Jeong-Hwan Kim

Jeong-Hwan Kim

Postdoctoral researcher

Publications

18 publications · 2418 citations · Google Scholar

*: Corresponding author, †: These authors contributed equally

2026 (5)

  1. Explainable Deep Learning Reveals Future Changes in East Asian Extreme Winter Predictability
    S. Y. Jo, J. H. Kim*, D. Kang, J. H. Park, S. K. Min, M. Kim, M. K. Sung*. npj Climate and Atmospheric Science
  2. Deep learning-based chlorophyll prediction: comparison with a dynamic model and applications to fish catch forecasting
    J. S. Park, J. Y. Park, Y. G. Ham, J. H. Kim, W. Jeon. Earth System Dynamics
  3. Data-driven global ocean model resolving atmospherically forced ocean dynamics
    J. H. Kim*, D. Kang*, Y. M. Yang, J. H. Park, Y. G. Ham. Science Advances
  4. AI for atmosphere-ocean sciences: advancements, challenges, and ways forward
    J. J. Luo*, J. Xia*, B. Pan*, … J. H. Kim, …, M. Zhao. National Science Review
  5. A super-resolution framework for downscaling machine learning weather prediction toward 1-km air temperature
    H. Park, S. Park*, D. Kang*, J. H. Kim. npj Climate and Atmospheric Science

2025 (2)

  1. Understanding machine learning weather prediction by designing a cost-efficient model with knowledge-oriented modules
    M. Cheon, J. H. Kim, Y. Choi, S. Y. Kang, J. G. Lee, Y. G. Ham, J. Y. Kim, D. Kang*. Scientific Reports
  2. Data-driven global ocean model resolving ocean-atmosphere coupling dynamics
    J. H. Kim*, D. Kang*, Y. M. Yang, J. H. Park, Y. G. Ham. arXiv

2024 (3)

  1. Partial-convolution-implemented generative adversarial network for global oceanic data assimilation
    Y. G. Ham*, Y. S. Joo, J. H. Kim, J. G. Lee. Nature Machine Intelligence
  2. Machine Learning–Based Lightning Parameterizations for the CONUS
    W. Y. Cheng, D. Kim*, S. Henderson, Y. G. Ham, J. H. Kim, R. Holzworth. Artificial Intelligence for the Earth Systems
  3. Improvement in Forecasting Short-Term Tropical Cyclone Intensity Change and Their Rapid Intensification Using Deep Learning
    J. H. Kim, Y. G. Ham*, D. Kim, T. Li, C. Ma. Artificial Intelligence for the Earth Systems

2023 (2)

  1. Exploring dominant processes for multi-month predictability of western Pacific precipitation using deep learning
    Y. M. Yang, J. H. Kim, J. H. Park, Y. G. Ham, S. I. An*, J. Y. Lee*, B. Wang. npj Climate and Atmospheric Science
  2. Anthropogenic fingerprints in daily precipitation revealed by deep learning
    Y. G. Ham*†, J. H. Kim†, S. K. Min*, D. Kim, T. Li, A. Timmermann, M. Stuecker. Nature

2022 (3)

  1. Southern Indian Ocean Dipole as a trigger for Central Pacific El Niño since the 2000s
    H. S. Jo, Y. G. Ham*, J. S. Kug, T. Li, J. H. Kim, J. G. Kim, H. Kim. Nature Communications
  2. Application of Deep Learning to Understanding ENSO Dynamics
    N. Y. Shin, Y. G. Ham, J. H. Kim, M. Cho, J. S. Kug*. Artificial Intelligence for the Earth Systems
  3. Deep Learning for Predicting Winter Temperature in North China
    L. Gao, Y. M. Yang*, Q. Li, Y. G. Ham, J. H. Kim. Atmosphere

2021 (1)

  1. Unified deep learning model for El Niño/Southern Oscillation forecasts by incorporating seasonality in climate data
    Y. G. Ham*, J. H. Kim, E. S. Kim, K. W. On. Science Bulletin

2019 (2)

  1. Deep learning for multi-year ENSO forecasts
    Y. G. Ham*, J. H. Kim, J. J. Luo. Nature
  2. Pantropical climate interactions
    W. Cai, L. Wu*, M. Lengaigne, T. Li, S. McGregor, J. S. Kug, … & P. Chang. Science