scholarly journals Climatology of Frequency, Life Period, Energy and Speed for Tropical Disturbances and Cyclones over the Bay of Bengal

2021 ◽  
Vol 10 (1) ◽  
pp. 23-31
Author(s):  
Saurav Dey Shuvo
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Bay Of Bengal ◽  
Coastal Areas ◽  
Bengal Basin ◽  
Environmental Sciences ◽  
Total Frequency ◽  
Life Period ◽  
Basic Features ◽  
Translational Speed

Tropical disturbances and cyclones are regularly formed at the Bay of Bengal basin. There are some common traits in them, albeit each one of them is unique. Discerning climatology for the basic features of any tropical cyclone is useful in numerous ways. This research has attempted to find a climatology for frequency, life period, energy, and speed for the tropical cyclones formed at the Bay of Bengal over a period of 31 years – from 1990 to 2020. The results elicit that there are marked changes in these aforementioned features. The total frequency, accumulated duration, and combined energy have escalated over the years. To be precise, these changes have taken effect more rigorously for the Post-monsoonal tropical cyclones. The overall translational speed has slightly diminished in recent years, except for the translational speed of cyclones formed during Pre-monsoon. These changes will have major ramifications on the lives and livelihoods of people, more so for those living in coastal areas. Hence, necessary actions are required to cut the probable losses and damages. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 10(1), 2021, P 23-31

scholarly journals Segmentation of Tropical Cyclone Eye using Satellite Infrared Images

2019 ◽  
Vol 8 (2S5) ◽  
pp. 181-185
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Bay Of Bengal ◽  
Bengal Basin ◽  
Infrared Images ◽  
Circular Area ◽  
Remarkable Feature ◽  
Rule Based ◽  
Cyclone Tracking ◽  
Rule Based Classifier

The tropical cyclones are destructive weather systems and are known for their devastating effects during landfall. Cyclone tracking is one of the important tasks for the meteorologist. The eye of the tropical cyclone is the most remarkable feature. The eye of the cyclone is the roughly circular area extending over 30 - 65 km in diameter. The deepest convection is found around the eyewall for some tens of kilometers. The eye grows deeper when the cyclone becomes heavy and the winds speed grows high. In this study, the data from the 1995 - 2016 of the CIRA imagery for the tropical cyclone of the Bay of Bengal basin is analyzed and the model is developed to determine the eye of the cyclone. The segmented eye features are fed into the Rule Based Classifier which classifies the tropical cyclone images based on the presence and absence of the eye.

Numerical Modeling of Tropical Cyclone Generated Waves; Case studies of Irma, Maria and Dorian

2020 ◽  
Author(s):  
Gozde Guney Dogan ◽  
Pamela Probst ◽  
Bora Yalciner ◽  
Alessandro Annunziato ◽  
Narcisse Zahibo ◽  
...  
Keyword(s):  
Numerical Modeling ◽  
Numerical Model ◽  
Tropical Cyclone ◽  
Atlantic Ocean ◽  
Tropical Cyclones ◽  
Storm Surge ◽  
Coastal Areas ◽  
Wind Fields ◽  
Caribbean Islands ◽  
The Caribbean

<p>Tropical cyclones can be considered one type of extreme event, with their destructive winds, torrential rainfall and storm surge. Every year these natural phenomena affect millions of people around the world, leaving a trail of destruction in several countries, especially along the coastal areas. Only in 2017, two devastating major hurricanes (Irma and Maria) moved across the Caribbean and south-eastern USA, causing extensive damage and deaths. Irma formed in the far eastern Atlantic Ocean on 30 August 2017 and moved towards the Caribbean islands during the following week, significantly strengthening, becoming a Category 5 Hurricane. It caused wide-ranging impacts such as significant storm surge (up to 3m according to US National Oceanic and Atmospheric Administration, NOAA report) to several islands in the Caribbean and Florida. On the second half of September, 2017, another strong Category 5 Hurricane named Maria formed over the Atlantic and moved west towards the Caribbean Sea. Maria also caused several impacts and severe damage in Caribbean Islands, Puerto Rico and the U.S. Virgin Islands due to high speed winds, rainfall, flooding and storm surge with a maximum runup of 3.7 m (US NOAA) on the southern tip of Dominica Island. The most recent devastating event for the Atlantic is Hurricane Dorian. It formed on August 24, 2019 over the Atlantic Ocean and it moved towards the Caribbean islands, as getting stronger as moving, becoming a Category 5 before reaching the Bahamas, where it left a trail of destruction after its passage. The major effect of Dorian was on north-western Bahamas with very strong winds, heavy rainfall and a large storm surge.</p><p>In this context, a rapid and reliable modeling of storm surge generated by such kind of events is essential for many purposes such as early accurate assessment of the situation, forecasting, estimation of potential impact in coastal areas, and operational issues like emergency management.</p><p>A numerical model, NAMI DANCE GPU T-SS (Tsunami-Storm Surge) is developed building up on tsunami numerical model NAMI DANCE GPU version to solve nonlinear shallow water equations, using the pressure and wind fields as inputs to compute spatial and temporal distribution of water level throughout the study domain and respective inundation related to tropical cyclones, based on the equations used in the HyFlux2 Code developed by the Joint Research Centre of the European Commission. The code provides a rapid calculation since it is structured for Graphical Processing Unit (GPU) using CUDA API.</p><p>NAMI DANCE GPU T-SS has been applied to many cases as regular shaped basins under circular static and dynamic pressure fields separately and also different wind fields for validation together with combinations of pressure and wind fields. This study has been conducted to investigate the potential of numerical modeling of tropical cyclone generated storm surge based on recent events Irma, Maria and Dorian. The results are presented and discussed based on comparison with the measurements and observations. The study shows promise for developing a cyclone modeling capability based on available measurement and observational data.</p>

Supervised Machine Learning Techniques to Assess Tropical Cyclones in Bay of Bengal and Bangladesh

2021 ◽  
Author(s):  
Mariam Hussain ◽  
Seon Ki Park
Keyword(s):  
Machine Learning ◽  
Tropical Cyclone ◽  
Linear Regression ◽  
Tropical Cyclones ◽  
Bay Of Bengal ◽  
Early Warning Systems ◽  
Supervised Machine Learning ◽  
Extreme Weather Events ◽  
Economic Losses ◽  
Coastal Regions

<p>Bangladesh experiences extreme weather events such as heavy rainfall due to monsoon, tropical cyclones, and thunderstorms resulting in floods every year. Regular flood events significantly affect in agricultural industries and human lives for economic losses. One of the reasons for these weather phenomena to sustain is latent heat release from Bay of Bengal (BoB) and Southeast Tropical Indian Ocean (SETIO). As the country has limited observations from stations and oceans, modeling for numerical weather prediction (NWP) are challenging for local operations. For operational NWP, computational resources and time are also concerns for a developing country like Bangladesh. Besides, recent machine learning (ML) techniques are widely applied to study various meteorological events with efficient results. Therefore, this research aims to estimate predictability and accuracy of supervised ML for tropical cyclones by assessing air temperature at 2 meter (AT) and sea surface temperature (SST). For AT and SST, the study utilizes monthly data at 0.25 × 0.25<sup>o</sup> horizontal resolution provided by the ECMWF reanalysis (ERA5). The gridded data is downscaled to area of interests such as coastal regions, BoB and SEITO with a study period of 40 years from 1979 to 2018. Furthermore, Bangladesh Meteorological Department (BMD) provides AT for 36 years from 1979 to 2015. The experiments segregate into two sections: (1) data normalizations via linear regression (LR) and multi-linear regression (MLR) and (2) supervised ML techniques applications in Matlab 2018b. The pre-processed data for LR show that AT from coastal regions such as Chittagong (CG), Barishal (BR), and Khulna (KL) divisions have stronger correlations (R) to SST in BOB with R = 0.910, 0.850, and 0.846 respectively than SEITO (R = 0.698, 0.675 and 0.678 respectively). Moreover, for these three regions, the correlation of MLR is 0.916 and 0.745 for BoB and SEITO with residual standard error (RSE) 1.312 and 1.218 respectively. For supervised ML applications, coarse decision tree (CDT) predict SST based on AT with train (80%) and test (20%) of the ERA5 data. Finally, the results from CDT model indicate that SST predictions are possible with 98.5% accuracy based on coastal stations. The trained CDT also validated model prediction utilizing observed AT (BMD observations) to forecast monthly SST and found 85% accuracy for monthly time series. In conclusions, CDT can predict SST from station data and assess if there is any possibility for tropical cyclone formation. The future works include further assessment for various categories of tropical cyclone and predict their intensity based on SSTs. This research aims to contribute in disaster mitigation by improving early warning systems. The possibility of cyclone formations will help for preparedness in saving property damages in Bangladesh.</p>

scholarly journals Extreme Translation Events of Atlantic Tropical Cyclones

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1032
Author(s):  
Wei Zhang
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
North Atlantic ◽  
Large Scale ◽  
Spatial And Temporal Variability ◽  
Pressure System ◽  
The North ◽  
The North Atlantic ◽  
Moving Groups ◽  
Translational Speed

Changes in the translational speed of tropical cyclones (e.g., sluggish tropical cyclones) are associated with extreme precipitation and flash flooding. However, it is still unclear regarding the spatial and temporal variability of extreme tropical cyclone translation events in the North Atlantic and underlying large-scale drivers. This work finds that the frequencies of extreme fast- and slow-translation events of Atlantic tropical cyclones exhibited a significant rising trend during 1980–2019. The extreme fast-translation events of Atlantic tropical cyclones are primarily located in the northern part of the North Atlantic, while the extreme slow-translation events are located more equatorward. There is a significant rising trend in the frequency of extreme slow-translation events over ocean with no trend over land. However, there is a significant rising trend in the frequency of extreme fast-translation events over ocean and over land. The extreme slow-translation events are associated with a strong high-pressure system in the continental United States (U.S.). By contrast, the extreme fast-translation events are related to a low-pressure system across most of the continental U.S. that leads to westerly steering flow that enhances tropical cyclone movement. This study suggests that it might be useful to separate tropical cyclone events into fast-moving and slow-moving groups when examining the translational speed of North Atlantic tropical cyclones, instead of examining regional or global mean translational speed.

scholarly journals Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks

2021 ◽  
Author(s):  
Tim Willem Bart Leijnse ◽  
Alessio Giardino ◽  
Kees Nederhoff ◽  
Sofia Caires
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Storm Surge ◽  
Bay Of Bengal ◽  
Coastal Hazards ◽  
Wind Fields ◽  
Tropical Cyclone Frequency ◽  
Wind Speeds ◽  
Wave Heights ◽  
Cyclone Frequency

Abstract. Deriving reliable estimates of design water levels and wave conditions resulting from tropical cyclones is a challenging problem of high relevance for, among others, coastal and offshore engineering projects and risk assessment studies. Tropical cyclone geometry and wind speeds have been recorded for the past few decades only, therefore resulting in poorly reliable estimates of the extremes, especially at regions characterized by a low number of past tropical cyclone events. In this paper, this challenge is overcome by using synthetic tropical cyclone tracks and wind fields generated by the open source tool TCWiSE (Tropical Cyclone Wind Statistical Estimation), to create thousands of realizations representative for 1,000 years of tropical cyclone activity for the Bay of Bengal. Each of these realizations is used to force coupled storm surge and wave simulations by means of the processed-based Delft3D Flexible Mesh Suite. It is shown that the use of synthetic tracks provides reliable estimates of the statistics of the first-order hazard (i.e. wind speed) compared to the statistics derived for historical tropical cyclones. Based on estimated wind fields, second-order hazards (i.e. storm surge and waves) are computed. The estimates of the extreme values derived for wind speed, wave height and storm surge are shown to converge within the 1,000 years of simulated cyclone tracks. Comparing second-order hazard estimates based on historical and synthetic tracks show that, for this case study, the use of historical tracks (a deterministic approach) leads to an underestimation of the mean computed storm surge up to −30 %. Differences between the use of synthetic versus historical tracks are characterized by a large spatial variability along the Bay of Bengal, where regions with a lower probability of occurrence of tropical cyclones show the largest difference in predicted storm surge and wave heights. In addition, the use of historical tracks leads to much larger uncertainty bands in the estimation of both storm surges and wave heights, with confidence intervals being +80 % larger compared to those estimated by using synthetic tracks (probabilistic approach). Based on the same tropical cyclone realizations, the effect that changes in tropical cyclone frequency and intensity, possibly resulting from climate change, may have on modelled storm surge and wave heights were computed. An increase in tropical cyclone frequency of +25.6 % and wind intensity of +1.6 %, based on literature values, could result in an increase of storm surge and wave heights of +11 % and +9 % respectively. This suggest that climate change could increase tropical cyclone induced coastal hazards more than just the actual increase in maximum wind speeds.

scholarly journals Tropical cyclone frequency in the north Indian Ocean in relation to southern oscillation phenomenon

MAUSAM ◽  
2022 ◽  
Vol 52 (3) ◽  
pp. 511-514
Author(s):  
O. P. SINGH ◽  
TARIQ MASOOD ALI KHAN ◽  
MD. SAZEDUR RAHMAN
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
Bay Of Bengal ◽  
Arabian Sea ◽  
Southern Oscillation ◽  
North Indian Ocean ◽  
Tropical Cyclone Frequency ◽  
The North ◽  
Cyclone Frequency

The present paper deals with the influence of Southern Oscillation (SO) on the frequency of tropical cyclones in the north Indian Ocean. The results show that during the negative phase of SO the frequency of tropical cyclones and depressions over the Bay of Bengal and the Arabian Sea diminishes in May which is most important pre-monsoon cyclone month. The correlation coefficient between the frequency of cyclones and depressions and the Southern Oscillation Index (SOI) is +0.3 which is significant at 99% level. Post-monsoon cyclone frequency in the Bay of Bengal during November shows a significant positive correlation with SOl implying that it also decreases during the negative phase of SO. Thus there is a reduction in the tropical cyclone frequency over the Bay of Bengal during both intense cyclone months May and November in EI-Nino/Southern Oscillation (ENSO) epochs. Therefore it would not be correct to say that ENSO has no impact on the cyclogenesis in the north Indian Ocean. It is true that ENSO has no significant impact on the frequency of cyclones in the Arabian Sea. ENSO also seems to affect the rate of intensification of depressions to cyclone stage. The rate of intensification increases in May and diminishes in November in the north Indian Ocean during ENSO. The results are based on the analysis of monthly frequencies of tropical cyclones and depressions and SOI for the 100 year period from 1891-1990.

scholarly journals Impact Analysis of Tropical Cyclone Cempaka-Dahlia on Wave Heights in Indonesian Waters from Numerical Model and Altimetry Satellite

2019 ◽  
Author(s):  
Wiwin Windupranata ◽  
Candida A.D.S. Nusantara ◽  
Dudy D. Wijaya ◽  
Kosasih Prijatna
Keyword(s):  
Numerical Modeling ◽  
Tropical Cyclone ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
Wave Height ◽  
Power Plants ◽  
Impact Analysis ◽  
Observation Point ◽  
Coastal Areas ◽  
The Indian Ocean

Indonesia is located side by side with the Pacific Ocean and the Indian Ocean where there are often tropical cyclones in these two oceans. As was the case some time ago in the Indian Ocean a tropical cyclone of Cempaka and Dahlia occurred which had a significant impact on Indonesian areas. Another impact felt is the disruption of economic activity in the area of tourism, ports, and power plants. Numerical modeling is carried out to simulate the phenomena of Cempaka and Dahlia tropical cyclones to determine the impacts caused especially in Lampung to Lombok areas. Numerical modeling is done using SWAN version 41.20. SWAN was chosen because it has good modeling calculations in coastal areas and is very suitable for wave analysis in coastal areas. The results of the modeling are verified by the significant wave height correlation coefficient from altimetry satellites that cross the tropical cyclones of Cempaka and Dahlia. The results showed a significant increase in wave height in the study area with an increase of up to 1028.31% at the observation point in Pelabuhan Ratu, West Java Province.

scholarly journals Spatio-temporal Behaviours of Tropical Cyclones over the Bay of Bengal Basin in last Five Decades

2021 ◽  
Author(s):  
Manas Mondal ◽  
Anupam Biswas ◽  
Subrata Haldar ◽  
Somnath Mandal ◽  
Subhasis Bhattacharya ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
Bay Of Bengal ◽  
Bengal Basin ◽  
Spatio Temporal
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