Publications
References and Links to Papers
Ongoing
Use of machine learning & deep learning in hazard modeling
Lockwood, J. W., T. Loridan, N. Lin, M. Oppenheimer, and N. Hannah, 2024: A Machine Learning Approach to Model Over-Ocean Tropical Cyclone Precipitation. J. Hydrometeor., 25, 207–221, https://doi.org/10.1175/JHM-D-23-0065.1.
Lockwood, J. W., Lin, N., Oppenheimer, M., & Lai, C.-Y. (2022). Using Neural Networks to Predict Hurricane Storm Surge and to Assess the Sensitivity of Surge to Storm Characteristics. Journal of Geophysical Research: Atmospheres, 127, e2022JD037617. https://doi.org/10.1029/2022JD037617
Ongoing
Machine learning and Generative AI
Lockwood, J. W. (working). ”A comparison of Neural Operator, Diffusion, and Generative Adversarial Network Models for Precipitation Super-resolution over the Continental US”
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Lockwood, J. W. (2024). A Physics-Informed Conditional Super-resolution Tropical Cyclone Wind Field Model
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Lockwood, J. W., Lin, N., Gori, A., & Oppenheimer, M. (2024). Increasing flood hazard posed by tropical cyclone rapid intensification in a changing climate. Geophysical Research Letters, 51, e2023GL105624. https://doi.org/10.1029/2023GL105624
2024
Climate Adaptation and Economics
Lockwood, J., Lin, N., Oppenheimer, M. (2024.), Addressing Socio-Economic Inequalities in Adaptation Measures: A
Cost-Benefit Approach in New York City, https://iopscience.iop.org/article/10.1088/1748-9326/ad4ef8/meta
Ongoing
Energy modeling
Lockwood et al., (working) ”Bayesian Network Modelling of Energy Demand and Extreme Weather”
2024
Decarbonization and Energy
Kousky, C. & Lockwood, J., (2024) ”Leveraging Insurance for Decarbonization,” Journal of Catastrophe Risk and Resilience, https://journalofcrr.com/leveraging-insurance-for-decarbonization/
2020-2022
Impact of correlations on future hazard
Lockwood, J. W., Oppenheimer, M., Lin, N., Kopp E. R., Vecchi, G. & Gori, A. (2022). Correlation between future sea-level rise and tropical cyclone activity in CMIP6 models, Earth's Future. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021EF002462