Research Interests

• Machine learning and interpretable data-driven parameterizations
• Climate model development
• Cloud microphysics
• High fidelity atmospheric simulations

Ph.D. Mechanical Engineering, California Institute of Technology, 2024

M.S. Mechanical Engineering, California Institute of Technology, 2021

B.S.E. Chemical and Biological Engineering, Princeton University, 2019

Google Scholar | ORCID

 

Selected Publications

de Jong, E., Schneider, T., Wennberg, P.O, and Jalkanen, J. P. (preprint) What can lightning and shipping regulations tell us about aerosols in deeply convecting clouds? ESS Open Archive, doi: 10.22541/essoar.171632547.79526166/v1.

de Jong, E., Quon, E., and Yellapantula, S. (2024) Mechanisms of Low-Level Jet Formation in the US Mid-Atlantic Offshore, Journal of Atmospheric Sciences 81 (1), 31-52, doi: 10.1175/JAS-D-23-0079.1

Azimi, S., Jaruga, A., de Jong, E., Arabas, S. and Schneider, T. (2023) Training warm-rain bulk microphysics schemes using super-droplet simulations, Journal of Advances in Modeling Earth Systems 16 (7), doi: 10.1029/2023MS004028.

de Jong, E., Mackay, J. B., Bulenok, O., Jaurga, A., and Arabas, S. (2023) Breakups are Complicated: An Efficient Representation of Collisional Breakup in the Superdroplet Method, Geoscientific Model Development 4193-4211, doi: 10.5194/gmd-16-4193-2023.

de Jong, E., Bischoff, T., Nadim, A., and Schneider, T. (2022) Spanning the Gap from Bulk to Bin: A Novel Spectral Microphysics Method, Journal of Advances in Modeling Earth Systems 14 (11), doi: 10.1029/2022MS003186.

Bieli, M., Dunbar, O.R.A., de Jong, E. K., Jaruga, A., Schneider, T., and Bischoff, T. (2022) An Efficient Bayesian Approach to Learning Droplet Collision Kernels: Proof of Concept Using “Cloudy”, a new n-Moment Bulk Microphysics Scheme, Journal of Advances in Modeling Earth Systems 14 (8), doi: 10.1029/2022MS002994.