top of page


References and Links to Papers


Use of machine learning & deep learning in hazard prediction

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,


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.


Sustainable Investing and Adaptation

Lockwood, J., Lin, N., Oppenheimer, M. (working.), Addressing Socio-Economic Inequalities in Adaptation Measures: A
Cost-Benefit Approach in New York City


Decarbonization and Energy

Kousky, C. & Lockwood, J., (in review.) ”Leveraging Insurance for Decarbonization,” Journal of Catastrophe Risk and Resilience


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.


Quantifying the physical processes that drive the response of polar regions to climate change.

Lockwood, J. W., Dufour, C. O., Griffies, S. M., & Winton, M. (2021). On the role of the Antarctic Slope Front on the occurrence of the Weddell Sea polynya under climate change, Journal of Climate, 1-56. 

bottom of page