| 2022-10 | An Advanced Operational Approach for Tropical Cyclone Center Estimation Using Geostationary-Satellite-Based Water Vapor and Infrared Channels | Shin, Yeji; Lee, Juhyun; Im, Jungho; Sim, Seongmun | ARTICLE | 248 |
| 2021-12 | Comparative Assessment of Linear Regression and Machine Learning for Analyzing the Spatial Distribution of Ground-level NO2 Concentrations: A Case Study for Seoul, Korea | Kang, Eunjin; Yoo, Cheolhee; Shin, Yeji; Cho, Dongjin; Im, Jungho | ARTICLE | 943 |
| 2022-08 | Machine learning-based quantitative precipitation estimation over the East Asia from geostationary satellite data | Im, Jungho; Shin, Yeji | Master's thesis | 920 |
| 2020-10 | Multi-task learning based tropical cyclone intensity monitoring and forecasting through fusion of geostationary satellite data and numerical forecasting model output | Lee, Juhyun; Yoo, Cheolhee; Im, Jungho; Shin, Yeji; Cho, Dongjin | ARTICLE | 635 |
| 2023-12 | Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learning | Han, Daehyeon; Choo, Minki; Im, Jungho; Shin, Yeji; Lee, Juhyun; Jung, Sihun | ARTICLE | 1267 |
| 2021-12 | Rainfall intensity estimation using geostationary satellite data based on machine learning: A case study in the korean peninsula in summer | Shin, Yeji; Han, Daehyeon; Im, Jungho | ARTICLE | 817 |
