Data Scientist
Data Scientist

Biography

Julian Rice is a data scientist in the Coastal Sciences Division at the PNNL-Seattle campus, where he studies the intersection of extreme weather events, remote sensing data, and AI/machine learning techniques. As the lead developer of the Risk Analysis Framework for Tropical Cyclones https://raft-model.pnnl.gov/, he focuses much of his work on understanding the behavior, hazards, and human-systems impacts of tropical cyclones.

‪Julian R. Rice‬ - ‪Google Scholar‬
JRice15 (Julian Rice) · GitHub

Research Interests

  • Physics-informed machine learning
  • Tropical cyclones and their impacts
  • Weather extremes and climate variability

Publications

2026

  • Balaguru, K., C.-C. Chang, L. R. Leung, P. A. Ullrich, Y. Han, J. R. Rice, S. Hagos, D. Chavas, S. Taraphdar, B. Harrop, N. Sun, and D. R. Judi. 2026. “Recent Tropical Cyclone Outer Size Increases in the Western North Atlantic.” Earth’s Future 14 (2): e2025EF007162. https://doi.org/10.1029/2025EF007162
  • John, E., K. Balaguru, S. Feng, K. Emanuel, C.-Y. Lee, J. R. Rice, N. Lalo, L. R. Leung, D. Judi, and L. K. Berg. 2026. “Characterizing uncertainty in synthetic tropical cyclone hazard models for U.S. energy infrastructure resilience.” Environmental Research: Climate. https://doi.org/10.1088/2752-5295/ae7202

2025

  • Rice, J. R., K. Balaguru, A. Staid, W. Xu, and D. Judi. 2025. “Projected increases in tropical cyclone-induced U.S. electric power outage risk.” Environmental Research Letters 20 (3): 034030. https://doi.org/10.1088/1748-9326/adad85
  • Rice, J. R., K. Balaguru, F. Ticona Rollano, J. Wilson, B. Daniel, D. Judi, N. Sun, and L. R. Leung. 2025. “Projecting U.S. coastal storm surge risks and impacts with deep learning.” Environmental Research Letters 20 (10): 104013. https://doi.org/10.1088/1748-9326/adfd74
  • Lalo, N., W. Xu, L. Yao, N. Sun, K. Balaguru, J. Rice, S. Lipari, T. Thurber, Z. Yang, M. Deb, and D. Judi. 2025. “Future North Atlantic tropical cyclone intensities in modified historical environments.” Scientific Data, 12 (1): 1924. https://doi.org/10.1038/s41597-025-06186-z
  • Rice, J. R., G. A. Fricker, and J. Ventura. (2025). “An end-to-end deep learning solution for automated LiDAR tree detection in the urban environment.” ISPRS Open Journal of Photogrammetry and Remote Sensing 17: 100092. https://doi.org/10.1016/j.ophoto.2025.100092

2024

  • Lipari, S., K. Balaguru, J. Rice, S. Feng, W. Xu, L. K. Berg, and D. Judi. 2024. “Amplified threat of tropical cyclones to US offshore wind energy in a changing climate.” Communications Earth & Environment 5 (1): 1–10. https://doi.org/10.1038/s43247-024-01887-6
  • Ventura, J., C. Pawlak, M. Honsberger, C. Gonsalves, J. Rice, N. L. Love, S. Han, V. Nguyen, K. Sugano, J. Doremus, G. A. Fricker, J. Yost, and M. Ritter. 2024. “Individual tree detection in large-scale urban environments using high-resolution multispectral imagery.” International Journal of Applied Earth Observation and Geoinformation 130: 103848. https://doi.org/10.1016/j.jag.2024.103848
  • Xu, W., K. Balaguru, D. R. Judi, J. Rice, L. R. Leung, and S. Lipari. 2024. “A North Atlantic synthetic tropical cyclone track, intensity, and rainfall dataset.” Scientific Data 11 (1): 130. https://doi.org/10.1038/s41597-024-02952-7

2022

  • Rice, J. 2022. “Deep Learning for Detecting Trees in the Urban Environment from Lidar.” Master’s thesis, California Polytechnic State University, San Luis Obispo. https://doi.org/10.15368/theses.2022.92

2020

  • Rice, J., W. Xu, and A. August. 2020. “Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forecasting.” arXiv Preprint arXiv:2010.00399. https://doi.org/10.48550/arXiv.2010.00399