A Simple Trajectory Model for Climatological Study of Tropical Cyclones

Shan, Kaiyue; Yu, Xing

doi:10.1175/jcli-d-20-0285.1

Cited by 9 publications

(4 citation statements)

References 43 publications

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“…In addition, the worry of evacuation also caused widespread fuel shortage throughout the state of Florida [25], which made it difficult for those who really needed to evacuate. The uncertainty of track forecasting in advance (e.g., more than 3 d) is largely due to the uncertainty of the forecast of the environmental factors themselves [27][28][29]. It may not be helpful for planning an evacuation a long time in advance (such as 4-5 d before the hurricane landing).…”

Section: Discussionmentioning

confidence: 99%

Integrating storm surge modeling with traffic data analysis to evaluate the effectiveness of hurricane evacuation

Huang

Yin

Ghorbanzadeh

et al. 2021

Front. Struct. Civ. Eng.

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An integrated storm surge modeling and traffic analysis were conducted in this study to assess the effectiveness of hurricane evacuations through a case study of Hurricane Irma. The Category 5 hurricane in 2017 caused a record evacuation with an estimated 6.8 million people relocating statewide in Florida. The Advanced Circulation (ADCIRC) model was applied to simulate storm tides during the hurricane event. Model validations indicated that simulated pressures, winds, and storm surge compared well with observations. Model simulated storm tides and winds were used to estimate the area affected by Hurricane Irma. Results showed that the storm surge and strong wind mainly affected coastal counties in south-west Florida. Only moderate storm tides (maximum about 2.5 m) and maximum wind speed about 115 mph were shown in both model simulations and Federal Emergency Management Agency (FEMA) post-hurricane assessment near the area of hurricane landfall. Storm surges did not rise to the 100-year flood elevation level. The maximum wind was much below the design wind speed of 150–170 mph (Category 5) as defined in Florida Building Code (FBC) for south Florida coastal areas. Compared with the total population of about 2.25 million in the six coastal counties affected by storm surge and Category 1–3 wind, the statewide evacuation of approximately 6.8 million people was found to be an over-evacuation due mainly to the uncertainty of hurricane path, which shifted from south-east to south-west Florida. The uncertainty of hurricane tracks made it difficult to predict the appropriate storm surge inundation zone for evacuation. Traffic data were used to analyze the evacuation traffic patterns. In south-east Florida, evacuation traffic started 4 days before the hurricane’s arrival. However, the hurricane path shifted and eventually landed in south-west Florida, which caused a high level of evacuation traffic in south-west Florida. Over-evacuation caused Evacuation Traffic Index (ETI) to increase to 200% above normal conditions in some sections of highways, which reduced the effectiveness of evacuation. Results from this study show that evacuation efficiency can be improved in the future by more accurate hurricane forecasting, better public awareness of real-time storm surge and wind as well as integrated storm surge and evacuation modeling for quick response to the uncertainty of hurricane forecasting.

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Section: Discussionmentioning

confidence: 99%

Integrating storm surge modeling with traffic data analysis to evaluate the effectiveness of hurricane evacuation

Huang

Yin

Ghorbanzadeh

et al. 2021

Front. Struct. Civ. Eng.

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show abstract

“…In addition, tropical rainfall is produced by different cloud and storm systems, such as monsoon depressions, typhoons (hurricanes), mesoscale convective systems and the Madden-Julian oscillation. Some pioneering studies have been done for the annual cycle changes of typhoons/hurricanes (Shan et al 2023) and Madden-Julian Oscillation (Bui and Hsu 2023) and definitely more researches are needed in the future. Quantifying rainfall changes requires understanding the contributions of different storm types to the tropical rainfall annual cycles and how they respond to global warming.…”

Section: Quantificationmentioning

confidence: 99%

Advances in understanding the changes of tropical rainfall annual cycle: a review

Song,

Leung,

et al. 2023

Environ. Res.: Climate

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Aided by progress in the theoretical understanding, new knowledge on tropical rainfall annual cycle changes under a global warming background has been advanced in the past decade. In this review, we focus on recent advances in understanding the changes of tropical rainfall annual cycle, including its four distinct features: amplitude, spatial pattern shift, phase and wet/dry season length changes. Here, amplitude refers to the local summer-winter rainfall contrast and phase refers to the timing of rainfall annual cycle. In a warming climate, the amplitude is enhanced, more evidently over ocean, while the phase is delayed, mainly over land. The former is explained by the wet-get-wetter mechanism and the latter is explained by the enhanced effective atmospheric heat capacity and increased convective barrier. The phase delay over land has already emerged in the past four decades. The spatial pattern shift under warming is marked by two features: equatorward shift of the ITCZ throughout the year and the land-to-ocean precipitation shift in the rainy season. The former is explained by the upped-ante mechanism and/or related to the enhanced equatorial warming in a warmer world. The latter may be caused by the opposite land and ocean surface temperature annual cycle changes in the tropics. Over tropical rainforest regions such as Amazon and Congo Basin, the dry season has lengthened in the recent decades, but the fundamental reason is still unclear. Despite the notable progress in the research of tropical rainfall annual cycle change, many gaps remain in understanding its mechanisms, quantifying and attributing its historical emergence, narrowing the inter-model uncertainty of its future projections, and evaluating its global impact, motivating future work guided by some directions proposed in this review.

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“…The trajectory of a TC is highly influenced by the large‐scale atmospheric environment such as by the monsoon circulation, intraseasonal variability associated with for example, the MJO, position and strength of the subtropical anticyclone and the presence of tropical upper‐tropospheric troughs (Camargo et al., 2007; Chen et al., 2009; Shan & Yu, 2020). Other factors at play are the TC's own circulation (Li & Wang, 1992), SSTs (Wang et al., 2021) and the genesis location (Harr & Elsberry, 1991).…”

Section: Atmospheric Forcingmentioning

confidence: 99%

Key Factors Modulating the Threat of the Arabian Sea's Tropical Cyclones to the Gulf Countries

2022

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The Arabian Sea is a body of water located in the northwestern Indian Ocean. It extends to about 25°N and from the shores of Somalia on the western side, to the Arabian Peninsula (AP), Iran and Pakistan on the northwestern and northern sides, and western India on the eastern side (Qasim, 1982). The atmospheric circulation here is dominated by the Asian monsoon system, with prevailing low-level southwesterly winds in the summer season and northeasterly winds in the winter months (Schott et al., 2009). Tropical cyclone (hereafter TC) formation is unfavorable in July and August due to the (a) presence of substantial vertical wind shear, when the strong low-level southwesterly winds co-occur with upper-level easterlies associated with the Asian summer monsoon anticyclone (Basha et al., 2020), and (b) copious amounts of precipitation that lower the sea surface temperatures (SSTs) through local air-sea interactions (Fu et al., 2002). Hence, TCs typically form in the pre-monsoon (May and June) or post-monsoon (October and November) months, with an average of one storm per season (Al-Maskari, 2012;Gray, 1968). Cooler SSTs, prevailing anticyclonic relative vorticity, and higher vertical wind shear arising from the presence of the subtropical jet at upper-levels prevent storms from developing in the winter months (Evan & Camargo, 2011). The main trigger of TC formation in the northern Indian Ocean is the Madden-Julian Oscillation (MJO; Madden & Julian, 1994), which is the dominant mode of tropical intraseasonal (30-90 days) variability. The MJO is characterized by regions of enhanced/suppressed convection that propagate eastwards across the tropics, with a poleward component in the boreal summer season (Kawamura et al., 1996). Krishnamohan et al. (2012) reported that roughly 82% of the TCs which formed in the area from 1979 to 2008 occurred during active phase of the MJO. There is also considerable interannual variability, with the higher SSTs and a more favorable dynamic and thermodynamic forcing in Central Pacific El Nino and Southern Oscillation (ENSO; Ashok & Yamagata, 2009) as opposed to eastern Pacific El Nino Southern Oscillation (ENSO) events, leading to an increased likelihood of TC formation in the Arabian Sea (Sumesh & Ramesh Kumar, 2013). The recent increase in SSTs in the basin in conjunction with the Pacific Decadal Oscillation (Mantua & Hare, 2002) also contributes to more active TC seasons (Rajeevan et al., 2013).While TCs are regularly seen in the Arabian Sea, it is rare for them to move into the Sea of Oman, a small body of water that borders Oman, the United Arab Emirates (UAE), Iran and western Pakistan. Only two such occurrences have been reported since 1900 (Mahmoud, 2021): TC Gonu in 2007 which, as of 2021, is the strongest TC

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A Simple Trajectory Model for Climatological Study of Tropical Cyclones

Cited by 9 publications

References 43 publications

Integrating storm surge modeling with traffic data analysis to evaluate the effectiveness of hurricane evacuation

Integrating storm surge modeling with traffic data analysis to evaluate the effectiveness of hurricane evacuation

Advances in understanding the changes of tropical rainfall annual cycle: a review

Key Factors Modulating the Threat of the Arabian Sea's Tropical Cyclones to the Gulf Countries

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