Weathering the Storm: Mitigating Hurricanes with Ground-Based CCN and Lightning

This research introduces a groundbreaking methodology aimed at mitigating storm and hurricane intensity through the application of a ground-based, manually operated Cloud Condensation Nuclei (CCN) Generator. To meet the demand for more comprehensive context and rationale, this study explores the escalating challenges presented by the growing intensity of hurricanes, exemplified by Hurricane IAN (2022). The controlled release of environmentally friendly aerosols into the atmosphere, achieved by combusting selected wood pieces and organic edible materials, is a pivotal response to the escalating threat of extreme weather events. By generating CCN, the novel approach seeks to augment positive lightning in the eyewall, providing a potential solution to the intensification of hurricanes. Results illustrate the successful implementation of the methodology, with released aerosols effectively reaching the clouds for seeding, thus contributing to the modification of convection in the outer wall of Hurricane IAN and consequent intensity reduction. Rigorous experiments, incorporating considerations of various parameters such as wind patterns and the experimental location in Sarasota City, emphasize the scientific rigor applied to weakening Hurricane IAN. This comprehensive approach not only holds promise in mitigating hurricane intensity but also sheds light on the potential impact of cloud seeding in reducing the severity of future hurricanes, addressing a critical need for sustainable solutions to climate-related challenges.

A Wavelet-Based Deep Learning Framework for Predicting Peak Intensity of Hurricanes in the Atlantic Ocean

Every year, hurricanes pose a serious threat to coastal communities, and forecasting their maximum intensities has been a crucial task for scientists. Computational methods have been used to forecast the intensities of hurricanes across varying time horizons. However, as climate change has increased the volatility of the intensities of recent hurricanes, newer and adaptable methods must be devised. In this study, a framework is proposed to estimate the maximum intensity of tropical cyclones (TCs) in the Atlantic Ocean using a multi-input convolutional neural network (CNN). From the Atlantic hurricane seasons of 2000 through 2021, over 100 TCs that reached hurricane-level wind speeds are used. Novel algorithms are used to collect and preprocess both satellite image data and non-image data for these TCs. Namely, Discrete Wavelet Transforms (DWTs) are used to decompose individual bands of satellite image data, eliminating noise and extracting hidden frequency details before training. Validation tests indicate that this framework can estimate the maximum wind speed of TCs with a root mean square error of 15 knots. This framework provides preliminary predictions that can supplement current computational methods that would otherwise not be able to account for climate change. Future work can be done by forecasting with time constraints, and to provide estimations for more metrics such as pressure and precipitation.

Quantifying the Contribution of Track Changes to Interannual Variations of North Atlantic Intense Hurricanes

Previous studies have linked interannual variability of tropical cyclone(TC)intensity in the North Atlantic basin(NA)to Sahelian rainfall,vertical shear of the environmental flow,and relative sea surface temperature(SST).In this study,the contribution of TC track changes to the interannual variations of intense hurricane activity in the North Atlantic basin is evaluated through numerical experiments.It is found that that observed interannual variations of the frequency of intense hurricanes during the period 1958–2017 are dynamically consistent with changes in the large-scale ocean/atmosphere environment.Track changes can account for~50%of the interannual variability of intense hurricanes,while no significant difference is found for individual environmental parameters between active and inactive years.The only significant difference between active and inactive years is in the duration of TC intensification in the region east of 60°W.The duration increase is not due to the slow-down of TC translation.In active years,a southeastward shift of the formation location in the region east of 60°W causes TCs to take a westward prevailing track,which allows TCs to have a longer opportunity for intensification.On the other hand,most TCs in inactive years take a recurving track,leading to a shorter duration of intensification.This study suggests that the influence of track changes should be considered to understand the basin-wide intensity changes in the North Atlantic basin on the interannual time scale.

ANALYSES OF THE ANNUAL FREQUENCY ANOMALIES OF TYPHOONS AND HURRICANES IN 1998

In 1998, the annual frequency of typhoon (including tropical storms) genesis created a minimum value — 14, far lower than the minimum of 20 in 1950 over North-West Pacific, while in the Atlantic Ocean, the annual frequency of hurricanes (including the tropical storm) created a maximum value — 14, far higher than the average number — 9.2. In this paper, an analysis on the relationship between the generation of Typhoon, Hurricane and the Cross-Equatorial Flow was done by using the NCEP/NCAR reanalysis data for 1979 – 1995. It is pointed out that the anomalies of the CEF over the Pacific and Atlantic Ocean is the main cause for the 1998 annual frequency anomalies of Typhoon and Hurricane, respectively.

Cross - equatorial flow and its effects on the hurricanes occurrence over the Southern Hemisphere

Channels of cross - equatorial flow and their characteristics Winds at every 5 deg. grid point from 30°E to 180° over the equator at 850 hPa and 200hPa from December 1984 to February 1985 have been separated into U(zonal wind) andV(meridional wind).The V component has been used to represent cross- equatorial flow. FromFig. 1 we obtained that as during the Northern summer,there are also severa1 regions in which

Formation and Prevention of Hurricanes,Typhoons and Cyclones

Hurricanes,typhoons and cyclones are the most destructive weather systems.In order to mitigate the disasters caused by these storms,it is necessary to clarify the cause and activity rule of these storms.However,the formation of hurricanes,typhoons and cyclones as well as the cause of their path and strength changes still remains the major unsolved problems in today’s world.Fortunately,the author has recently studied the formation and activity of polar vortices,therefore can reveal the formation and current driving warm core structure of hurricanes,typhoons and cyclones,which plays an important guiding role in preventing major disasters caused by them.The author finds that all hurricanes,typhoons and cyclones are formed by polar vortices pulled by the moon.In order to prevent hurricanes from raging along the east coast of the United States or cyclones from setting wildfires in western United States,the potentially dangerous vortex genesises near Baffin Island and those over northeastern Siberia should be monitored and weakened in the month before they prevail;in order to prevent typhoons from ravaging the Northwest Pacific or South China Sea,the potentially dangerous vortex genesises over northeastern Siberia should be monitored and weakened in the month before they prevail;in order to prevent cyclones from raging over the South Indian Ocean or the Bay of Bengal,the potentially dangerous vortex genesises in Antarctic should be monitored and weakened in the month before they prevail;in order to avoid abnormal cold in late winter or early spring in the Northern Hemisphere,the potentially dangerous Arctic cold vortex genesises near Baffin Island and those over northeastern Siberia should be monitored and weakened in the month before they prevail.

A Brief Parametric Analysis of Catastrophic or Disastrous Hurricanes That Have Hit the Florida Keys between 1900 and 2000

The most intense and catastrophic hurricanes on record to hit the Florida Keys during 1900 to 1950 were in 1919, and 1935. From 1950 to 2000, the most intense hurricanes to hit or affect the Florida Keys were in 1960, 1965, and 1992. In this paper, we will present a brief parametric analysis of the hurricanes that have hit the Florida Keys in the last 100 years. This analysis will include the descriptive statistics, best fit probability distribution of the latitude of the catastrophic hurricanes and a confidence interval that detects the average latitude of hurricanes (category 3 or higher) which have hit the Florida Keys in the last 100 years.

Resilience in a Mexican Pacific Mangrove after Hurricanes: Implications for Conservation Restoration

High resilience is a mangrove trait that is compatible with life in a dynamic environment, however, catastrophic disturbance can lead to the entire forest structure being re-defined. In the Pochutla district of Oaxaca, two hurricanes made landfall in 1997 and one more in 2012. Following the 1997 hurricanes, extensive mangrove restoration was carried out in the study area. A cohort of Rhizophora mangle saplings planted in 2007 showed 10% mortality during the first year after transplantation, an average growth of 39 cm, and a positive association between growth rate and the level of water in the lagoon. Following Hurricane Carlotta of 2012, measurements of structural impact and tree mortality were taken in restored R. mangle stands and in naturally regenerated patches of Laguncularia racemosa. The role of tree girth in R. mangle susceptibility to wind damage was also investigated. The stands of R. mangle suffered 80% reduction in stem density and 86% loss of basal area, whereas the corresponding values for L. racemosa were 26% and 15%, respectively. Within stands of R. mangle, mortality conserved a positive relationship with structural impact categories and the frequency of snapped stems was segregated across girth classes. The results suggest that L. racemosa has greater resilience to hurricane damage, which has some consistency with previous research and implications for conservation restoration protocols. In order to promote a system with higher resilience, we recommend an on-going restoration effort with mixed mangrove species.

Analytical Models for Hurricanes

A two-layer theoretical model of hurricanes traveling (quasi-) steadily over open seas has been developed. The use of coherency concept allowed avoiding the common turbulent approximations, except a thin sub-layer near the air/sea interface. The model analytically describes 3D distributions of dynamic and thermodynamic variables in hurricanes and analyzes processes of evaporation and condensation. Using this modeling, the following fundamental problems were naturally resolved-change in the cyclonic/anti-cyclonic directions of hurricane rotation and the directions of radial wind in lower and upper parts of hurricane;increase in wind angular momentum in hurricane boundary layer;dramatic effect of ocean spray and its radial distribution;and a high increase in temperature at the upper region of boundary layer. Additionally, integral balances allowed expressing the governing parameters of field variables via two external parameters, the sailing wind and temperature of a warm air band, in which direction the hurricane travels. A rude model for the hurricane genesis and maturing has also been developed.

Possibility of Predicting Category-5 North Atlantic Hurricanes

Category-5 hurricanes are the most devastating from the standpoint of human and economic losses. The occurrence of this kind of hurricane is believed to be of quasi-random nature, so it is very difficult to predict them well in advance. Warnings at this regard are generally given in the course of their development. We propose here, that there are some inherently periodicities of the phenomena that allow to predict category-5 hurricanes, even with some years of anticipation. For our study, we consider the North Atlantic category-5 hurricanes since 1920. We consider in this study data of the SST （sea surface temperature） of the North Atlantic Ocean as a representative parameter of hurricane activity. Then, by means of the wavelet analysis, we determine the dominant oscillation periods and establish correspondence rules using fuzzy logic. The wavelet power spectrum yields the following dominant periodicities： 0.5, 1, 3, 11, 22 and 32 years. The fuzzy logic searches for associations between the hurricanes occurrence and the behavior of the harmonics. Such correspondence rules lead us to restrict dates of possible hurricane occurrence. Interpolation of the periodic behavior allows for a good reconstruction of past hurricanes dates since 1920, as well as extrapolation to predict dates of occurrence in the future. We conclude that the conditions for the formation of the next category-5 hurricane in the North Atlantic may occur during the seasons of 2015-2017 with the highest probability in 2017.

Thermodynamics of Hurricanes Revisited

Hurricanes and tropical storms are heat engines operating between warm tropical oceans and the cold upper troposphere. The purpose of this article is to examine the existing theories for hurricanes and tropical storms, and to discuss their validity. It is argued that contrary to previous claims that hurricanes are Carnot engines, these systems operate at efficiencies considerably below their maximum thermodynamic efficiency. As such, the validity of the current theories of thermodynamics of hurricanes remains questionable, and the phenomenon continues to be a geophysical enigma.

Deep learning for real-time social media text classification for situation awareness-using Hurricanes Sandy,Harvey,and Irma as case studies

Social media platforms have been contributing to disaster management during the past several years.Text mining solutions using traditional machine learning techniques have been developed to categorize the messages into different themes,such as caution and advice,to better understand the meaning and leverage useful information from the social media text content.However,these methods are mostly event specific and difficult to generalize for cross-event classifications.In other words,traditional classification models trained by historic datasets are not capable of categorizing social media messages from a future event.This research examines the capability of a convolutional neural network(CNN)model in cross-event Twitter topic classification based on three geo-tagged twitter datasets collected during Hurricanes Sandy,Harvey,and Irma.The performance of the CNN model is compared to two traditional machine learning methods:support vector machine(SVM)and logistic regression(LR).Experiment results showed that CNN models achieved a consistently better accuracy for both single event and crossevent evaluation scenarios whereas SVM and LR models had lower accuracy compared to their own single event accuracy results.This indicated that the CNN model has the capability of pre-training Twitter data from past events to classify for an upcoming event for situational awareness.

AN INTRODUCTION TO THE NASA EAST PACIFIC ORIGINS AND CHARACTERISTICS OF HURRICANES(EPOCH) FIELD CAMPAIGN

Over the past fi ve years, tropical activity in the East Pacifi c has increased, while declining in the Atlantic Basin. In addition, during El Ni?o years, warmer than average sea surface temperatures further increase the likelihood of tropical cyclone formation in the East Pacifi c. Hurricane fi eld campaigns used the Ku-/Ka-band HighAltitude Wind and Rain Airborne Profi ler(HIWRAP) radar on the Global Hawk(GH) unmanned aircraft, in GRIP(Genesis and Rapid Intensifi cation Processes 2010), HS3(Hurricane and Severe Storm Sentinel 2012-14), and the NOAA Sensing Hazards with Operational Unmanned Technology(SHOUT 2015-16) fi eld campaigns. Although originally designed for the GH, the X-band high-altitude RADar(EXRAD) has yet to be integrated and fl own on an unmanned aerial vehicle. EXRAD will provide data with less attenuation of signal over deep convection as well as better estimates of three-dimensional winds with its nadir-pointing beam. As part of the NASA Hand On Project Experience(HOPE) Training Opportunity, our team proposed to fl y the AV-6 GH aircraft with the EXRAD radar, the High Altitude MMIC Sounding Radiometer(HAMSR), and NOAA Advanced Vertical Atmospheric Profi ling System(AVAPS) dropsondes to investigate genesis and/or rapid intensifi cation(RI) of an East Pacifi c hurricane by measuring both the environment and interior structures. Information on planned activities primarily focused on the EXRAD high-altitude radar integration for the July-August 2017 science fl ight will be presented.

Nonlinear interaction of axisymmetric circulation and nonaxisymmetric disturbances in hurricanes

The nonlinear interaction of axisymmetric circulation and nonaxisymmetric disturbances in hurricanes is numerically studied with a quasigeostrophic barotropic model of a higher resolution. It is pointed out that the interaction may be divided into two categories. In the first category, nonaxisymmetric disturbances decay, the coordinate locus of maximum relative vorticity ζmax is seemingly unordered, and the central pressure of hurricane rises; while in the second one, nonaxisymmetric disturbances develop, the locus of ζmax shows an ordered limit cycle pattern, and the central pressure falls remarkably. A succinct criterion is given to judge which category the interaction belongs to, i.e. the vortex beta Rossby number at the initial time Rβ< 1 belongs to the decaying category and Rβ> 1 to the developing one. Finally, practical applications of theoretical results of the rotational adaptation process presented by Zeng and numerical results in this paper to the hurricane intensity prediction in China are also discussed.

Assessing impacts of energy storage on resilience of distribution systems against hurricanes

This paper develops a comprehensive framework to analyze the impact of energy storage on improving the resilience of distribution systems against hurricanes.This paper first develops a spatio-temporal model of progressing hurricane when making landfall that can be used to anticipate outage scenarios caused by the gust-wind speed.An optimization model is then developed for optimizing the operation of distribution systems during hurricane that captures both pre-outage and post-outage network operation constraints.Numerical simulations are performed on the modified IEEE 33-bus distribution system with real hurricane data in Houston to demonstrate the effectiveness of the proposed model.

Understanding the Perceptions and Practices of Homeowners in the Event of Hurricane: A Case Study of University Employees in Louisiana

The purpose of this qualitative study is to explore and gain an understanding of Louisiana homeowners’ perceptions and practices in preparing for hurricanes;contribute to academic research and student learning, while guiding the development of future projects. We focused specifically on how homeowners in Louisiana would protect their important documents and possessions. These documents and possessions include but are not limited to photographs, social security cards, birth certificates, and insurance paperwork. We collected the data throughout the fall semester of 2022 at Louisiana State University (LSU) by conducting a focus group and in-depth interviews. We collected a variety of answers, but most participants expressed a strong need to have their important documents and possessions protected and gave different strategies for how this was carried out, such as digitizing or protective storage. Participants also expressed a strong need for more information to be delivered to residents to talk more about ways they can protect their own documents and possessions. Emphases were also made on the need for: leaders of large institutions to be especially concerned with preparedness for vulnerable populations;outreach programs to be put in place to spread awareness of the importance of protecting important documents and possessions during a disaster;news outlets to switch their focus from normal survival tips to discussions on the importance of protecting documents and possessions;government and non-profit agencies to work together to share tips and information through social media and other forms of handouts.

Impacts on Bats by a Supertyphoon vs.Ordinary Typhoons along a Habitat Urbanization Gradient

Two major human-caused threats to ecosystems are habitat modification and the increasing frequency and intensity of extreme weather events.To study the combined effect of these threats,the authors used acoustic monitoring of bats along a habitat modification gradient on the island of Okinawa,Japan.During the observation period,the island experienced numerous typhoons and one supertyphoon.Native bat species remained active even at high wind speeds(up to 30 m/s in some cases).Milder typhoons had no observable effect on bat populations,with activity levels fully recovering within a few hours or days.The super typhoon also did not seem to affect bats in fully or partially forested habitats but caused their local disappearance at the urban site,which they have not re-colonized three years after the event.Notably,bats that disappeared at the urban site were species roosting in well-protected places such as caves and concrete structures.In all cases,the biomass of small flying insects and the acoustic activity of insects recovered within days after extreme weather events.Thus,the striking difference between habitats in supertyphoon effects on bats cannot be explained by the super typhoon directly killing bats,destroying their roosting sites,or decreasing the abundance of their prey.The results underscore the importance of preserving natural habitats in areas particularly affected by changing climate and show that the survival of species and ecosystems during the numerous episodes of climate change in the Earth’s history does not necessarily mean their ability to survive the accelerating climate change of our time.

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images.Hyperspectral remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic spectrum.In order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and quantitativemonitoring.Particularly,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on vegetation.With this motivation,this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing Images.The presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images.To accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature vectors.Besides,Elman neural network(ENN)model is applied to allot proper class labels into the input HSI.Finally,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this article.The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects.Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.

Puerto Rico’s Water Supply: An Investigation of the Levels of Trihalomethanes and Other Contaminants

The US Commonwealth of Puerto Rico is comprised of 143 islands, atolls, cays, and islets. Of the 143 localities, only 3 islands are inhabited: The mainland (often referenced as Puerto Rico), Culebra, and Vieques. To properly analyze the water supply quality, the mainland will be the focal point for examining environmental and social injustices. Puerto Rico is a racially diverse but ethnically homogenous territory, with most of the commonwealth living below the poverty level. Access to clean water sources is always tenuous in Puerto Rico. Over 70 percent of the island is served by water, violating US health standards. However, the recent hurricanes made the situation even more detrimental. According to data reported between January 2015 and March 2018 by the Consumer Confidence Report (CCR), 97 percent of the population of Puerto Rico utilizes a common drinking water system with one or more recent violations of the Safe Drinking Water Act for its testing requirements for lead and copper levels. The amounts found were far higher than any US state, meaning that virtually everyone on the island gets water from systems that violated testing or reporting requirements. In this study, we have collected and analyzed the levels of trihalomethanes (THMs), haloacetic acids (HAAs), copper, lead, and total organic compounds (TOCs) in drinking water providing systems in Puerto Rico and compared them with the recommended levels of contaminants provided by the US Environmental Protection Agency (EPA) guidelines. Many of these reported contaminants can have serious and detrimental health effects after prolonged exposure to higher concentrations of the contaminants found in the drinking water sources of Puerto Rico.

三维变分同化机载雷达资料对飓风预报的影响研究——2012年Isaac试验

随着气旋内部资料(Inner core data)在热带气旋预报中的使用,其重要性逐渐受到人们越来越多的关注。为了研究该资料中尾部机载雷达(Tail Doppler Radar,TDR)资料在业务系统中的应用效果,本文利用2012年飓风等级热带气旋Isaac期间的TDR资料,采用业务HWRF(Weather Research and Forecasting model for Hurricane)数值模式与业务GSI(Grid-point Statistical Interpolation system)三维变分同化(Three-Dimensional Variational Data Assimilation,3DVar)系统对TDR资料进行了同化,展开了一系列预报试验,并对其效果进行了分析和研究。结果表明与HWRF的业务预报相比,GSI系统同化TDR资料后对热带气旋的路径和强度预报有明显改进;但其同化效果同时也表明业务三维变分中的静态背景误差协方差在TDR资料的应用中仍需要进一步的改进。