Introduction to WIND CHARACTERISTICS: STRONG WINDS & TROPICAL CYCLONES

The coral reef ecosystem in Cu Lao Cham, Vietnam is part of the central zone of the Cu Lao Cham -Hoi An, a biosphere reserve and it is strictly protected. However, the impacts of natural disasters - tropical cyclones (TCs) go beyond human protection. The characteristic feature of TCs is strong winds and the consequences of strong winds are high waves. High waves caused by strong TCs (i.e. level 13 or more) cause decline in coral cover in the seas around Cu Lao Cham. Based on the relationship between sea surface temperature (SST) and the maximum potential intensity (MPI) of TCs, this research determines the number of strong TCs in Cu Lao Cham in the future. Using results from a regional climate change model, the risk is that the number of strong TCs in the period 2021-2060 under the RCP4.5 scenario, will be 3.7 times greater than in the period 1980-2019 and under the RCP 8.5 scenario it will be 5.2 times greater than in the period 1980-2019. We conclude that increases in SST in the context of climate change risks will increase the number and intensity of TCs and so the risk of their mechanical impact on coral reefs will be higher leading to degradation of this internationally important site.

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Compound Events of Tropical Cyclone and Flooding in India

10.5194/egusphere-egu21-15759 ◽

2021 ◽

Author(s):

Akshay Rajeev ◽

Vimal Mishra

Keyword(s):

Soil Moisture ◽

Tropical Cyclones ◽

Heavy Rain ◽

Disaster Mitigation ◽

Infiltration Capacity ◽

Monsoon Period ◽

Antecedent Soil Moisture ◽

Vic Model ◽

Compound Events ◽

Strong Winds

<p>India is severely affected by tropical cyclones (TC) each year, which generates intense rainfall and strong winds leading to flooding. Most of the TC induced floods have been attributed to heavy rain associated with them. Here we show that both rainfall and elevated antecedent soil moisture due to temporally compounding tropical cyclones cause floods in the major Indian basins. We assess each basin's response to observed TC events from 1980 to 2019 using the Variable Infiltration Capacity (VIC) model. The VIC model was calibrated (R2 > 0.5) and evaluated against observed hourly streamflow for major river basins in India. We find that rainfall due to TC does not result in floods in the basin, even for rainfall intensities similar to the monsoon period. However, TCs produce floods in the basins, when antecedent soil moisture was high. Our findings have implications for the understanding of TC induced floods, which is crucial for disaster mitigation and management.</p>

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Wind characteristics observed in the vicinity of tropical cyclones: An investigation of the gradient balance and super-gradient flow

Wind and Structures ◽

10.12989/was.2014.19.3.249 ◽

2014 ◽

Vol 19 (3) ◽

pp. 249-270 ◽

Cited By ~ 6

Author(s):

K.T. Tse ◽

S.W. Li ◽

C.Q. Lin ◽

P.W. Chan

Keyword(s):

Tropical Cyclones ◽

Gradient Flow ◽

Wind Characteristics

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Typhoon Type Index: A New Index for Understanding the Rain or Wind Characteristics of Typhoons and Its Application to Agricultural Losses and Crop Vulnerability

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-19-0208.1 ◽

2020 ◽

Vol 59 (5) ◽

pp. 973-989

Author(s):

Yuan-Chien Lin ◽

Wen-Hsin Wang ◽

Chun-Yeh Lai ◽

Yong-Qing Lin

Keyword(s):

Heavy Rainfall ◽

Leafy Vegetables ◽

General Situation ◽

Strong Wind ◽

Novel Approach ◽

Bamboo Shoots ◽

Typhoon Intensity ◽

Wind Characteristics ◽

Strong Winds ◽

Different Characteristics

AbstractHeavy rainfall and strong wind are the two main sources of disasters that are caused by tropical cyclones (TCs), and typhoons with different characteristics may induce different agricultural losses. Traditionally, the classification of typhoon intensity has not considered the amount of rainfall. Here, we propose a novel approach to calculate the typhoon type index (TTI). A positive TTI represents a “wind type” typhoon, where the overall damage in a certain area from TCs is dominated by strong wind. On the other hand, a negative TTI represents a “rain type” typhoon, where the overall damage in a certain area from TCs is dominated by heavy rainfall. From the TTI, the vulnerability of crop losses from different types of typhoons can be compared and explored. For example, Typhoon Kalmaegi (2008) was classified as a rain-type typhoon (TTI = −1.22). The most affected crops were oriental melons and leafy vegetables. On the contrary, Typhoon Soudelor (2015) was classified as a significant wind-type typhoon in most of Taiwan (TTI = 1.83), and the damaged crops were mainly bananas, bamboo shoots, pomelos, and other crops that are easily blown off by strong winds. Through the method that is proposed in this study, we can understand the characteristics of each typhoon that deviate from the general situation and explore the damages that are mainly caused by strong winds or heavy rainfall at different locations. This approach can provide very useful information that is important for the disaster analysis of different agricultural products.

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A mask R-CNN model for reidentifying extratropical cyclones based on quasi-supervised thought

Scientific Reports ◽

10.1038/s41598-020-71831-z ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Chuhan Lu ◽

Yang Kong ◽

Zhaoyong Guan

Keyword(s):

Tropical Cyclones ◽

Traditional Method ◽

Target Identification ◽

Meteorological Data ◽

Model Performance ◽

Extratropical Cyclones ◽

Research Fields ◽

Considerable Portion ◽

Strong Winds ◽

Applications Of Machine Learning

Abstract The applications of machine learning/deep learning (ML/DL) methods in meteorology have developed considerably in recent years. Massive amounts of meteorological data are conducive to improving the training effect and model performance of ML/DL, but the establishment of training datasets is often time consuming, especially in the context of supervised learning. In this paper, to identify the two-dimensional (2D) structures of extratropical cyclones in the Northern Hemisphere, a quasi-supervised reidentification method for extratropical cyclones is proposed. This method first uses a traditional automatic cyclone identification method to construct a trainable labeled dataset and then reidentifies extratropical cyclones in a quasi-supervised fashion by using a (pre-trained) Mask region-based convolutional neural network (Mask R-CNN) model. In comparison, the new method increases the number of identified cyclones by 8.29%, effectively supplementing the traditional method. The newly recognized cyclones are mainly shallow or moderately deep subsynoptic-scale cyclones. However, a considerable portion of the new cyclones along the coastlines of the oceans are accompanied by strong winds. In addition, the Mask R-CNN model also shows good performance in identifying the horizontal structures of tropical cyclones. The quasi-supervised concept proposed in this paper may shed some light on accurate target identification in other research fields.

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Machine Learning in Tropical Cyclone Forecast Modeling: A Review

Atmosphere ◽

10.3390/atmos11070676 ◽

2020 ◽

Vol 11 (7) ◽

pp. 676

Author(s):

Rui Chen ◽

Weimin Zhang ◽

Xiang Wang

Keyword(s):

Machine Learning ◽

Tropical Cyclone ◽

Tropical Cyclones ◽

Numerical Models ◽

Weather Forecasts ◽

Dynamic Mechanisms ◽

Forecast Models ◽

Strong Winds ◽

The Many ◽

Axisymmetric Structures

Tropical cyclones have always been a concern of meteorologists, and there are many studies regarding the axisymmetric structures, dynamic mechanisms, and forecasting techniques from the past 100 years. This research demonstrates the ongoing progress as well as the many remaining problems. Machine learning, as a means of artificial intelligence, has been certified by many researchers as being able to provide a new way to solve the bottlenecks of tropical cyclone forecasts, whether using a pure data-driven model or improving numerical models by incorporating machine learning. Through summarizing and analyzing the challenges of tropical cyclone forecasts in recent years and successful cases of machine learning methods in these aspects, this review introduces progress based on machine learning in genesis forecasts, track forecasts, intensity forecasts, extreme weather forecasts associated with tropical cyclones (such as strong winds and rainstorms, and their disastrous impacts), and storm surge forecasts, as well as in improving numerical forecast models. All of these can be regarded as both an opportunity and a challenge. The opportunity is that at present, the potential of machine learning has not been completely exploited, and a large amount of multi-source data have also not been fully utilized to improve the accuracy of tropical cyclone forecasting. The challenge is that the predictable period and stability of tropical cyclone prediction can be difficult to guarantee, because tropical cyclones are different from normal weather phenomena and oceanographic processes and they have complex dynamic mechanisms and are easily influenced by many factors.

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An Analytical Framework for the Investigation of Tropical Cyclone Wind Characteristics over Different Measurement Conditions

Applied Sciences ◽

10.3390/app9245385 ◽

2019 ◽

Vol 9 (24) ◽

pp. 5385 ◽

Cited By ~ 4

Author(s):

Lixiao Li ◽

Yizhuo Zhou ◽

Haifeng Wang ◽

Haijun Zhou ◽

Xuhui He ◽

...

Keyword(s):

Tropical Cyclones ◽

Field Measurements ◽

Analytical Framework ◽

Reference Station ◽

Complex Flow ◽

Integral Scale ◽

Gust Factor ◽

Wind Characteristics ◽

Averaging Time ◽

Complex Flow Structure

Wind characteristics (e.g., mean wind speed, gust factor, turbulence intensity and integral scale, etc.) are quite scattered in different measurement conditions, especially during typhoon and/or hurricane processes, which results in the structural engineer ambiguously determining the wind parameters in wind-resistant design of buildings and structures in cyclone-prone regions. In tropical cyclones (including typhoons and hurricanes), the inconsistent wind characteristics may be in part ascribed to the complex flow structure with the coexistence of both mechanical and convective turbulence in the boundary layer of tropical cyclones. Another significant contribution to the scattered wind characteristics is due to various measurement conditions (e.g., terrain exposure and height) and data processing schemes (e.g., averaging time). The removal of the inconsistency in the field-measurement system may offer a more rational comparison of measured wind data from various observation platforms, and hence facilitates a better identification scheme of the wind characteristics to guide the urban planning design and wind-resistant design of buildings and structures. In this study, an analytical framework was firstly proposed to eliminate the potential observation-related effects in wind characteristics and then the wind characteristics of seven field measured tropical cyclones (four typhoons and three hurricanes) were comparatively investigated. Specifically, field measurements of wind characteristics were converted to a standard reference station with a roughness length of 0.03 m, observation duration of 10 min for mean wind and averaging time of 3 s for gusty wind at a 10 m height. The differences of the measured wind characteristics between the typhoons and hurricanes were highlighted. The standardized turbulent wind characteristics under the analytical framework for typhoons and hurricanes were compared with the corresponding recommendations in standard of American Society of Civil Engineers (ASCE 7-10) and Architectural Institute of Japan Recommendations for Loads on Buildings (AIJ-RLB-2004).

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Dynamical and Precipitation Structures of Poleward-Moving Tropical Cyclones in Eastern Canada, 1979–2005

Monthly Weather Review ◽

10.1175/2008mwr2578.1 ◽

2009 ◽

Vol 137 (3) ◽

pp. 836-851 ◽

Cited By ~ 18

Author(s):

Shawn M. Milrad ◽

Eyad H. Atallah ◽

John R. Gyakum

Keyword(s):

Tropical Cyclones ◽

Late Summer ◽

Extreme Rainfall ◽

Heavy Precipitation ◽

Main Concern ◽

Eastern Canada ◽

Strong Winds ◽

Regional Reanalysis ◽

Using Data ◽

Environmental Prediction

Abstract Tropical cyclones in the western North Atlantic basin are a persistent threat to human interests along the east coast of North America. Occurring mainly during the late summer and early autumn, these storms often cause strong winds and extreme rainfall and can have a large impact on the weather of eastern Canada. From 1979 to 2005, 40 named (by the National Hurricane Center) tropical cyclones tracked over eastern Canada. Based on the time tendency of the low-level (850–700 hPa) vorticity, the storms are partitioned into two groups: “intensifying” and “decaying.” The 16 intensifying and 12 decaying cases are then analyzed using data from both the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and the NCEP global reanalysis. Composite dynamical structures are presented for both partitioned groups, utilizing both quasigeostrophic (QG) and potential vorticity (PV) perspectives. It is found that the proximity to the tropical cyclone and subsequent negative tilt (or lack thereof) of a precursor trough over the Great Lakes region is crucial to whether a storm “intensifies” or “decays.” Heavy precipitation is often the main concern when tropical cyclones move northward into the midlatitudes. Therefore, analyses of storm-relative precipitation distributions show that storms intensifying (decaying) as they move into the midlatitudes often exhibit a counterclockwise (clockwise) rotation of precipitation around the storm center.

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