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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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Posts
[논문 리뷰] Skillful joint probabilistic weather forecasting from marginals (Alet et al., 2025)
Published:
요약
AI 기상 예측 모델의 multi-step rollout 학습 전략 — BPTT, Replay Buffer, Online Replay Buffer
Published:
1. 왜 multi-step 학습이 필요한가
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
Pantropical climate interactions
W. Cai, L. Wu*, M. Lengaigne, T. Li, S. McGregor, J. S. Kug, … & P. Chang. Science
Deep learning for multi-year ENSO forecasts
Y. G. Ham*, J. H. Kim, J. J. Luo. Nature
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
Deep Learning for Predicting Winter Temperature in North China
L. Gao, Y. M. Yang*, Q. Li, Y. G. Ham, J. H. Kim. Atmosphere
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
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
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
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
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
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
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
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
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
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
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
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
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
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
talks
Deep learning for tropical cyclone forecasts
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Deep learning for short-term forecasts of tropical cyclone intensity and their rapid intensification
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Convolutional networks for climate change detection and tropical cyclone intensity prediction
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Convolutional networks for climate change detection and tropical cyclone intensity prediction
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최근 잦아지는 기상이변, 기후변화 때문일까?
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Weather and climate research using AI
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Deep learning for global three-dimensional ocean modeling with physical response consistency
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Tutorial for deep learning beginners
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.