Maeda, Y., Satoh, M. (2025). Deep Learning Improves Prediction of the Boreal Summer Intraseasonal Oscillation Using Predictive Source Analysis. Geophysical Research Letters. https://doi.org/10.1029/2024GL114477 (accepted May 13, 2025; published May 22, 2025).
Yuki Maeda and Masaki Satoh. Probabilistic Deep Neural Network Estimates the Intraseasonal Predictability of the Western North Pacific Subtropical High via Transfer Learning. The 7th International Workshop on Nonhydrostatic Models, Aiina, Morioka, Japan, November 17-19, 2025, P-26 (Poster)
Yuki Maeda and Masaki Satoh. Estimating Subseasonal Predictability with Deep Learning: From Sources to Forecasts of Opportunity. AORI-MPIM-Monash-NTU meeting, AORI, Nov 13, 2025 (Oral)
Yuki Maeda and Masaki Satoh. Deep Learning-Based Subseasonal Prediction of the Boreal Summer Intraseasonal Oscillation: Phase-Dependent Predictability Source Analysis. The 5th Joint Symposium on Ocean, Coastal, and Atmospheric Sciences, AORI, Oct 8, 2025 (Poster)
Yuki Maeda and Masaki Satoh. Deep Learning Approach to Subseasonal Prediction of the Western North Pacific Subtropical High: Transfer and Multitask Learning. Asia Oceania Geosciences Society 2025, Singapore. July 30, 2025 (Poster; AOGS2025 Best Student Poster Award)
Yuki Maeda and Masaki Satoh. Enhancing Subseasonal Western North Pacific Subtropical High Predictability: Deep Learning Framework Bridging Climate Simulations and Reanalysis. 2025 University Allied Workshop on Weather in the Changing Climate, National Taiwan University, Taipei, Taiwan, July 14, 2025 (Oral)
Yuki Maeda and Masaki Satoh. Deep learning approach to subseasonal prediction of the western North Pacific subtropical high: transfer and multitask learning. Japan Geoscience Union Meeting 2025, Makuhari, May 29, 2025, MGI27-03 (Oral)
Yuki Maeda and Masaki Satoh. Deep Learning-Based Subseasonal Prediction of the Boreal Summer Intraseasonal Oscillation: Phase-Dependent Predictability Source Analysis. The World Climate Research Program Global KM-scale Hackathon, AORI, May 12-16, 2025, (Poster)
2024
Yuki Maeda and Masaki Satoh. Deep Learning for Boreal Summer Intraseasonal Oscillation (BSISO) Prediction and Exploration of Predictability Sources. The 4th Asian Conference on Meteorology (ACM) 2024, Tsukuba, Nov 19, 2024, P098 (Poster)
Yuki Maeda and Masaki Satoh. Deep Learning for Boreal Summer Intraseasonal Oscillation (BSISO) Prediction and Explainability. Workshop on Global Storm-Resolving Analysis Bridging Atmospheric and Cloud Dynamics, Hakone, Jun 17-19, 2024, (Poster)
Yuki Maeda and Masaki Satoh. Deep Learning for Boreal Summer Intraseasonal Oscillation (BSISO) Prediction and Explainability. Japan Geoscience Union Meeting 2024, Makuhari, May 30, 2024, MGI26-10 (Oral)
2023
Yuki Maeda, Tomoki Kimura, Minami Tokushige, Eitaro Nakada, Shotaro Sakai, Naoki Terada. A 3D magnetohydrodynamic modeling for atmospheric escape from Earth during the geomagnetic reversal event. Japan Geoscience Union Meeting 2023, Makuhari, May 26, 2023, PCG19-P01 (Poster)