Investigation of Cluster-Based Cyclone Track Pattern Within the Bay of Bengal
Yıl 2022,
Cilt: 11 Sayı: 3, 320 - 330, 30.09.2022
Md. Abdul Al Mohit
,
Md. Towhiduzzaman
,
Atish Kumar Joardar
Mossa. Samima Nasrin
Mst. Rabiba Khatun
Öz
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.
Destekleyen Kurum
University Grant Commission (UGC) of Bangladesh, Department of Mathematics (Islamic University, Bangladesh)
Teşekkür
The first author acknowledges receipt of a project from the University Grant Commission (UGC) of Bangladesh. The first author would like to express his gratitude to the Government of Bangladesh for the project fund during the research period. We would also like to thank the staff of the Department of Mathematics (Islamic University, Bangladesh) for their endless support of laboratory facilities.
Kaynakça
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- 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
- 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.
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- 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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- Nakicenovic, N., Davidson, O., Davis, G., Grübler, A., Kram, T., Rovere, E. L. La, Metz, B., Morita, T., Pepper, W., Pitcher, H., Sankovski, A., Shukla, P., Swart, R., Watson, R., & Dad, Z. (2014). Emissions Scenarios — IPCC. Summary for Policymakers. https://www.ipcc.ch/report/emissions-scenarios/
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- Ramsay, D. L., Gibberd, B., Dahm, J., & Bell, R. G. (2012). Defining coastal hazard zones for setback lines. A guide to good practice. National Institute of Water & Atmospheric Research Ltd.
- Rehman, S., Sahana, M., Kumar, P., Ahmed, R., & Sajjad, H. (2021). Assessing hazards induced vulnerability in coastal districts of India using site-specific indicators: an integrated approach. GeoJournal, 86(5), 2245–2266. https://doi.org/10.1007/s10708-020-10187-3
- Saha, C. K. (2015). Dynamics of disaster-induced risk in southwestern coastal Bangladesh: an analysis on tropical Cyclone Aila 2009. Natural Hazards, 75(1), 727–754. https://doi.org/10.1007/s11069-014-1343-9
- Sahoo, B., & Bhaskaran, P. K. (2018). A comprehensive data set for tropical cyclone storm surge-induced inundation for the east coast of India. International Journal of Climatology, 38(1), 403–419. https://doi.org/10.1002/joc.5184
- Szczyrba, L. (2022). Imaging coastal waves with radar. Nature Reviews Earth and Environment, 3(7), 423–423. https://doi.org/10.1038/s43017-022-00310-y
- Uma, G., & Sannasiraj, S. A. (2022). Assessment of input and dissipation packages for significant wave height during Tropical cyclones of varying intensity in Bay of Bengal. IEEE Explore (Proceedings of the OCEANS 2022–Chennai), India, pp. 1-5. https://doi.org/10.1109/OCEANSChennai45887.2022.9775274
- Varghese, S. J., Surendran, S., Rajendran, K., & Kitoh, A. (2020). Future projections of Indian Summer Monsoon under multiple RCPs using a high resolution global climate model multiforcing ensemble simulations: Factors contributing to future ISMR changes due to global warming. Climate Dynamics, 54(3–4), 1315–1328. https://doi.org/10.1007/s00382-019-05059-7
- Wang, C., Xu, Q., Cheng, Y., Pan, Y., & Li, H. (2022). Ensemble forecast of tropical cyclone tracks based on deep neural networks. Frontiers of Earth Science, In press. https://doi.org/10.1007/s11707-021-0931-8
- Xu, H., Yao, S., Li, Q., & Ye, Z. (2020). An improved K-means clustering algorithm. IEEE Explore (Proceedings of the 2020 IEEE 5th International Symposium on Smart and Wireless Systems within the Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS), Germany, pp. 1-5. https://doi.org/10.1109/IDAACS-SWS50031.2020.9297060
- Ying, M., Zhang, W., Yu, H., Lu, X., Feng, J., Fan, Y. X., Zhu, Y., & Chen, D. (2014). An overview of the China meteorological administration tropical cyclone database. Journal of Atmospheric and Oceanic Technology, 31(2), 287–301. https://doi.org/10.1175/JTECH-D-12-00119.1
- Zhu, L., Jin, J., Cannon, A. J., & Hsieh, W. W. (2016). Bayesian neural networks based bootstrap aggregating for tropical cyclone tracks prediction in South China Sea. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9949 LNCS, 475–482. https://doi.org/10.1007/978-3-319-46675-0_52
Yıl 2022,
Cilt: 11 Sayı: 3, 320 - 330, 30.09.2022
Md. Abdul Al Mohit
,
Md. Towhiduzzaman
,
Atish Kumar Joardar
Mossa. Samima Nasrin
Mst. Rabiba Khatun
Kaynakça
- 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
- 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
- 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.
- 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
- 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
- Gayathri, R., Murty, P. L. N., Bhaskaran, P. K., & Srinivasa Kumar, T. (2016). A numerical study of hypothetical storm surge and coastal inundation for AILA cyclone in the Bay of Bengal. Environmental Fluid Mechanics, 16(2), 429–452. https://doi.org/10.1007/s10652-015-9434-z
- Giffard-Roisin, S., Yang, M., Charpiat, G., Kumler Bonfanti, C., Kégl, B., & Monteleoni, C. (2020). Tropical cyclone track forecasting using fused deep learning from aligned reanalysis data. Frontiers in Big Data, 3, 1. https://doi.org/10.3389/fdata.2020.00001
- IPCC (2007a). Climate Change 2007 Synthesis Report. Intergovernmental Panel on Climate Change [Core Writing Team IPCC. https://www.ipcc.ch/report/ar4/syr/
- IPCC. (2007b). Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, Pachauri, R. K, & Reisinger, A. (Eds.)]. IPCC, Geneva, Switzerland, 104 pp.
- IPCC. (2014). Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C. B., Barros, V. R., Dokken, D. J., Mach, K. J., Mastrandrea, M. D., Bilir, T. E., Chatterjee, M., Ebi, K., L., Estrada, Y. O., Genova, R. C., Girma, B., Kissel, E. S., Levy, A. N., MacCracken, S., P. Mastrandrea, R., & White, L. L. (Eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1132 pp.
- Kitoh, A., & Endo, H. (2021). Future changes in global monsoon precipitation and their uncertainty: Results from 20-km and 60-km MRI-AGCM ensemble simulations. In Chang, C. – P., Ha, K. -J., Kim, D., & Wang, B. (Eds.), The Multiscale Global Monsoon System (World Scientific Series on Asia-Pacific Weather and Climate: Volume 11) (pp. 343–354). https://doi.org/10.1142/9789811216602_0027
- Knutson, T. R., McBride, J. L., Chan, J., Emanuel, K., Holland, G., Landsea, C., Held, I., Kossin, J. P., Srivastava, A. K., & Sugi, M. (2010). Tropical cyclones and climate change. Nature Geoscience, 3(3), 157–163. https://doi.org/10.1038/ngeo779
- Kowaleski, A. M., & Evans, J. L. (2016). Regression mixture model clustering of multimodel ensemble forecasts of hurricane sandy: Partition characteristics. Monthly Weather Review, 144(10), 3825–3846. https://doi.org/10.1175/MWR-D-16-0099.1
- Li, B., Zhou, L., Wang, C., Gao, C., Qin, J., & Meng, Z. (2020). Modulation of Tropical Cyclone Genesis in the Bay of Bengal by the Central Indian Ocean Mode. Journal of Geophysical Research: Atmospheres, 125(12), e2020JD032641. https://doi.org/10.1029/2020JD032641
- Mishra, A. K., & Vanganuru, N. (2020). Monitoring a tropical super cyclone Amphan over Bay of Bengal and nearby region in May 2020. Remote Sensing Applications: Society and Environment, 20, 100408. https://doi.org/10.1016/j.rsase.2020.100408
- Mizanur Rahman, M., Chandra Paul, G., & Hoque, A. (2011). A cyclone induced storm surge forecasting model for the coast of Bangladesh with application to the cyclone `Sidr’. International Journal of Mathematical Modelling & Computation. 1(2), 77-86.
- Murakami, H., Wang, Y., Yoshimura, H., Mizuta, R., Sugi, M., Shindo, E., Adachi, Y., Yukimoto, S., Hosaka, M., Kusunoki, S., Ose, T., & Kitoh, A. (2012). Future changes in tropical cyclone activity projected by the new high-resolution MRI-AGCM. Journal of Climate, 25(9), 3237–3260. https://doi.org/10.1175/JCLI-D-11-00415.1
- Murty, P. L. N., Srinivas, K. S., Rao, E. P. R., Bhaskaran, P. K., Shenoi, S. S. C., & Padmanabham, J. (2020). Improved cyclonic wind fields over the Bay of Bengal and their application in storm surge and wave computations. Applied Ocean Research, 95, 102048. https://doi.org/10.1016/j.apor.2019.102048
- Nakicenovic, N., Davidson, O., Davis, G., Grübler, A., Kram, T., Rovere, E. L. La, Metz, B., Morita, T., Pepper, W., Pitcher, H., Sankovski, A., Shukla, P., Swart, R., Watson, R., & Dad, Z. (2014). Emissions Scenarios — IPCC. Summary for Policymakers. https://www.ipcc.ch/report/emissions-scenarios/
- Paul, G. C., & Ali, M. E. (2019). Numerical storm surge model with higher order finite difference method of lines for the coast of Bangladesh. Acta Oceanologica Sinica, 38(6), 100–116. https://doi.org/10.1007/s13131-019-1385-7
- Ramsay, D. L., Gibberd, B., Dahm, J., & Bell, R. G. (2012). Defining coastal hazard zones for setback lines. A guide to good practice. National Institute of Water & Atmospheric Research Ltd.
- Rehman, S., Sahana, M., Kumar, P., Ahmed, R., & Sajjad, H. (2021). Assessing hazards induced vulnerability in coastal districts of India using site-specific indicators: an integrated approach. GeoJournal, 86(5), 2245–2266. https://doi.org/10.1007/s10708-020-10187-3
- Saha, C. K. (2015). Dynamics of disaster-induced risk in southwestern coastal Bangladesh: an analysis on tropical Cyclone Aila 2009. Natural Hazards, 75(1), 727–754. https://doi.org/10.1007/s11069-014-1343-9
- Sahoo, B., & Bhaskaran, P. K. (2018). A comprehensive data set for tropical cyclone storm surge-induced inundation for the east coast of India. International Journal of Climatology, 38(1), 403–419. https://doi.org/10.1002/joc.5184
- Szczyrba, L. (2022). Imaging coastal waves with radar. Nature Reviews Earth and Environment, 3(7), 423–423. https://doi.org/10.1038/s43017-022-00310-y
- Uma, G., & Sannasiraj, S. A. (2022). Assessment of input and dissipation packages for significant wave height during Tropical cyclones of varying intensity in Bay of Bengal. IEEE Explore (Proceedings of the OCEANS 2022–Chennai), India, pp. 1-5. https://doi.org/10.1109/OCEANSChennai45887.2022.9775274
- Varghese, S. J., Surendran, S., Rajendran, K., & Kitoh, A. (2020). Future projections of Indian Summer Monsoon under multiple RCPs using a high resolution global climate model multiforcing ensemble simulations: Factors contributing to future ISMR changes due to global warming. Climate Dynamics, 54(3–4), 1315–1328. https://doi.org/10.1007/s00382-019-05059-7
- Wang, C., Xu, Q., Cheng, Y., Pan, Y., & Li, H. (2022). Ensemble forecast of tropical cyclone tracks based on deep neural networks. Frontiers of Earth Science, In press. https://doi.org/10.1007/s11707-021-0931-8
- Xu, H., Yao, S., Li, Q., & Ye, Z. (2020). An improved K-means clustering algorithm. IEEE Explore (Proceedings of the 2020 IEEE 5th International Symposium on Smart and Wireless Systems within the Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS), Germany, pp. 1-5. https://doi.org/10.1109/IDAACS-SWS50031.2020.9297060
- Ying, M., Zhang, W., Yu, H., Lu, X., Feng, J., Fan, Y. X., Zhu, Y., & Chen, D. (2014). An overview of the China meteorological administration tropical cyclone database. Journal of Atmospheric and Oceanic Technology, 31(2), 287–301. https://doi.org/10.1175/JTECH-D-12-00119.1
- Zhu, L., Jin, J., Cannon, A. J., & Hsieh, W. W. (2016). Bayesian neural networks based bootstrap aggregating for tropical cyclone tracks prediction in South China Sea. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9949 LNCS, 475–482. https://doi.org/10.1007/978-3-319-46675-0_52