surge outcomes across diverse coastal locations, reflecting variations in coastal geometry, including bathymetry. For example, some of the Pareto‐optimized solutions that illustrate worst surge scenarios for inner bay locations are not necessarily accountable for bringing severe surge cases in open coasts. We further emphasize that an in‐depth evaluation of Pareto‐optimal solutions can shed light on how meteorological variables such as track, intensity, and size of TCs influence the worst and optimum surge scenarios, which is not clearly quantified in current multiscenario assessment methods such as those used by JMA/National Hurricane Center in the United States.

Operational Storm Surge Forecasting at the National Hurricane Center: The Case for Probabilistic Guidance and the Evaluation of Improved Storm Size Forecasts Used to Define the Wind Forcing

Abstract The primary source of guidance used by the Storm Surge Unit (SSU) at the National Hurricane Center (NHC) for issuing storm surge watches and warnings is the Probabilistic Tropical Storm Surge model (P-Surge). P-Surge is an ensemble of Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model forecasts that is generated based on historical error distributions from NHC official forecasts. A probabilistic framework is used for operational storm surge forecasting to account for uncertainty related to the tropical cyclone track and wind forcing. Previous studies have shown that the size of a storm’s wind field is an important factor that can affect storm surge. A simple radius of maximum wind (RMW) prediction scheme was developed to forecast RMW based on NHC forecast parameters. Verification results indicate this scheme is an improvement over the RMW forecasts used by previous versions of P-Surge. To test the impact of the updated RMW forecasts in P-Surge, retrospective cases were selected from 25 storms from 2008 to 2020 that had an adequate number of observations. Evaluation of P-Surge forecasts using these improved RMW forecasts shows that the probability of detection is higher for most probability of exceedance thresholds. In addition, the forecast reliability is improved, and there is an increase in the number of high probability forecasts for extreme events at longer lead times. The improved RMW forecasts were recently incorporated into the operational version of P-Surge (v2.9), and serve as an important step toward extending the lead time of skillful and reliable storm surge forecasts.

ProxyVis—A Proxy for Nighttime Visible Imagery Applicable to Geostationary Satellite Observations

Abstract Visible satellite imagery is widely used by operational weather forecast centers for tropical and extratropical cyclone analysis and marine forecasting. The absence of visible imagery at night can significantly degrade forecast capabilities, such as determining tropical cyclone center locations or tracking warm-topped convective clusters. This paper documents ProxyVis imagery, an infrared-based proxy for daytime visible imagery developed to address the lack of visible satellite imagery at night and the limitations of existing nighttime visible options. ProxyVis was trained on the VIIRS day/night band imagery at times close to the full moon using VIIRS IR channels with closely matching GOES-16/17/18, Himawari-8/9, and Meteosat-9/10/11 channels. The final operational product applies the ProxyVis algorithms to geostationary satellite data and combines daytime visible and nighttime ProxyVis data to create full-disk animated GeoProxyVis imagery. The simple versions of the ProxyVis algorithm enable its generation from earlier GOES and Meteosat satellite imagery. ProxyVis offers significant improvement over existing operational products for tracking nighttime oceanic low-level clouds. Further, it is qualitatively similar to visible imagery for a wide range of backgrounds and synoptic conditions and phenomena, enabling forecasters to use it without special training. ProxyVis was first introduced to National Hurricane Center (NHC) operations in 2018 and was found to be extremely useful by forecasters becoming part of their standard operational satellite product suite in 2019. Currently, ProxyVis implemented for GOES-16/18, Himawari-9, and Meteosat-9/10/11 is being used in operational settings and evaluated for transition to operations at multiple NWS offices and the Joint Typhoon Warning Center. Significance Statement This paper describes ProxyVis imagery, a new method for combining infrared channels to qualitatively mimic daytime visible imagery at nighttime. ProxyVis demonstrates that a simple linear regression can combine just a few commonly available infrared channels to develop a nighttime proxy for visible imagery that significantly improves a forecaster’s ability to track low-level oceanic clouds and circulation features at night, works for all current geostationary satellites, and is useful across a wide range of backgrounds and meteorological scenarios. Animated ProxyVis geostationary imagery has been operational at the National Hurricane Center since 2019 and is also currently being transitioned to operations at other NWS offices and the Joint Typhoon Warning Center.

User-Responsive Diagnostic Forecast Evaluation Approaches: Application to Tropical Cyclone Predictions

Abstract The Hurricane Forecast Improvement Project (HFIP; renamed the “Hurricane Forecast Improvement Program” in 2017) was established by the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 with a goal of improving tropical cyclone (TC) track and intensity predictions. A major focus of HFIP has been to increase the quality of guidance products for these parameters that are available to forecasters at the National Weather Service National Hurricane Center (NWS/NHC). One HFIP effort involved the demonstration of an operational decision process, named Stream 1.5, in which promising experimental versions of numerical weather prediction models were selected for TC forecast guidance. The selection occurred every year from 2010 to 2014 in the period preceding the hurricane season (defined as August–October), and was based on an extensive verification exercise of retrospective TC forecasts from candidate experimental models run over previous hurricane seasons. As part of this process, user-responsive verification questions were identified via discussions between NHC staff and forecast verification experts, with additional questions considered each year. A suite of statistically meaningful verification approaches consisting of traditional and innovative methods was developed to respond to these questions. Two examples of the application of the Stream 1.5 evaluations are presented, and the benefits of this approach are discussed. These benefits include the ability to provide information to forecasters and others that is relevant for their decision-making processes, via the selection of models that meet forecast quality standards and are meaningful for demonstration to forecasters in the subsequent hurricane season; clarification of user-responsive strengths and weaknesses of the selected models; and identification of paths to model improvement. Significance Statement The Hurricane Forecast Improvement Project (HFIP) tropical cyclone (TC) forecast evaluation effort led to innovations in TC predictions as well as new capabilities to provide more meaningful and comprehensive information about model performance to forecast users. Such an effort—to clearly specify the needs of forecasters and clarify how forecast improvements should be measured in a “user-oriented” framework—is rare. This project provides a template for one approach to achieving that goal.

Feasibility of Wave Simulation in Typhoon Using WAVEWATCH-III Forced by Remote-Sensed Wind

The purpose of our work was to assess the feasibility of hindcasting waves using WAVEWATCH-III (WW3) in a typhoon by assembling winds from multiple remote-sensed products. During the typhoon season in 2021–2022, the swath wind products in the Western Pacific Ocean were collected from scatterometers and radiometers. Cyclonic winds with a spatial resolution of 0.125° at intervals of 6 h were obtained by assembling the remote-sensed winds from those satellites. The maximum wind speeds, Vmax, were verified using the reanalysis data from the National Hurricane Center (NHC), yielding a root-mean-squared error (RMSE) of 4.79 m/s and a scatter index (SI) value of 0.2. The simulated wave spectrum was compared with the measurements from Surface Waves Investigation and Monitoring (SWIM) carried out on the Chinese–French Oceanography Satellite (CFOSAT), yielding a correlation coefficient (Cor) of 0.80, squared error (Err) of 0.49, RMSE of significant wave height (SWH) of 0.48 m with an SI of 0.25, and an RMSE of the peak wave period (PWP) of 0.95 s with an SI of 0.10. The bias of wave (WW3 minus European Centre for Medium-Range Weather Forecasts (ECMWFs) reanalysis (ERA-5)) concerning the bias of wind (assembling minus ERA-5) showed that the WW3-simulated SWH with the assembling wind forcing was significantly higher than that with the ERA-5 wind forcing. Moreover, the bias of SWH gradually increased with an increasing bias of wind speed; i.e., the bias of SWH increased up to 4 m as the bias of wind speed reached 30 m/s. It was concluded that the assembling wind from multiple scatterometers and radiometers is a promising source for wave simulations via WW3 in typhoons.

Rapid Reinstatement of Cancer Care after Hurricane Ian

Category 4 Hurricane Ian brought significant destruction and challenges to western Florida's healthcare facilities. A Radiation Oncology practice network serving the area minimized interruptions in care by implementing planned and improvised solutions. Examining actions that allowed rapid recovery of care could assist others in disaster response. The National Hurricane Center issued a hurricane warning 36 hours prior to Ian's landfall. Center closures were based on evacuations and scheduled take down of the power grid. Computers were unplugged, elevated and wrapped in plastic. The LINAC was isolated from the power grid at the main breaker. Staff and patient contacts were printed. Communication techniques were agreed upon for post-storm scenarios with or without cellular service and internet. Hurricane Ian made landfall on September 28 with 150 mph sustained winds and gusts of 216 mph. Unsafe conditions prevented action on September 28. Facility assessment began on September 29. The majority of facilities were inaccessible due to impassible roads and entry hazards, or could not be safely occupied. Based on location and condition, one center from the network of facilities was selected for immediate operational restoration. After confirmation that the site's restored power would remain stable, the LINAC was powered on. An onsite engineering evaluation was done to assess machine functionality and vacuum integrity. Medical Physics then performed monthly QA and isocenter verification due to concern for building vibrations from extreme winds. Interfacing with patient charts in the Aria database was more complex than anticipated. Aria is a hub that uses relative paths to refer to plans, structure sets, images, and radiotherapy treatment records stored in remote servers. These Aria servers could not be accessed because the remote sites had no power. As the Aria servers could not be moved, teams went to retrieve the related Adaptivo servers to allow re-import of patient plans. When booted these servers had no common network and no graphical user interface. Using command line tools, the servers were added to the hub network and IP addresses were assigned. MS-DOS command Robocopy allowed a large-scale resumable transfer from Adaptivo patient folders to the Eclipse import directory. Plan validation and QA followed. Records could not be updated with delivered treatments, necessitating reconciliation of total dose when normal operations resumed. Within 72 hours of Ian passing, treatment resumed for all patients within the cancer center network who were able to access the restored site. Within days, seven of the eight regional centers were operational and patients were redistributed. The unexpected challenges posed by data and network access suggest that improving data location and back-up networks could facilitate center response and resiliency in future disasters.

The Development and Evaluation of a Tropical Cyclone Probabilistic Landfall Forecast Product

Abstract Improving estimates of tropical cyclone forecast uncertainty remains an important goal of the Hurricane Forecast Improvement Project (HFIP). Intensity forecast uncertainty near landfall is especially complicated because intensity forecasts depend on track forecasts. Ensembles can be difficult to interpret near land due to differences in both spatial and temporal resolution and differences in landfall timing (if at all) and location. The Monte Carlo Wind Speed Probability (WSP) model is a statistical ensemble based on the error characteristics of forecasts by the National Hurricane Center (NHC) and the spread of several track forecast models. The landfall distribution product (LDP) introduced in this paper was developed to use the statistical ensemble of forecasts from the WSP model to estimate both the track and intensity forecast uncertainty associated with potential landfalls. The LDP includes probabilistic intensity estimates as well as estimates of the most likely and reasonable strongest intensity at landfall. These products could communicate concise intensity uncertainty information to users at risk for tropical cyclone impacts. Demonstration on a retrospective dataset from 2010 to 2018 and evaluation of the LDP on the 2020–21 Atlantic hurricane seasons shows that the probability of landfall and the landfall intensity probabilities generated by the WSP model are reliable and potentially useful for preparedness decision-making. A case study of Hurricane Ida (2021) highlights how the LDP can be implemented to communicate landfall uncertainty to a broad range of users. Significance Statement With the new landfall distribution product (LDP), the National Hurricane Center can provide both track and intensity forecast uncertainty surrounding the landfall of hurricanes. The issuance of a reasonable worst case scenario for the strongest winds that could impact a region could amplify messaging to encourage people to take appropriate action prior to a landfall.

Hope, optimism, and self-efficacy predicting mental health andillness in a community sample exposed to Hurricane Harvey.

In 2017, Hurricane Harvey flooded more than 300,000 buildings causing an estimated $125 billion in damages and resulting in 68 deaths (National Hurricane Center). This actual or threatened loss of life and physical harm led many to report negative effects on mental well-being and greater mental illness. However, many individuals have been able to experience similar adverse events without a significant negative impact on their mental health and well-being. Positive thinking factors such as hope, optimism, and self-efficacy have been proposed as protective factors in the face of difficult life events. Hope, optimism, and self-efficacy are related but distinct constructs that have often been studied separately, but whose unique impact on well-being and mental illness is less clear, especially in the context of a natural hazard. The current study uses structural equation modeling to measure the unique contribution of hope, optimism, and hurricane-coping self-efficacy on mental well-being and mental illness in a community sample of 300 subjects who experienced Hurricane Harvey, recruited from Mechanical Turk.

The 2022 Eastern Pacific Hurricane Season An Above Average Season with Five Landfalls

Click to increase image sizeClick to decrease image size AcknowledgementsThe cyclone summaries are based on Tropical Cyclone Reports prepared by NHC hurricane specialists Robbie Berg, Jack Beven, Eric Blake, Daniel Brown, Lisa Bucci, John Cangialosi, Andrew Latto, Philippe Papin, Richard Pasch, and Brad Reinhart. These reports are available on the Internet at: https://www.nhc.noaa.gov/data/tcr/index.php?season=2022&basin=epac.Additional informationNotes on contributorsEric BlakeERIC S. BLAKE is a senior hurricane specialist at NOAA's National Hurricane Center (NHC) in Miami, Florida

Hurricane boundary layer height distribution from dropsonde composites based on the bulk Richardson number method

The distribution of hurricane boundary layer heights (HBLHs) relative to the hurricane center is investigated in a composite framework, using total 2674 dropsondes collected within four times the radius of maximum wind speed (RMW) of 61 hurricanes during 2002–2020. The HBLH is computed by the bulk Richardson number (Rib) method, which is sensitive to the critical Rib choice and more reasonable when the surface friction effect is considered. The sea surface friction velocity is derived from the 10-m wind speed through an air-sea drag relation. The results show that the HBLH is significantly low in the eye zone (0–0.5 RMW), out of which the HBLH is highest in the gale region (0.5–1.5 RMW) and decreases with the radius to the hurricane center. Similar to the hurricane wind field asymmetry, the HBLH is generally higher in the right than in the left in relation to the hurricane motion direction. ERA5 generally overestimates the HBLH, but substantially underestimates the higher HBLHs around the RMW, showing different variation of HBLHs along with the radius compared to dropsonde observations. According to linear regression analyses, the HBLH at least determined by the Rib method is importantly affected by the hurricane boundary layer dynamic factor, while has no significant correlation with the thermal factor.

Structural Forecasting for Short-Term Tropical Cyclone Intensity Guidance

Abstract Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to short-term probabilistic forecasts of TC convective structure to subsequently predict TC intensity. Here, we present a prototype model that is trained solely on two inputs: Geo infrared imagery leading up to the synoptic time of interest and intensity estimates up to 6 h prior to that time. To estimate future TC structure, we compute cloud-top temperature radial profiles from infrared imagery and then simulate the evolution of an ensemble of those profiles over the subsequent 12 h by applying a deep autoregressive generative model (PixelSNAIL). To forecast TC intensities at hours 6 and 12, we input operational intensity estimates up to the current time (0 h) and simulated future radial profiles up to +12 h into a “nowcasting” convolutional neural network. We limit our inputs to demonstrate the viability of our approach and to enable quantification of value added by the observed and simulated future radial profiles beyond operational intensity estimates alone. Our prototype model achieves a marginally higher error than the National Hurricane Center’s official forecasts despite excluding environmental factors, such as vertical wind shear and sea surface temperature. We also demonstrate that it is possible to reasonably predict short-term evolution of TC convective structure via radial profiles from Geo infrared imagery, resulting in interpretable structural forecasts that may be valuable for TC operational guidance. Significance Statement This work presents a new method of short-term probabilistic forecasting for tropical cyclone (TC) convective structure and intensity using infrared geostationary satellite observations. Our prototype model’s performance indicates that there is some value in observed and simulated future cloud-top temperature radial profiles for short-term intensity forecasting. The nonlinear nature of machine learning tools can pose an interpretation challenge, but structural forecasts produced by our model can be directly evaluated and, thus, may offer helpful guidance to forecasters regarding short-term TC evolution. Since forecasters are time limited in producing each advisory package despite a growing wealth of satellite observations, a tool that captures recent TC convective evolution and potential future changes may support their assessment of TC behavior in crafting their forecasts.