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Level 17 Tropical Cyclone Editors

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Last Updated: 14 December 2020

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General | Latest Info

Atmospheric researchers tend to agree that tropical cyclones of unusual ferocity are coming this century, but the strange fact is that there is no consensus to date on the Five-point scale used to classify the power of these anticipated storms. In what may sound like page from the script of rock-band spoof Spinal Tap with its reference to beyond-loud electric guitar amplifier volume 11, there is actually talk of adding a sixth level to the current Saffir-Simpson Hurricane scale, on which Category 5 intensity means sustain winds higher than 155 miles per hour for at least one minute, with no speed cap. The lack of an upper limit on scale results in all of the most intense Tropical cyclones getting lump together, despite their wide range of power. Category 5 becomes less descriptive when it includes 2005's Emily, which reached peak wind speeds of 257. 5 kph and six hours in Category 5; same year's Katrina, which held peak wind velocity of 280 kph for 18 hours in Category; and 1980's Allen, churning with peak winds at 305 kph maintain for 72 hours in the highest Category. And now ferocity forecast for century add to this classification problem. Severe hurricanes might actually become worse. We may have to invent Category 6, say David Enfield, senior scientist at University of Miami and former physical oceanographer at US National Oceanic and Atmospheric Administration. This new level wouldn't be arbitrary relabeling. Global Satellite data from the past 40 years indicates that the net destructive potential of hurricanes has increase, and the strongest hurricanes are becoming more commonespecially in the Atlantic. This trend could be related to warmer seas or it could simply be History repeating itself. Data gathered earlier than the 1970s, although unreliable, show cycles of quiet decades followed by active ones. Quiet '60s, '70s and '80s ended in 1995, year that brought Felix and Opal, among others, and resulted in 13 billion in damage and more than 100 deaths in the US. Pros and cons of categories: Five or six? The average difference between current categories equals nearly 20 mph, so the Category 6 label would likely be applied to hurricanes with sustained winds over 175 mph. The speed and destruction of hypothetical Category 6 storms is speculative, despite hurricanes with winds at that level. After all, meteorologists and climate researchers may not even choose a Category 5 Storm from the record books if asked to identify the most powerful Tropical Cyclone in history, because the Saffir-Simpson scale fixates on maximum wind speed lasting for at least one minute and disregards many other large-scale components that factor into the storm's level of devastation. The whole index should be thrown out Hurricane-proof window, some say. If I could do it, I would do away with categories, say Bill Read, Director of NOAA's National Hurricane Center.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Naming

Ranking of tropical cyclone hits by country since 1970

RankNation
1China
2Philippines
3Japan
4Mexico
5United States of America
6Australia
7Taiwan
8Vietnam
9Madagascar
10Cuba

See also: Lists of Tropical Cyclone names Tropical Cyclones are usually given names because they help in forecasting, locating, and reporting. They are named once they have steady winds of 62 km / h. Committees of the World Meteorological Organization pick names. Once name, Cyclone is usually not rename. For several hundred years, Hurricanes were named after Saints. In 1887, Australian meteorologist Clement Wragge began giving women's names to Tropical Cyclones. He thinks of history and mythology for names. When he used men's names, they were usually of politicians he hat. By World War II Cyclone names were based on the phonetic alphabet. In 1953, the United States stopped using phonetic names and began using female names for these storms. This ended in 1978 when both male and female names were used for Pacific storms. In 1979, this practice was added for Hurricanes in the Gulf of Mexico and Atlantic.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

Table

201920202021202220232024
AndreaArthurAnaAlexArleneAlberto
BarryBerthaBillBonnieBretBeryl
ChantalCristobalClaudetteColinCindyChris
DorianDollyDannyDanielleDonDebby
ErinEdouardElsaEarlEmilyErnesto
FernandFayFredFionaFranklinFrancine
GabrielleGonzaloGraceGastonGertGordon
HumbertoHannaHenriHermineHaroldHelene
ImeldaIsaiasIdaIanIdaliaIsaac
JerryJosephineJulianJuliaJoseJoyce
KarenKyleKateKarlKatiaKirk
LorenzoLauraLarryLisaLeeLeslie
MelissaMarcoMindyMartinMargotMilton
NestorNanaNicholasNicoleNigelNadine
OlgaOmarOdetteOwenOpheliaOscar
PabloPaulettePeterPaulaPhilippePatty
RebekahReneRoseRichardRinaRafael
SebastienSallySamSharySeanSara
TanyaTeddyTeresaTobiasTammyTony
VanVickyVictorVirginieVinceValerie
WendyWilfredWandaWalterWhitneyWilliam

Table2

IntensityCasesMedian DamagePotential Damage
Tropical/Subtropical Storm118less than $1,000,0000
Hurricane Category 145$33,000,0001
Hurricane Category 229$336,000,00010
Hurricane Category 340$1,412,000,00050
Hurricane Category 410$8,224,000,000250
Hurricane Category 52$5,973,000,000500

Table3

Local Time ZoneTime Adjustment (hours)
Atlantic Daylight Time (ADT)-3
Atlantic Standard Time (AST) Eastern Daylight Time (EDT)-4
Eastern Standard Time (EST) Central Daylight Time (CDT)-5
Central Standard Time (CST) Mountain Daylight Time (MDT)-6
Mountain Standard Time (MST) Pacific Daylight Time (PDT)-7
Pacific Standard Time (PST) Alaskan Daylight Time (ADT)-8
Alaskan Standard Time (ASA)-9
Hawaiian Standard Time (HAW)-10
New Zealand Standard Time (NZT) International Date Line Time (IDLE)+12
Guam Standard Time (GST) Eastern Australian Standard Time (EAST)+10
Japan Standard Time (JST)+9
China Coast Time (CCT)+8
West Australia Standard Time (WAST)+7
Russian Time Zone 5 (ZP5)+6
Russian Time Zone 4 (ZP4)+5
Russian Time Zone 3 (ZP3)+4
Bagdad Time (BT) Russian Time Zone 2(ZP2)+3
Eastern European Time (EET) Russian Time Zone 1(ZP1)+2
Central European Time (CET) French Winter Time (FWT) Middle European Time (MET) Swedish Winter Time (SWT) Middle European Winter Time (MEWT)+1
Western European Time (WET) Greenwich Mean Time ( GMT )0

Table5

CategoryMaximumMinimum
Named storms28 (2005)4 (1983)
Hurricanes15 (2005)2 (1982,2013)
Major Hurricanes8 (1950)0 (many times,2013 last)
Named storms9 (2004)0 (1990)
Hurricanes6 + (1916,1985,2004,2005)0 (many,2015)
Major hurricanes4 (2005)0 (many,2015)
Named storms28 (1992)8 (1977,2010)
Hurricanes16 (1990,1992,2014,2015)3 (2010)
Major hurricanes11 (2015)0 (many,2003)
Named storms39 (1964)14 (2010)
Typhoons26 (1964)5 (1999)
Super typhoons11 (1965,1997)1 (1999,2010)

Table6

CategoryAverageMaximumYearsMinimumYears
Named storms (including subtropical storms)11.728200541983
Hurricanes6.3>15200521982,2013
Major Hurricanes2.4720050many times, last 2013
USA landfalling hurricanes1.761985, 2004, 2005 +0many, last 2015
> USA landfalling major hurricanes0.6420050many, last 2015

Total number of tropical cyclone hits by country

RankNationHits
1United States of America268
2China230
3Philippines176
4Mexico134
5Japan133
6Cuba79
7Australia66
8Bahamas61
9Vietnam45
10Madagascar30
* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Impact

Though climatological studies can help explain overall characteristics of ET for give basin, whether in past, present, or future, impacts of individual ET events on lives and property are directly tied to hazards of strong winds, large waves, and heavy precipitation. During ET, TC wind field expands and becomes increasingly asymmetric, shifting coverage and location of maximum wind speeds and thus regions at risk. Evolving wind fields affect distribution of large waves, and these large waves can directly impact marine interests and coastlines. In addition, extreme inland precipitation can occur within ET systems, sometimes far remove from the cyclone Center. Like wind field, precipitation distribution tends to shift during the ET process. Jones ET al. Emphasized importance of better understanding of evolution and prediction of these hazards to mitigate societal impacts of the ET event. Many studies have responded to this research needs since Jones ET al. Was publish, and the following subsections examine research progress on each of three major direct ET hazards.


Conclusion and Discussion

Application of ANNs use in HIL Model to evaluate historical events allows for examination of changes in outcome as result of location, maximum wind speed, minimum pressure, maximum storm surge, and total precipitation at landfall. Previously, such events have been analyzed either by solely looking at increase in population and wealth, while neglecting locational changes, or could potentially be evaluated for loss from wind and wind driven rain only. Simulations using HIL Model highlight an increase in economic damage from storms further North along the East Coast and how improvements to various communities have been somewhat beneficial. The I-95 corridor along the US East Coast has been notoriously known for high vulnerability and deteriorating infrastructure. In comparison to southern cities, there are older buildings and high population densities. This difference, as most individuals would observe if they visit both, is what HIL Model recognizes when same meteorological parameters are move between northern and southern location. Differing Results for same meteorological parameters between Galveston and North Carolina, call attention to the prospect that nature's build-in defenses are more suitable for protection than man-make solutions in more urbanized and crowded areas. Essentially, so far, no mitigation solution has do same for location as lower population and de-urbanization would, as confirmed by HIL Model simulations. Over time, better infrastructure quality, increased knowledge, and experience may assist in maintaining coastal communities ' resilience to hurricanes. However, increased population, wealth, and inflation have had a counteracting effect by placing more population in harms way. Building codes continue to improve and become more stringent for coastal communities, yet with the increase in capital in these counties, there is more to lose. Coastal communities north of North Carolina, however, have not made overall relevant locational improvements with concern to hurricanes over the past century. In comparison to their southern counterparts, northeastern counties are risking lower resiliency overall to potential hurricanes and tropical storms, especially due to larger populations that are more reliant on local, deteriorated, infrastructure systems. HIL Model simulation Results show that even with increased mitigation strategies over time, whether it be building codes or policy, has not been able to reduce the expected impact from severe events. Infrastructure improvements are fighting just to keep up with the surge in population, and are more commonly falling behind than succeeding in keeping pace. Coastal areas have reached a tipping point where their resilience is becoming more dependent on wealth and overall population quantity than infrastructure quality. If infrastructure improvements lack or slow down, however, this resilience can be expected to only deteriorate alongside locale infrastructure.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Classifications

Ranking of tropical cyclone hits by country since 1970

RankNation
1China
2Philippines
3Japan
4Mexico
5United States of America
6Australia
7Taiwan
8Vietnam
9Madagascar
10Cuba

See also: Saffir-Simpson Hurricane Scale tropical cyclones are classified into different categories by their strength and location. The National Hurricane Center, which observes hurricanes in the Atlantic Ocean and Eastern and Central Pacific Ocean, classifies them using the Saffir-Simpson Hurricane Scale. Tropical cyclones in other places such as the Western Pacific Ocean or Southern Hemisphere are classified on scales that are quite a bit like the Saffir-Simpson Scale. For example; if a tropical storm in the Western Pacific reaches Hurricane-strength winds, it is then officially called a typhoon. A Tropical depression is an organized group of clouds and thunderstorms with clear circulation in the air near the Ocean and maximum continuing winds of less than 17 m / S. It has no eye and does not usually have the spiral shape that more powerful storms have. Only the Philippines know to name tropical depressions. A Tropical storm is an organized system of strong thunderstorms with very clear surface circulation and continuing winds between 17 and 32 m / S. At this point, cyclonic shapes start to form, although eyes do not usually appear in tropical storms. Most tropical cyclone agencies start naming cyclonic storms at this level, except for the Philippines, which have their own way of naming cyclones. A hurricane or typhoon or cyclone is a large cyclonic weather system with continuing winds of at least 33 m / S. Tropical cyclones with this wind speed usually develop an eye, which is an area of calm conditions at the center of its circulation. The eye is often see from space as small, round, cloud-free spot. Around the eye is the eye wall, area where the strongest thunderstorms and winds spin around the storm's center. The fastest possible continuing wind speed found in tropical cyclones is thought to be around 85 m / S.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

Table

IntensityCasesMedian DamagePotential Damage
Tropical/Subtropical Storm118less than $1,000,0000
Hurricane Category 145$33,000,0001
Hurricane Category 229$336,000,00010
Hurricane Category 340$1,412,000,00050
Hurricane Category 410$8,224,000,000250
Hurricane Category 52$5,973,000,000500

Table2

Local Time ZoneTime Adjustment (hours)
Atlantic Daylight Time (ADT)-3
Atlantic Standard Time (AST) Eastern Daylight Time (EDT)-4
Eastern Standard Time (EST) Central Daylight Time (CDT)-5
Central Standard Time (CST) Mountain Daylight Time (MDT)-6
Mountain Standard Time (MST) Pacific Daylight Time (PDT)-7
Pacific Standard Time (PST) Alaskan Daylight Time (ADT)-8
Alaskan Standard Time (ASA)-9
Hawaiian Standard Time (HAW)-10
New Zealand Standard Time (NZT) International Date Line Time (IDLE)+12
Guam Standard Time (GST) Eastern Australian Standard Time (EAST)+10
Japan Standard Time (JST)+9
China Coast Time (CCT)+8
West Australia Standard Time (WAST)+7
Russian Time Zone 5 (ZP5)+6
Russian Time Zone 4 (ZP4)+5
Russian Time Zone 3 (ZP3)+4
Bagdad Time (BT) Russian Time Zone 2(ZP2)+3
Eastern European Time (EET) Russian Time Zone 1(ZP1)+2
Central European Time (CET) French Winter Time (FWT) Middle European Time (MET) Swedish Winter Time (SWT) Middle European Winter Time (MEWT)+1
Western European Time (WET) Greenwich Mean Time ( GMT )0

Table4

CategoryMaximumMinimum
Named storms28 (2005)4 (1983)
Hurricanes15 (2005)2 (1982,2013)
Major Hurricanes8 (1950)0 (many times,2013 last)
Named storms9 (2004)0 (1990)
Hurricanes6 + (1916,1985,2004,2005)0 (many,2015)
Major hurricanes4 (2005)0 (many,2015)
Named storms28 (1992)8 (1977,2010)
Hurricanes16 (1990,1992,2014,2015)3 (2010)
Major hurricanes11 (2015)0 (many,2003)
Named storms39 (1964)14 (2010)
Typhoons26 (1964)5 (1999)
Super typhoons11 (1965,1997)1 (1999,2010)

Table5

CategoryAverageMaximumYearsMinimumYears
Named storms (including subtropical storms)11.728200541983
Hurricanes6.3>15200521982,2013
Major Hurricanes2.4720050many times, last 2013
USA landfalling hurricanes1.761985, 2004, 2005 +0many, last 2015
> USA landfalling major hurricanes0.6420050many, last 2015

Total number of tropical cyclone hits by country

RankNationHits
1United States of America268
2China230
3Philippines176
4Mexico134
5Japan133
6Cuba79
7Australia66
8Bahamas61
9Vietnam45
10Madagascar30
* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Current Job Duties

Dr. Vigh is currently leading NCAR side of the joint MIT-NCAR Project, funded by NOAA Hurricane Forecast Improvement Project, to develop new frameworks for predicting extreme rapid intensification of Tropical cyclones. As part of this project, he is updating research-grade dataset of high resolution flight level data. He is also assisting with another HFIP subtask examining ensemble verification of rapid intensification. Dr. Vigh provides scientific support to developers of Model Evaluation Tools and related Project that is developing METplus use cases for verification of space weather. Dr. Vigh is also involved in international capacity building through a new project to improve subseasonal-to-seasonal forecasting in Indonesia. In addition to these projects, Dr. Vigh is developer and maintainer of the Tropical Cyclone Guidance Project, which provides real-Time Model Guidance for Tropical cyclones. Finally, Dr. Vigh is lead developer for Climate Risk Management Engine, which supports a number of climate science applications. Dr. Vigh is actively involved in the provision of climate data and indicators for Climate Risk screening.


Introduction

Typhoons that reach East Asia threaten island, coastal, and inland populations of nearly 1 billion people. Using in situ observations to advance our knowledge of air-Sea exchanges during extremely strong winds and, in turn, improving the accuracy of numerical Typhoon forecasts is of particular importance to providing timely warnings to the public for disaster mitigation and for reducing economic loss that results from false announcements. These false announcements are mostly attributed to forecasting errors in storm landfall time / location and Typhoon's wind strength. Since typhoons can intensify by obtaining heat from the ocean via air-Sea interactions 1, better knowledge of hydrographic conditions of the upper Ocean is key to improving typhoon forecasts and thus increasing the efficiency of disaster mitigation. Evolving processes in mixed layers of the upper Ocean, atmospheric and oceanic heat exchange, inertial oscillations, and cold wakes in the open Ocean are crucial to the development of typhoons and successive typhoons, and therefore, these processes are the central focus of many studies using field observations, satellite remote-sensing, and numerical modeling 2 3 4 5 6. Previous observations in stratified coastal Ocean and associated studies 7 and 8 have proven that velocity shear-induced vertical mixing during direct wind influence of tropical cyclones is the primary mechanism for rapid upper Ocean cooling while storm wind is still effective. Rapid cooling subsequently causes reduction in intensity of some hurricanes translate across the continental shelf of Mid-Atlantic Bight 7. Whether similar rapid cooling occurs during typhoons direct wind influence period in deep water has yet to be examined by in situ observations. Despite the modeling approach, satellite observations, although powerful, are considerably limited by cloud cover, spatial and temporal resolutions and are only representative of surface conditions 3 9. Upper ocean temperature, salinity, and velocity profiles before, during, and after passage of the typhoon can only be obtained by in situ measurements. However, high sea state under influence of extremely strong winds, reliability of instrumentation, and unpredictable nature of typhoons prevent direct shipboard measurements and hinder sampling using limited data buoys and autonomous underwater vehicles. Successful sampling of the near sea surface atmosphere and upper Ocean during typhoons relies on correct selection of locations for deployment of data buoys and / or gliders prior to passage of typhoons. A More suitable locations can be determined using statistics of historical typhoon tracks. Although rare, there are notable direct observations of few typhoons / hurricanes using anchor data buoys. Bermuda Testbed Mooring in western North Atlantic observed Hurricane Fabian, which caused a prominent surface temperature decrease of > 3. 5 C, deepening of vertical mixing to 130 m, and upper Ocean current of up to 1 m s 1 after passing of Fabian 3. Kuroshio Extension Observatory in northwestern Pacific recorded Ocean responses to Typhoon Choi-Wan at closest distance of approximately 40 km from the typhoon's center.


Results

Coastal wetland coverage is associated with statistically significant reductions in cyclone-related property damage. Loss of 1 km 2 of wetland coverage increases the predicted probability of experiencing property damage during storms by 0. 02 % in county with average wetland coverage, wind speed, and flooding area. For coastal communities suffering from property damage from storm, 1 % loss of coastal wetlands is associated with 0. 6 % increase in property damage, controlling for storm-specific characteristics, property value under flooding risk, state-specific time-invariant determinants of property damage, and year-level shocks. Coefficient estimates of wind, potential storm surge area, property value under flooding risk, and being located to right-hand of storm path are positive and significant. The wind effect is particularly large and counties on storm paths ' right side experience 140 % more property damage than those on the left. Coastal wetlands protective effects are nonlinear in wind intensity, conditional on damage. This may be because once wetland vegetation is fully saturate with water, wave dissipation effects are weaker. To detect this type of nonlinearity, wetland effects are decomposed by wind speeds experienced by the county. Wetlands are effective against storms of all different magnitudes. The Elasticity of property damage with respect to wetlands is 0. 58 for tropical storm, 0. 55 for Category 1 hurricane, 0. 40 for Category 2 hurricane, and 0. 35 for a Category 3 to 5 hurricane. This pattern is consistent with laboratory experiments. The preventative effect is especially strong for tropical storms, which happen twice as often as hurricanes. However, because property damage is rapidly increasing in storm strength, absolute magnitude of damage prevented is predicted to be the largest for major hurricanes. Saltwater wetlands are located closer to shore than freshwater wetlands, providing the first line of defense against storm surges. Nevertheless, freshwater wetlands typically have more coverage than saltwater wetlands, providing a wider buffer zone, as freshwater wetlands constitute about 85 % of total coastal wetland coverage. We find significant reductions in property damage for both freshwater and saltwater wetlands. The difference between their contributions is small and not significantly different from zero. This is not surprising since storm surges can extend miles inland and encompass both types of wetlands. Forest wetlands, having rougher woody vegetation, may provide more effective buffer than emergent or scrub / shrub wetlands. Costanza et al. Do not find significant evidence that forest wetlands reduced economic losses, perhaps due to data limitations. We find forested and nonforested wetlands play similarly protective roles. We cannot reject the hypothesis that forest wetland reduces damage more than nonforested wetlands, as suggested by simulation studies, although our result is consistent with that of Gedan et al., Who surveyed field observation studies and found mangroves and marshes confer comparable wave attenuation. Coastal states take different strategies in terms of disaster relief and preparedness. Some adopt more stringent building codes, eg, requiring building on stilts or setting minimum construction elevation, while others do not.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Professional Interests

Dr. Aksoy has scientific interests in Data Assimilation, numerical Weather prediction, and Ensemble methods, and especially in their application to tropical and mesoscale meteorology, as well as remote sensing platforms such as radars, lidars, and satellites. He has been jointly appointed at Hurricane Research Division and the University of Miami's Cooperative Institute for Marine and Atmospheric Studies since 2008. He earned his PH. D degree from Texas & M University Department of Atmospheric Sciences in 2005. Dr. Aksoy is a member of the Data Assimilation group and is the main developer of the Hurricane Ensemble Data Assimilation System. This is an advanced Data Assimilation System design to assimilate Hurricane inner-core observations at vortex and smaller scales. Observation platforms that are commonly utlized for this purpose include tail Doppler radar, dropwindsondes, step Frequency Microwave Scatterometer, and in-situ flight-level sensors installed on NOAA P-3 and G-IV and Air Force Reserve C-130 aircraft. HEDAS is designed as a state-of-art System to carry out research on new Data Assimilation techniques and to investigate potential impacts of new observing platforms for Hurricane applications. It utilizes Ensemble Kalman filter and is interfaced with NOAA's HWRF model. Using HEDAS, Dr. Aksoy is currently actively carrying out research in parameter estimation, radar Data Assimilation, and satellite Data Assimilation. Dr. Aksoy is also actively involved in serving the Atmospheric science community through his Editor role for Monthly Weather Review published by American Meteorological Society. MWR is one of most popular scientific journals in the areas of numerical weather prediction and Data Assimilation.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

1. Introduction

In October 2012, Hurricane Sandy drove a devastating storm surge in excess of 2 m into the northeastern US coastline, tore down trees and power lines that left millions without electricity, and dumped over 900 mm of snow. As Sandy approaches the coast, it acquires structural characteristics consistent with both tropical and extratropical cyclones, with an intact inner-tropical cyclone warm core embed within an expansive outer-core wind field. Contributions from both tropical and baroclinic energy sources cause Sandy to reintensify as it approaches the coastline. TC follows an atypical track northwestward toward Northeast United States, rather than out to the Sea, fostered by interaction with upstream trough of type identified by Fujiwhara, practical predictability of which depends on the modeling system. Sandy tests existing infrastructure for hazard communication and poses challenges related to risk perception due to its atypical track and forecast structure near landfall. Few TCs produce such a broad range of impacts, but Sandy was not ordinary. Rather, Sandy is a dramatic example of direct impacts, structural evolution, and forecast challenges associated with TCs that become extratropical cyclones, process known as extratropical transition. Tropical cyclones gain energy from warm Ocean waters through evaporation and subsequent latent heat release by deep, moist convection. Storm develop warm core. As a result, with strongest winds near the surface that decrease in strength with height. Wind, precipitation, and temperature fields become more axisymmetric as TC matures. Conversely, extratropical cyclones are driven by comparatively large temperature and moisture gradients. Within these baroclinic environments, frontal boundaries separate warm, moist air from cool, dry air, resulting in highly asymmetric energy distributions to drive wind and rainfall. In addition, wind speed increases with height due to the cold-core structure of these systems. During ET, deep warm core associated with TC becomes shallow and is often replaced by cold-core, asymmetric structure, including development of surface fronts. This evolution occurs as TC moves poleward into the baroclinic environment characterized by aforementioned temperature and moisture gradients as well as increased vertical wind shear, reduce sea surface temperature, and increased Coriolis parameter. Only a subset of TCs complete ET and become fully extratropical, yet even cyclones that only begin ET can directly produce hazards and / or generate hazards downstream. Fig. 1. Download Figure Download Figure As PowerPoint slide two-stage ET classification based on Klein ET al. Onset and completion times correspond to definitions of Evans and Hart. Tropical and extratropical labels indicate approximately how the system would be regarded by the operational Forecast Center. Figure reproduced from Jones ET al. Citation: Monthly Weather Review 145 11; 10. 1175 / MWR-D17-0027. 1 earlier Review provides then-current synthesis of fundamental understanding of ET and its direct impacts. The paper also outlines significant ET-relate Forecast challenges and research needs that have yet to be address.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

2. Data and methodology

To create 10 000 years of Synthetic TC data, there are three main stages, as follow: first stage is data preparation and input, which involve two steps. In step 1. 1, as input data for STORM, we extracted TCs from global historical dataset IBTrACS 17 for the time period 1980-2018. We use data from 1980 onwards to comply with modern ERA of satellite observations. IBTrACS is a unified dataset of postseason Best-Track data produced by Tropical warning centers, for all TC basins. Here, we use all basins except the South Atlantic. Basin domains are adapted from basin domains used in IBTrACS dataset. South Atlantic has been left out as there are too few TC formations in this basin for adequate distribution and relationships fitting. Prior to extracting storms, we first unify report wind speeds to 10-minute average sustain wind speeds. This is because the definition of these Report wind speeds differ per Tropical warning center: 4 centers use either 1-minute or 3-minute average periods. 17. These wind speeds are multiplied by factor of 0. 88 to convert them to U10 18. Next, for each basin, we extract storms at all consecutive time steps where U10 is greater than 18 M / S, or where TC has not reached extratropical cyclOne-classification in IBTrACS dataset. We selected the 18 M / S-threshold to comply with Tropical STORM-classification on Saffir-Simpson Hurricane scale 19. For convenience, we refer to this subset of storms as Tropical cyclones hereafter. We linearly interpolate all extracted data to 3-hourly values. Extract tracks are shown in Fig. 1; overview of all extracted elements of IBTrACS dataset is listed in Fig. 2. Modeling of Synthetic tracks also requires information on environmental conditions such as monthly mean MSLP and sea-surface temperatures. Therefore, in stage 1, we extracted MSLP and SST fields from the European Centre for Medium-Range Weather Forecasting S fifth geneRation Climate reanalysis dataset ERA-5 20. The spatial and temporal resolution of this dataset is 0. 25 0. 25 and 1-hourly. For both variables, we calculate monthly mean values during TC seasons, as defined in Table 1. In the second stage, extracted TC tracks and characteristics from IBTrACS along with environmental conditions from ERA-5 are used as input to our Synthetic resampling algorithm called Synthetic Tropical cyclOne geneRation Model. The STORM Model follows three main steps that are visualized in Fig. 2 in the red column. In step 2. 1, STORM samples number of genesis events, and their corresponding genesis month, for every simulated year. In step 2. 2, for each of these genesis events, genesis location is determine, and, by adding consecutive changes in longitude and latitude, Synthetic Track is form. In step 2. 3, TC characteristics such as minimum pressure, maximum wind speed, and radius to maximum winds are assigned along each of these tracks. These three steps are described in detail below.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

4. Conclusion

Coastal wetland coverage is associated with statistically significant reductions in cyclone-related property damage. Loss of 1 km 2 of wetland coverage increase predicted probability of experiencing property damage during storms by 0. 02 % in county with average wetland coverage, wind speed, and flooding area. For coastal communities suffering from property damage from storm, 1 % loss of coastal wetlands is associated with 0. 6 % increase in property damage, controlling for storm-specific characteristics, property value under flooding risk, State-specific time-invariant determinants of property damage, and year-level shocks. Coefficient estimates of wind, potential storm surge area, property value under flooding risk, and being located to right-hand of storm path are positive and significant. The wind effect is particularly large and counties on storm paths ' right side experience 140 % more property damage than those on the left. Coastal wetlands protective effects are nonlinear in wind intensity, conditional on damage. This may be because once wetland vegetation is fully saturate with water, wave dissipation effects are weaker. To detect this type of nonlinearity, wetland effects are decomposed by wind speeds experienced by the county. Wetlands are effective against storms of all different magnitudes. The Elasticity of property damage with respect to wetlands is 0. 58 for tropical storm, 0. 55 for Category 1 Hurricane, 0. 40 for Category 2 Hurricane, and 0. 35 for a Category 3 to 5 Hurricane. This pattern is consistent with laboratory experiments. The preventative effect is especially strong for tropical storms, which happen twice as often as hurricanes. However, because property damage is rapidly increasing in storm strength, absolute magnitude of damage prevented is predicted to be the largest for major hurricanes. Saltwater wetlands are located closer to shore than freshwater wetlands, providing the first line of defense against storm surges. Nevertheless, freshwater wetlands typically have more coverage than saltwater wetlands, providing a wider buffer zone, as freshwater wetlands constitute about 85 % of total coastal wetland coverage. We find significant reductions in property damage for both freshwater and saltwater wetlands. The difference between their contributions is small and not significantly different from zero. This is not surprising since storm surges can extend miles inland and encompass both types of wetlands. Forest wetlands, having rougher woody vegetation, may provide a more effective buffer than emergent or scrub / shrub wetlands. Costanza et al. Do not find significant evidence that forest wetlands reduced economic losses, perhaps due to data limitations. We find forested and nonforested wetlands play similarly protective roles. We cannot reject the hypothesis that forest wetland reduces damage more than nonforested wetlands, as suggested by simulation studies, although our result is consistent with that of Gedan et al., Who surveyed field observation studies and found mangroves and marshes confer comparable wave attenuation. Coastal States take different strategies in terms of disaster relief and preparedness. Some adopt more stringent building codes, eg, requiring building on stilts or setting minimum construction elevation, while others do not.


Global Warming and Hurricanes

Observed records of Atlantic Hurricane activity show some correlation, on multi-year time-scales, between local tropical Atlantic sea surface temperatures and Power Dissipation Index see for example Fig. 3 on this EPA Climate Indicators site. PDI is an aggregate measure of Atlantic Hurricane activity, combining frequency, intensity, and duration of hurricanes in a single Index. Both Atlantic SSTs and PDI have risen sharply since the 1970s, and there is some evidence that PDI levels in recent years are higher than in previous active Atlantic Hurricane era in the 1950s and 60s. Model-base Climate Change detection / attribution studies have linked increasing tropical Atlantic SSTs to increasing greenhouse gases, but proposed links between increasing greenhouse gases and Hurricane PDI or frequency has been based on statistical correlations. The Statistical linkage of Atlantic Hurricane PDI to Atlantic SST suggests at least possibility of large anthropogenic influence on Atlantic hurricanes. If this statistical relation between tropical Atlantic SSTs and Hurricane activity is used to infer future changes in Atlantic Hurricane activity, implications are sobering: large increases in tropical Atlantic SSTs projected for late 21st Century would imply very substantial increases in Hurricane destructive potential-roughly 300 % increase in PDI by 2100. On other hand, Swanson and others note that Atlantic Hurricane Power Dissipation is also well-correlate with other SST indices besides tropical Atlantic SST alone, and in particular with indices of Atlantic SST relative to tropical mean SST. This is in fact a crucial distinction, because while the statistical relationship between Atlantic hurricanes and local Atlantic SST shown in the upper Panel of Figure 1 would imply very large increases in Atlantic Hurricane activity due to 21 Century greenhouse warming, alternative statistical relationship between PDI and relative SST measure shown in lower Panel of Figure 1 would imply only modest future long-term trends of Atlantic Hurricane activity with greenhouse warming. In the latter case, alternative relative SST measure in the lower Panel do not change very much over the 21 century, even with substantial Atlantic warming projections from climate models, because, crucially, warming project for the tropical Atlantic in models is not very different from that project for the tropics as a whole. The key question then is: Which of two future Atlantic Hurricane scenarios inferred from statistical relations in Figure 1 is more likely? To try to gain insight on this question, we have first attempt to go beyond ~50 year historical record of Atlantic hurricanes and SST to examine even longer records of Atlantic tropical storm activity and second to examine dynamical models of Atlantic Hurricane activity under global warming conditions. These separate approaches are discussed below. To gain more insight on this problem, we have attempted to analyze much longer records of Atlantic Hurricane activity.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Sources

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

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