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Md. Babul Miah

Earth Science, Environment and Energy · Jeonbuk National University, South Korea

https://researchid.co/babulbsmrstu16
@jbnu.ac.kr
13Google Scholar Citations
1Google Scholar h-index
1Google Scholar i10-index

Research Interests

Atmospheric Modeling, Climate Modeling, Tropical Cyclones, Climate Extremes

Biography

Md. Babul Miah is a Doctoral Research Fellow/Ph.D. (M.S.-Ph.D. Integrated) at the Department of Environment and Energy/Climate Modeling Lab at Jeonbuk National University(JBNU), South Korea, advised by Prof. Jong-Yeon Park. His research interests include climate change, the reversibility of climate extremes (i.e. temperature and precipitation extremes) and biogeochemical prediction using the ESM4 and MOM6 Models. His work is primarily concerned with the application of machine learning and deep learning to climate change, climate extremes and biogeochemical prediction. Other research areas include the understanding of the response of ocean-atmosphere teleconnections to climate extremes and drought.

Education

Ph.D Fellow in Earth Science, Environment and Energy, Jeobnuk National University, Jeonju, South Korea.( Sep. 2023 -present) B.Sc in Environmental Science and Disaster Management, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Bangladesh ( Jan. 2017 – Mar. 2022)

Recent Google Scholar Publications

  1. Irreversibility of extreme precipitation intensity in global monsoon areas under multiple carbon neutrality scenarios
    Weather and Climate Extremes, 100843 , 2025, 2025 | Citations: 1.0
  2. Statistical Data Analysis and Visualization with SPSS
    https://doi.org/10.5281/zenodo.14195590 1, ISBN: 978-984-37-0124-4 , 2024, 2024
  3. Irreversibility in Extreme Precipitation of Global Monsoon Area under Multiple Carbon Neutrality Scenarios
    한국기상학회 학술대회 논문집, 231-231 , 2024, 2024
  4. Scientific Data Analysis and Visualization with Python
    https://doi.org/10.5281/zenodo.7592894 1, ISBN: 978-984-35-3614-3 , 2023, 2023
  5. Extreme rainfall indices prediction with atmospheric parameters and ocean-atmospheric teleconnections using Random Forest model
    Journal of Applied Meteorology and Climatology 61 (6), 651–667 , 2022, 2022 | Citations: 12.0

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