Investigation of Cluster-Based Cyclone Track Pattern Within the Bay of Bengal
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Published:2022-09-30
Issue:Early View
Volume:11
Page:320-330
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ISSN:2147-9666
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Container-title:Marine Science and Technology Bulletin
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language:en
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Short-container-title:Mar. Sci. Tech. Bull.
Author:
AL MOHİT Md. Abdul1, TOWHİDUZZAMAN Md.2, KUMAR JOARDAR Atish1, NASRİN Mossa. Samima1, KHATUN Mst. Rabiba1
Affiliation:
1. Islamic University, Department of Mathematics, Kushtia-7003, Bangladesh 2. Uttara University, Department of Electrical & Electronic Engineering, Dhaka-1230, Bangladesh
Abstract
Bangladesh is a highly disaster prone flat land country in south Asia. 80% of the disaster comes from cyclonic disaster around this area. To investigate the damage risk due to the cyclonic event around the Bay of Bengal associated with the cyclone track (CT) is an important issue. The present study has extensive analysis on generating a most favorable track along the Bay of Bengal from the MRI-AGCM cyclone track data. We have investigated present (1978-2003) and future (2075-2099) track data from the MRI-AGCM data set to ensure the synthetic track for the present and future climate conditions of Bangladesh. A k-mean clustering technique has been applied to investigate the synthetic track for the present and future climate condition. This work may insight the changes in cyclone track patterns in both the present and future climate conditions with the global warming scenario. This study has found that the Sundarbans and its adjacent areas are the risky coastline area of the landfall zone and for the global warming scenario it will be shifted to the Odisha area in India.
Publisher
Marine Science and Technology Bulletin
Subject
General Earth and Planetary Sciences,General Environmental Science
Reference31 articles.
1. Al Mohit, M. A., & Towhiduzzaman, M. (2022). A numerical estimate of water level elevation due to a cyclone associated with a different landfall angle. Sains Tanah, 19(1), 33–41. https://doi.org/10.20961/stjssa.v19i1.56600 2. Al Mohit, M. A., Yamashiro, M., Hashimoto, N., Mia, M. B., Ide, Y., & Kodama, M. (2018a). Impact assessment of a major river basin in Bangladesh on storm surge simulation. Journal of Marine Science and Engineering, 6(3), 99. https://doi.org/10.3390/JMSE6030099 3. Al Mohit, M. A., Yamashiro, M., Ide, Y., Kodama, M., & Hashimoto, N. (2018b). Tropical cyclone activity analysis using MRI-AGCM and d4PDF data. Proceedings of The 28th International Ocean and Polar Engineering Conference, Japan, pp. 852–859. 4. Chen, G., Yu, H., Cao, Q., & Zeng, Z. (2013). The Performance of Global Models in TC Track Forecasting Over the Western North Pacific from 2010 to 2012. Tropical Cyclone Research and Review, 2(3), 149–158. https://doi.org/10.6057/2013TCRR03.02 5. Gao, S., Zhao, P., Pan, B., Li, Y., Zhou, M., Xu, J., Zhong, S., & Shi, Z. (2018). A nowcasting model for the prediction of typhoon tracks based on a long short term memory neural network. Acta Oceanologica Sinica, 37(5), 8–12. https://doi.org/10.1007/s13131-018-1219-z
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