Saffir-Simpson Research Articles

Downscaling, bias correction, and spatial adjustment of extreme tropical cyclone rainfall in ERA5 using deep learning

Hydrological models that are used to analyse flood risk induced by tropical cyclones often input ERA5 reanalysis data. However, ERA5 precipitation has large systematic biases, especially over heavy precipitation events like Tropical Cyclones, compromising its usefulness in such scenarios. Few studies to date have performed bias correction of ERA5 precipitation and none of them for extreme rainfall induced by tropical cyclones. Additionally, most existing works on bias adjustment focus on adjusting pixel-wise metrics of bias, such as the Mean Squared Error (MSE). However, it is equally important to ensure that the rainfall peaks are correctly located within the rainfall maps, especially if these maps are then used as input to hydrological models. In this paper, we describe a novel machine learning model that addresses both gaps, RA-Ucmpd, based on the popular U-Net model. The key novelty of RA-Ucmpd is its loss function, the compound loss, which optimizes both a pixel-wise bias metric (the MSE) and a spatial verification metric (a modified version of the Fractions Skill Score). Our results show how RA-Ucmpd improves ERA5 in almost all metrics by 3-28%—more than the other models we used for comparison which actually worsen the total rainfall bias of ERA5—at the cost of a slightly increased (3%) error on the magnitude of the peak. We analyse the behaviour of RA-Ucmpd by visualizing accumulated maps of four particularly wet tropical cyclones and by dividing our data according to the Saffir-Simpson scale and to whether they made landfall, and we perform an error analysis to understand under what conditions our model performs best.

Projecting Future Tropical Cyclone Frequencies by Combining Uncertain Empirical Estimates of Baseline Frequencies with Climate Model Estimates of Change

Various studies have given projections for how frequencies of tropical cyclones (TCs) might change under climate change. In this study, we combine a set of such projections with uncertain estimates of frequencies of tropical cyclones in a baseline climate to produce probabilistic projections of tropical cyclone frequencies for the next 50 years. The novel aspect of our projections is the inclusion of baseline uncertainty. We consider frequencies of Saffir-Simpson Hurricane Wind Scale category 0-5 and category 4-5 storms for the six major tropical cyclone basins. We find that in several cases the means and medians of the frequency of category 0-5 storms are projected to decrease, but that increasing uncertainty nevertheless leads to increases in the likelihood of high rates of TC activity in the future. We then show how the variance of the distributions of uncertainty can be decomposed into terms due to baseline uncertainty and climate change uncertainty. We use this decomposition to determine the year in which climate change uncertainty overtakes baseline uncertainty. Over the next 20 years we find that in some basins baseline uncertainty dominates and in other basins climate change uncertainty dominates. We are able to relate these variations between basins to the coefficients of variation of the baseline and climate change inputs. Finally, we quantify how climate change affects estimates of near-term TC frequency, including the extent to which it increases the uncertainty. These results help us understand two of the major sources of uncertainty in estimates of tropical cyclone behaviour now and in the future, and the role climate change is playing in changing that behaviour. They also demonstrate how estimates of TC change can be combined with observations to create projections of future TC climate.

Assessment of Seasonal Distribution and Characterisation of Geomagnetic Storm Occurrence during Solar Cycles 21–24

Geomagnetic storms (GMSs) are an important space weather phenomenon that poses serious threats to the advancement of space technology, power transmission lines, oil pipelines, and other infrastructure. This study investigates seasonal patterns of GMSs due to recent reports on the prominence of large storms (Dst ? -50 nT) during equinox conditions. Hourly Dst index data provided by the World Data Center, Kyoto, Japan, for solar cycles 21–24 (1976–2019) were employed. Storm occurrences in each solar cycle considered were identified using the minimum Dst value. The identified storms were categorized and analyzed statistically. Results revealed that storm occurrence varied from month to month, season to season, and solar cycle to solar cycle based on storm categories. Furthermore, the observed seasonal distribution of GMS occurrence decreases in the following order: autumn, spring, winter, and summer. This indicates that equinox conditions are more likely to have GMSs, consistent with the Russell-McPherron effect, compared to solstice conditions. The findings suggest that the distribution and characterization of storm occurrence vary seasonally due to solar activity. The insights on storm occurrence, distribution, and characterization may serve as a guide to space scientists to avert the impacts of GMSs while exploring space.

Memristor-based circuit design of episodic memory neural network and its application in hurricane category prediction

Episodic memory, as a type of long-term memory (LTM), is used to learn and store the unique personal experience. Based on the episodic memory biological mechanism, this paper proposes a bionic episodic memory memristive neural network circuit. The proposed memristive neural network circuit includes a neocortical module, a parahippocampal module and a hippocampus module. The neocortical module with the two paths structure is used to receive the sensory signal, and is also used to separate and transmit the spatial information and the non-spatial information involved in the sensory signal. The parahippocampal module is composed of the parahippocampal cortex-MEA and the perirhinal cortex-LEA, which receives the two types of information from the neocortical module respectively. As the last module, the hippocampus module receives and integrates the output information of the parahippocampal module as well as generates the corresponding episodic memory. Meanwhile, the specific scenario information with the certain temporal signal from the generated episodic memory is also extracted by the hippocampus module. The simulation results in PSPICE show that the proposed memristive neural network circuit can generate the various episodic memories and extract the specific scenario information successfully. By configuring the memristor parameters, the proposed bionic episodic memory memristive neural network circuit can be applied to the hurricane category prediction, which verifies the feasibility of this work.

Hurricane season hindsight 2020: Applying the IDEA model toward local tropical cyclone forecasts.

Hurricane Laura began as a disorganized tropical depression in August 2020. Early forecast guidance showed that the tropical cyclone could either completely dissipate or strengthen to a major hurricane as it approached the United States Gulf Coast. While this uncertainty was known by meteorologists, it was not necessarily communicated to the public in a direct manner. As it turned out, the worst-case scenario was the correct one. The tropical depression rapidly intensified and made landfall near Cameron, Louisiana, with sustained winds of 150 mph, making Laura a Category 4 hurricane on the Saffir-Simpson scale. Laura's rapid intensification caught some people off guard. Ideally, weather forecasts would have begun warning Louisiana residents to prepare for the possibility of a devastating hurricane in the early stages of tropical cyclone development. No one is suggesting that meteorologists did anything wrong. However, with the benefit of hindsight and decades of scholarly research in risk communication, we can speculate how an ideal forecast would have been written. This paper demonstrates that there are some simple considerations that could be made that might better alert the public to future hurricane worst-case scenarios, even in uncertain situations.

The growing inadequacy of an open-ended Saffir–Simpson hurricane wind scale in a warming world

Global warming increases available sensible and latent heat energy, increasing the thermodynamic potential wind intensity of tropical cyclones (TCs). Supported by theory, observations, and modeling, this causes a shift in mean TC intensity, which tends to manifest most clearly at the greatest intensities. The Saffir-Simpson scale for categorizing damage based on the wind intensity of TCs was introduced in the early 1970s and remains the most commonly used metric for public communication of the level of wind hazard that a TC poses. Because the scale is open-ended and does not extend beyond category 5 (70 m/s windspeed or greater), the level of wind hazard conveyed by the scale remains constant regardless of how far the intensity extends beyond 70 m/s. This may be considered a weakness of the scale, particularly considering that the destructive potential of the wind increases exponentially. Here, we consider how this weakness becomes amplified in a warming world by elucidating the past and future increases of peak wind speeds in the most intense TCs. A simple extrapolation of the Saffir-Simpson scale is used to define a hypothetical category 6, and we describe the frequency of TCs, both past and projected under global warming, that would fall under this category. We find that a number of recent storms have already achieved this hypothetical category 6 intensity and based on multiple independent lines of evidence examining the highest simulated and potential peak wind speeds, more such storms are projected as the climate continues to warm.

Relating Tropical Cyclone Intensification Rate to Precipitation and Convective Features in the Inner Core

Abstract Using Tropical Rainfall Measuring Mission Microwave Imager observations of global tropical cyclones (TCs) from 1998 to 2013, relationships between TC intensification rate and inner-core convective and precipitation parameters are examined by decoupling the dependency of these parameters on TC intensity and that on TC intensification rate. A total of 16 TC intensity change–intensity categories are categorized based on the initial intensity and 24-h future intensity change. The results show that the TC inner-core mean rain rate, convective intensity, and stratiform rain occurrence, and axisymmetric index of convective intensity increase significantly with TC intensification rate for each TC intensity category. The symmetry of rain rate and stratiform rainfall occurrence also increase significantly with TC intensification rate for each intensity category, except from slowly intensifying (SI) to rapidly intensifying (RI) group when the initial intensity is major hurricane. The RI major hurricanes have significantly more asymmetric rainfall distribution and distribution of stratiform rainfall occurrence than those of SI major hurricanes. For TCs with initial intensity in tropical depression, tropical storm, and major hurricane categories, the RI group has a significantly more asymmetric pattern of shallow precipitation/convection occurrence in the inner core than the SI group, while it has a significantly more symmetric pattern of deep convection occurrence than the SI group. The inner-core size, as quantified by the radius of maximum azimuthal mean rainfall decreases with both TC intensification rate and TC intensity.

Spatiotemporal changes in hurricane-force wind risk assessment in the Yucatan Peninsula, Mexico

Tropical cyclones are one of the most dangerous natural phenomena. These extreme events involve various hazards, such as strong winds, severe precipitation, storm surge, flooding, and landslides. In Mexico, tropical cyclones are the most frequent natural threats and have a high cost to affected populations. This research aimed to characterise the spatial and temporal changes in risk associated with hurricane winds on the Yucatan Peninsula. This effort included a comprehensive analysis of three integral risk components (hazard, vulnerability, and exposure) for three distinct time intervals (1950–2000, 1950–2010, and 1950–2020). This analytical process was executed utilising a fine-resolution hexagonal grid. Hazard was estimated by calculating the probabilities of occurrence of winds related to various hurricane categories after estimating wind fields from the International Best Track Archive for Climate Stewardship (IBTrACS) data with a parametric model. Vulnerability was approximated by constructing indicators with sociodemographic data from the National Population and Housing Census issued by Mexico’s National Institute of Statistics and Geography. With these indicators, a factor analysis was performed, and a weighted index was constructed. Finally, exposure was estimated from population density. Each of these indices was aggregated at the hexagonal level, allowing the calculation of the risk associated with hurricane-force wind. The results showed high-risk levels associated with high-hazard levels, e.g. in coastal areas such as the Riviera Maya. Similarly, high-risk levels are related to high marginalisation, i.e. vulnerability, in the northeastern zone of the Yucatan Peninsula. The increased frequency of tropical cyclones combined with high population densities has recently led to higher risk levels in this region of Mexico.

Resilience in Adversity: Acapulco and the Aftermath of Otis Hurricane Category 5 of October 25, 2023

Acapulco, a global tourist destination, faces challenges from frequent hydrometeorological events. This article examines its vulnerability and governmental responses to hurricanes. The study focuses on analyzing resilience in adversity through the case of Acapulco after Hurricane Otis category 5 from October 25, 2023. The objectives were to synthesize vulnerability and governmental responses to natural events in Acapulco, emphasizing the importance of sustainability and resilience. The methodology involved collection official data on climatic events, economic losses, and governmental responses. The evolution of the population and affected infrastructure was analyzed, focusing on specific cases such as Hurricane Otis. The results reveal an increase in vulnerability due to population growth and urban development without considering climatic risks. Despite governmental efforts in hurricane response, a lack of preparedness and risk communication impacted the city’s resilience. Therefore, the study will aim to extract lessons learned from the experience of Acapulco to provide practical recommendations for enhancing resilience in other communities facing similar disasters. Conclusions are as follows: 1) Acapulco’s vulnerability is heightened by growth without considering climatic risks; 2) governmental responses, though coordinated, need improvement in preparedness and risk communication; and 3) the key to sustainability lies in joining efforts, reducing vulnerability, and promoting resilience to extreme climatic events and 4) informing about the future strategy for disaster management and reconstruction in affected communities.

Examining the dynamics of a Borneo vortex using a balance approximation tool

Abstract. Cyclonic vortices that are weaker than tropical storm category can bring heavy precipitation as they propagate across the South China Sea and surrounding countries. Here we investigate the structure and dynamics responsible for the intensification of a Borneo vortex that moved from the north of Borneo across the South China Sea and impacted Vietnam and Thailand in late October 2018. This case study is examined using Met Office Unified Model (MetUM) simulations and a semi-geotriptic (SGT) balance approximation tool. Satellite observations and a MetUM simulation with 4.4 km grid initialised at 12:00 UTC on 21 October 2018 show that the westward-moving vortex is characterised by a coherent maximum in total column water and by a comma-shaped precipitation structure with the heaviest rainfall to the northwest of the circulation centre. The Borneo vortex comprises a low-level cyclonic circulation and a mid-level wave embedded in the background easterly shear flow, which strengthens with height up to around 7 km. Despite being in the tropics at 6∘ N, the low-level vortex and mid-level wave are well represented by SGT balance dynamics. The mid-level wave propagates along a vertical gradient in moist stability, i.e. the product between the specific humidity and the static stability, at 4.5 to 5 km and is characterised by a coherent signature in the potential vorticity, meridional wind, and balanced vertical velocity fields. The vertical motion is dominated by coupling with diabatic heating and is shifted relative to the potential vorticity so that the diabatic wave propagates westwards, relative to the flow, at a rate consistent with prediction from moist semi-geostrophic theory. Initial vortex development at low levels is consistent with baroclinic growth initiated by the mid-level diabatic Rossby wave, which propagates on baroclinic shear flow on the southern flank of a large-scale cold surge.

Post-Disaster Reconstruction of Residential Buildings: Evolution of Structural Vulnerability on Caribbean Island of Saint Martin after Hurricane Irma

This article presents a summary of the results obtained as part of the ANR (French National Research Agency)-RELEV project, which focuses on the long-term recovery and reconstruction of the island of Saint Martin following the passage of Hurricane Irma in 2017. This hurricane was classified as category five on the Saffir-Simpson scale, with an average wind speed of 287 km/h. It caused catastrophic damage along its path and highlighted the significant vulnerability of Caribbean societies to this type of phenomenon. This article focuses on the reconstruction of residential buildings on the French part of the island of Saint Martin. It aims to identify and analyze the factors that have favorably or unfavorably influenced their reconstruction and their structural vulnerability reduction. The research is based mainly on a series of interviews with local actors (construction and insurance companies, architects, territorial services, etc.), an online survey of residents (180 responses), and a field survey involving visits to 104 buildings with interviews of the occupants. The results obtained show that having access to financial resources for the reconstruction of buildings is central. However, different parameters must be considered to understand the disparity of situations and identify the factors that have most favorably contributed to the speed and quality of reconstruction and reduction of vulnerability. Even five years after Irma, a significant number of buildings on the island remain either unrepaired or abandoned. These buildings nevertheless constitute a danger in the case of strong winds (becoming a source of projectiles) and have a negative impact on the reputation and attractiveness of the island. The results reveal that in general, buildings in Saint Martin are slightly more resilient than they were prior to Irma, while presenting a great heterogeneity of situations.

A RETROFIT SOLUTION FOR UNCODED STEEL RAFTER TO CONCRETE RING BEAM CONNECTIONS

Some building contractors in the Caribbean have been constructing non-coded steel rafter to concrete ring beam connections on low budget residential buildings. These connections may resist failure under normal loading conditions, but critical connections fail under moderate to high loads resulting in partial or full dislodgement of the roof. In this study, the performance of non-coded steel rafter to concrete ring beam designs were analyzed in a finite element program and a retrofit solution for the failed uncoded connections were designed and modeled. Nine versions of the connection were identified and consisted of several variations including mechanically anchoring the rebar to the rafter without welds, using one and two rebars, varying the rebar embedment depth and using 90 degree hooks and straight rebars. A standard, code-compliant, bolted-endplate connection was used as the control for the investigation. The connections were analyzed in “ABAQUS” with a bond slip and damage plasticity model. The maximum load resistances were compared with category 1 hurricane loads for the region. The load capacities of all the connections were found to be lower than the control connection but were still adequate for hurricane category 1 loads, with the exception of the connection in which the rebars were mechanically anchored to the rafter without welds. Connections with welds to the top flange of the rafter were less stiff, experienced larger rafter displacements, inflicted less damage on the concrete ring beam, and required less rebar embedment to prevent pull out failure than the connections to the bottom flange.

Understanding the Effects of Wind Intensity, Forward Speed, and Wave on the Propagation of Hurricane Harvey Surges

Hurricane storm surges are influenced by wind intensity, forward speed, width and slope of the ocean bottom, central pressure, angle of approach, shape of coastal lines, local features, and storm size. A numerical experiment is conducted using the Advanced Circulation + Simulation and Simulating Waves Nearshore (ADCIRC + SWAN) coupled model for understanding the effects of wind intensity, forward speed, and wave on the storm surges caused by Hurricane Harvey. The ADCIRC + SWAN is used to simulate hurricane storm surges and waves. The wind fields of Hurricane Harvey were reconstructed from observed data, aided by a variety of methodologies and analyses conducted by Ocean Weather Inc (OWI) after the event. These reconstructed wind fields were used as the meteorological forcing in the base case in ADCIRC+SWAN to investigate the storm surges caused by the hurricane. Hurricane Harvey was the second most costly hurricane in the United States, causing severe urban flooding by dropping more than 60 inches of rainfall in Texas. The hurricane made three landfalls, with its first landfall as a Category 4 based on the Saffir–Simpson Hurricane Wind Scale (SSHWS), with wind intensities of 212.98 km/h (59 m/s). The storm surges caused by Hurricane Harvey were unique due to the slow speed, crooked tracks, triple landfalls in the USA, and excessive rain. The model’s storm surge and wave results were compared against observed data. High water marks at 21 locations and time series at 12 National Oceanic and Atmospheric Administration (NOAA) gauges were compared with the generated results. Several cases were investigated by increasing or decreasing the wind intensity or hurricane forward speed by 25% of the OWI wind and pressure data. The effects of the wave were analyzed by comparing the results obtained from ADCIRC + SWAN (with waves) and ADCIRC (without waves) models. The study found that the changes in wind intensity had the most significant effect on storm surges, followed by wave and forward speed changes. This study signifies the importance of considering these factors to enhance accuracy in predicting storm surges.

Ocean Temperature Observations in Hurricane Dorian (2019)

Abstract Upper-ocean temperatures from 72 airborne expendable bathythermographs (AXBTs) collected during U.S. Air Force Hurricane Hunter flights into Hurricane Dorian (2019) over a 72-h period are examined. Three transects collected behind the storm reveal increased cross-track sea surface temperature gradient magnitudes as Dorian intensified to a category-5 hurricane and slowed while approaching the Bahamas. The cold wake, evident in vertical and horizontal cross sections from in situ and satellite sensors, appears as an expected response to tropical cyclone passage. Atypical, however, is the 2°C surface cooling observed over 36 h in a pair of transects ahead of hurricane force winds in Dorian, likely due to changes in the tropical cyclone’s translation speed and direction and/or proximity to the Gulf Stream and continental shelf. Collocated AXBT pairs document a dynamical regime shift from mixing to upwelling as Dorian slows and turns. Relationships between time-integrated wind stress and sea surface temperature indicate track-relative differences varying with storm translation speed and heading changes, paralleling the shift in cooling dynamics. Significance Statement We studied in situ and satellite ocean temperature observations beneath Hurricane Dorian (2019) as the storm moved slowly, turned north, and weakened near Grand Bahama Island. We found a distinct change in the spatial distribution of cool upper-ocean temperatures beneath the storm, which indicated a shift in the primary cooling mechanism from ocean mixing to upwelling. This mechanism shift is important because hurricanes depend on warm ocean temperatures for energy, and upwelling roughly doubles the area of cooling beneath the storm. Our results highlight the effects of large heading changes on the upper-ocean response beneath tropical cyclones, especially in tandem with slow translation speeds.

A new index for assessing the coastal wind disasters based on the HY-2 satellite data

Tropical cyclone disasters have a substantial impact on human activity in coastal zones. The traditional methods used to classify wind disasters include the Beaufort Scale, Saffir-Simpson Scale, and Chicago Mercantile Exchange Hurricane Index (CMEHI). This research established a new index, the Haiyang Cyclone Index (HYCI), which considers spatial distributions and sea level anomalies (SLA) using HY-2 satellite data. The HYCI of three mega coastal cities, Qingdao, Shanghai, and Guangzhou, was calculated. A brief analysis was then performed using Moving Average Convergence Divergence (MACD) and Long Short-Term Memory (LSTM). The proposed HYCI can accurately evaluate the impact of cyclone disasters, thereby confirming its financial application potential.

Tropical cyclones position and intensity in the Southwest Indian Ocean as represented by CFS and ERA5 atmospheric reanalysis datasets

AbstractTropical cyclones are the most devastating meteorological systems that affect the Southwestern Indian Ocean (SWIO) bordering nations. The understanding of their track and intensity evolution is essential for the characterization of the systems. The National Centers for Environmental Prediction–Climate Forecast System (CFS) dataset and the ECWMF fifth‐generation reanalysis (ERA5) are evaluated against best‐track data from the International Best‐Track Archive for Climate Stewardship (IBTrACS) from 1980 to 2019 cyclone seasons. The cyclones were tracked within the reanalyses using a tracking algorithm based on relative vorticity and identified using a spatiotemporal matching method. Climatology from IBTrACS showed significant negative (positive) trend in the total number (number of intense) of systems; however, the evaluation of trends has to be cautious because it may be limited due to uncertainties in the first decades of data. Results for reanalyses showed that both performed similarly although some peculiarities were noted. CFS presented a slightly higher probability of detection, related to its special treatment of tropical cyclone‐like vortex, being able to reproduce all systems between 2000 and 2019. For positions, the majority of the systems have average errors around 50–60 km and the errors generally decrease with the intensity of the systems in both reanalyses, with ERA5 being slightly better during the early stages, while CFS outperforms ERA5 for intensity beyond the IBTrACS's moderate tropical storm category. Intensity follows a reverse pattern of position, with reanalyses largely degrading systems' intensity as they intensify. CFS (ERA5) appeared to be slightly better in terms of maximum wind (minimum sea‐level pressure), representing stronger systems although both datasets were unable to reproduce wind intensities beyond the tropical cyclone category. All evaluated parameters showed improvements when the most recent data period (2000–2019) is considered, especially in ERA5, tailored to an improved quality of the observations that are used to generate best‐track data and also to nudge the reanalyses through data assimilation. Based on the reanalyses characteristics revealed here, the choice of the most suitable dataset to address a specific study within the SWIO region may be dependent on the desired parameters and period.

Characterization and classification of wave storm events and wave climate on the Sea of Marmara

This study aims to conduct a wave climate study and provide a detailed analysis of wave storm events on the Sea of Marmara based on a 40-year (1979–2018) hindcast dataset. The accuracy of the used dataset was initially investigated in terms of both climatic and storm conditions using five-year (2013–2017) measurements of significant wave height (Hm0) at the Silivri buoy station. For the wave climate, the measurements’ 5-year mean and maximum Hm0 values were compared with SWAN hindcasts. To detect wave storm events from a time series, a critical value was determined by using the mean and standard deviation of the measurements. After that, the corresponding hindcasted storms with the measurements were detected. This analysis showed that the hindcasts are compatible with the measurements not only for climatic conditions but also for storm conditions. Monthly and annual variations of mean and maximum Hm0 were examined based on a 40-year long-term wave dataset and different return period Hm0 values based on annual maximums were determined at various locations. Wave storm events in the long-term analysis have been characterized and classified based on the storm energy density (Es) in five storm categories according to their energy. Temporal analysis of wave storm events shows that there has been an increase in the number and severity of storm events in recent years. The middle regions of the sea and the northeastern coasts of Marmara Island and Kapıdağ Peninsula are the most affected regions by extreme sea conditions.

Hailstorms and rainstorms versus supercells—a regional analysis of convective storm types in the Alpine region

The behaviour of severe thunderstorms, particularly supercells, in complex terrain is still poorly understood. Utilising 6 years of radar-, lightning- and radiosounding-based thunderstorm data in the domain of the Swiss radar network, we study different thunderstorm types in separate topographical regions. We classify the storms as ordinary thunderstorms, intense and severe rainstorms, hail and severe hailstorms and supercellular storms. After identifying the overlaps between the storm categories of rainstorms, hailstorms and supercells, the life cycles of several intensity metrics are investigated. This analysis allows the identification of predictors for intensification within severe storm life cycles. One of the most important predictors is the detection of a mesocyclone in a supercell before the onset or intensification of hail. We then divide the radar domain into sub-regions ranging from the Northwestern Po Valley, the Southern Prealps, main Alpine ridge, Northern Prealps, Swiss Plateau and Jura. This regional split separates storms in different terrain complexities. An investigation of the intensity distribution of storms in each region shows a clear intensity decrease over the main Alpine ridge, intermediate values over the moderately complex Prealpine regions and peaks for the flat Po Valley and Swiss Plateau. In contrast, the highest frequency of storms is found in the Prealpine regions on each side, with a lower frequency in the flat areas and a minimum in convective activity over the main Alpine ridge.

Improving the accuracy of hurricane wave modeling in Gulf of Mexico with dynamically-coupled SWAN and ADCIRC

Dynamically-coupled SWAN and ADCIRC models have been applied to enhance the predictions of extreme waves and storm surges induced by hurricanes and sea level rise (SLR) in the Gulf of Mexico. The model performance was evaluated using Hurricane Michael, a Category-5 hurricane, as a case study. Modeled wave heights were compared to the observations. Results indicate that the dynamically-coupled SWAN-ADCIRC models substantially enhance the modeling accuracy. By comparing to the maximum observed 2.69 m of wave height near the hurricane landing site, the error is 0.04 m by the SWAN-ADCIRC models in comparison to the 0.39 m by the SWAN stand-alone simulation. Effects of sea level rise on hurricane wave heights were investigated under four SLR scenarios of 0.2m, 0.5m, 1m, and 1.5m. Results indicate that, as sea level rises, wave heights increase non-linearly in shallow waters near the hurricane landing site. At the wave observation station near the hurricane landing site, the ratio of the wave-height change to SLR increases to 117% and the ratio of the combined wave-surge change to SLR increases to 265%. Analysis indicates that this is due to the substantial percentage changes in water depth occurring in shallow water compared to deep water caused by SLR.

Moisture Source for the Precipitation of Tropical Cyclones over the Pacific Ocean through a Lagrangian Approach

Abstract Tropical cyclones (TCs) are an important component of the hydrological cycle at tropical latitudes. In this study, we investigated the origin of precipitation associated with TCs formed from 1980 to 2018 over the Pacific Ocean in three subbasins: the western North Pacific Ocean (WNP), central and east Pacific Ocean (NEPAC), and South Pacific Ocean (SPO) basins. The analysis was performed throughout the TC lifetime during genesis, when they reached the lifetime maximum intensity (LMI), and the dissipation stage. The backward trajectories of all precipitant atmospheric parcels residing over the TC locations from the global outputs of the Lagrangian Flexible Particle (FLEXPART) dispersion model fed by the ERA-Interim dataset were used to identify moisture sources. The South and East China Seas and the western tropical North Pacific Ocean were identified as the principal moisture sources in the WNP basin, while the atmospheric moisture that precipitated mainly came from the eastern tropical North and South Pacific Ocean in the NEPAC basin, followed by the Caribbean Sea. Meanwhile, the Coral Sea, western tropical South Pacific Ocean, and northern Australia are the origins of the moisture in the SPO. The mean moisture uptake per TC was higher during the hurricane category than during any other stage in each basin.