Journal Publications
GHG work (selected)
Jeong, S., Hamilton, S. D., Johnson, M. S., Wu, D., Turner, A. J., & Fischer, M. L. (2025). Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO2 Emissions. Environmental Science & Technology, 59(9), 4376–4387. https://doi.org/10.1021/acs.est.4c09395.
Johnson, M. S., Hamilton, S. D., Jeong, S., Cui, Y. Y., Wu, D., Turner, A., & Fischer, M. (2025). State-wide California 2020 carbon dioxide budget estimated with OCO-2 and OCO-3 satellite data. Atmospheric Chemistry and Physics, 25(15), 8475–8492. https://doi.org/10.5194/acp-25-8475-2025.
Hamilton, S. D., Wu, D., Johnson, M. S.,Turner, A. J., Fischer, M. L., Dadheech, N.,& Jeong, S. (2024). Estimating carbon dioxide emissions in two California cities using Bayesian inversion and satellite measurements. Geophysical Research Letters, 51, e2024GL111150. https://doi.org/10.1029/2024GL111150.
Maasakkers, J. D.; McDuffie, E. E.; Sulprizio, M. P.; Chen, C.; Schultz, M.; Brunelle, L.; Thrush, R.; Steller, J.; Sherry, C.; Jacob, D. J.; Jeong, S.; Irving, B.; Weitz, M. A Gridded Inventory of Annual 2012–2018 U.S. Anthropogenic Methane Emissions. Environ. Sci. Technol. 2023, 57 (43), 16276–16288. https://doi.org/10.1021/acs.est.3c05138.
Jeong, S., M. L. Fischer, H. Breunig, A. R. Marklein, F. M. Hopkins, S. C. Biraud. Artificial Intelligence Approach for Estimating Dairy Methane Emissions (2022), Environmental Science & Technology, https://pubs.acs.org/doi/full/10.1021/acs.est.1c08802.
Heerah, S., Frausto-Vicencio, I., Jeong, S., Marklein, A. R., Ding, Y., Meyer, A. G., et al. (2021). Dairy methane emissions in California's San Joaquin Valley inferred with ground-based remote sensing observations in the summer and winter. Journal of Geophysical Research: Atmospheres, 126, e2021JD034785. https://doi.org/10.1029/2021JD034785.
Marklein, A. R., Meyer, D., Fischer, M. L., Jeong, S., Rafiq, T., Carr, M., & Hopkins, F. M. (2021). Facility-scale inventory of dairy methane emissions in California: implications for mitigation. Earth System Science Data, 13(3), 1151–1166. https://doi.org/10.5194/essd-13-1151-2021.
Guha, A., Newman, S., Fairley, D., Dinh, T. M., Duca, L., Conley, S. C., et al. (2020). Assessment of Regional Methane Emission Inventories through Airborne Quantification in the San Francisco Bay Area. Environmental Science & Technology, 54(15), 9254–9264. https://doi.org/10.1021/acs.est.0c01212.
Brophy, K., Graven, H., Manning, A. J., White, E., Arnold, T., Fischer, M. L., Jeong, S. et al. (2019). Characterizing uncertainties in atmospheric inversions of fossil fuel CO2 emissions in California. Atmospheric Chemistry and Physics, 19(5), 2991–3006. https://doi.org/10.5194/acp-19-2991-2019.
Cui, Y. Y., Vijayan, A., Falk, M., Hsu, Y.-K., Yin, D., Chen, X. M., … Croes, B. (2019). A Multiplatform Inversion Estimation of Statewide and Regional Methane Emissions in California during 2014–2016. Environmental Science & Technology, 53(16), 9636–9645. https://doi.org/10.1021/acs.est.9b01769.
Cui, X., Newman, S., Xu, X., Andrews, A. E., Miller, J., Lehman, S., Jeong, S., … Fischer, M. L. (2019). Atmospheric observation-based estimation of fossil fuel CO2 emissions from regions of central and southern California. Science of The Total Environment, 664, 381–391. https://doi.org/10.1016/j.scitotenv.2019.01.081.
Jeong, S., Newman, S., Zhang, J., Andrews, A. E., Bianco, L., Dlugokencky, E., et al. (2018). Inverse Estimation of an Annual Cycle of California’s Nitrous Oxide Emissions. Journal of Geophysical Research: Atmospheres, 123(9), 4758–4771. https://doi.org/10.1029/2017jd028166.
Fischer, M. L., Chan, W. R., Delp, W., Jeong, S., Rapp, V., & Zhu, Z. (2018). An Estimate of Natural Gas Methane Emissions from California Homes. Environmental Science & Technology, 52(17), 10205–10213. https://doi.org/10.1021/acs.est.8b03217.
Graven, H., Fischer, M. L., Lueker, T., Jeong, S., Guilderson, T. P., Keeling, R. F., … Walker, S. J. (2018). Assessing fossil fuel CO2 emissions in California using atmospheric observations and models. Environmental Research Letters, 13(6), 065007. https://doi.org/10.1088/1748-9326/aabd43.
Jeong, S., Cui, X., Blake, D. R., Miller, B., Montzka, S. A., Andrews, A., … Fischer, M. L. (2017). Estimating methane emissions from biological and fossil-fuel sources in the San Francisco Bay Area. Geophysical Research Letters, 44(1), 486–495. https://doi.org/10.1002/2016gl071794.
Bagley, J. E., Jeong, S., Cui, X., Newman, S., Zhang, J., Priest, C., et al. (2017). Assessment of an atmospheric transport model for annual inverse estimates of California greenhouse gas emissions. Journal of Geophysical Research: Atmospheres, 122(3), 1901–1918. https://doi.org/10.1002/2016jd025361.
Fischer, M. L., Parazoo, N., Brophy, K., Cui, X., Jeong, S., Liu, J., … Graven, H. (2017). Simulating estimation of California fossil fuel and biosphere carbon dioxide exchanges combining in situ tower and satellite column observations. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1002/2016jd025617.
Tadić, J. M., Michalak, A. M., Iraci, L., Ilić, V., Biraud, S. C., Feldman, D. R., … Ryoo, J.-M. (2017). Elliptic Cylinder Airborne Sampling and Geostatistical Mass Balance Approach for Quantifying Local Greenhouse Gas Emissions. Environmental Science & Technology, 51(17), 10012–10021. https://doi.org/10.1021/acs.est.7b03100.
Cui, Y. Y., Brioude, J., Angevine, W. M., Peischl, J., McKeen, S. A., Kim, S.-W., … Trainer, M. (2017). Top-down estimate of methane emissions in California using a mesoscale inverse modeling technique: The San Joaquin Valley. Journal of Geophysical Research: Atmospheres, 122(6), 3686–3699. https://doi.org/10.1002/2016jd026398.
Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Turner, A. J., Weitz, M., Wirth, T., … Fischer, M. L. (2016). Gridded National Inventory of U.S. Methane Emissions. Environmental Science & Technology, 50(23), 13123–13133. https://doi.org/10.1021/acs.est.6b02878.
Johnson, M. S., Xi, X., Jeong, S., Yates, E. L., Iraci, L. T., Tanaka, T., et al. (2016). Investigating seasonal methane emissions in Northern California using airborne measurements and inverse modeling. Journal of Geophysical Research: Atmospheres, 121(22), 13,753-13,767. https://doi.org/10.1002/2016jd025157.
Jeong, S., Newman, S., Zhang, J., Andrews, A. E., Bianco, L., Bagley, J., et al. (2016). Estimating methane emissions in California’s urban and rural regions using multitower observations. Journal of Geophysical Research: Atmospheres, 121(21), 13,031-13,049. https://doi.org/10.1002/2016jd025404.
Feng, S., Lauvaux, T., Newman, S., Rao, P., Ahmadov, R., Deng, A., et al. (2016). Los Angeles megacity: a high-resolution land–atmosphere modelling system for urban CO2 emissions. Atmospheric Chemistry and Physics, 16(14), 9019–9045. https://doi.org/10.5194/acp-16-9019-2016.
Xi, X., Johnson, M. S., Jeong, S., Fladeland, M., Pieri, D., Diaz, J. A., & Bland, G. L. (2016). Constraining the sulfur dioxide degassing flux from Turrialba volcano, Costa Rica using unmanned aerial system measurements. Journal of Volcanology and Geothermal Research, 325, 110–118. https://doi.org/10.1016/j.jvolgeores.2016.06.023.
Jeong, S., Millstein, D., & Fischer, M. L. (2014). Spatially Explicit Methane Emissions from Petroleum Production and the Natural Gas System in California. Environmental Science & Technology, 48(10), 5982–5990. https://doi.org/10.1021/es4046692.
Johnson, M. S., Yates, E. L., Iraci, L. T., Loewenstein, M., Tadić, J. M., Wecht, K. J., et al. (2014). Analyzing source apportioned methane in northern California during Discover-AQ-CA using airborne measurements and model simulations. Atmospheric Environment, 99, 248–256. https://doi.org/10.1016/j.atmosenv.2014.09.068.
Jeong, S., Hsu, Y.-K., Andrews, A. E., Bianco, L., Vaca, P., Wilczak, J. M., & Fischer, M. L. (2013). A multitower measurement network estimate of California’s methane emissions. Journal of Geophysical Research: Atmospheres, 118(19), 11,339-11,351. https://doi.org/10.1002/jgrd.50854.
Newman, S., Jeong, S., Fischer, M. L., Xu, X., Haman, C. L., Lefer, B., … Yung, Y. L. (2013). Diurnal tracking of anthropogenic CO2 emissions in the Los Angeles basin megacity during spring 2010. Atmospheric Chemistry and Physics, 13(8), 4359–4372. https://doi.org/10.5194/acp-13-4359-2013.
Wennberg, P. O., Mui, W., Wunch, D., Kort, E. A., Blake, D. R., Atlas, E. L., et al. (2012). On the Sources of Methane to the Los Angeles Atmosphere. Environmental Science & Technology, 46(17), 9282–9289. https://doi.org/10.1021/es301138y.
Jeong, S., Zhao, C., Andrews, A. E., Dlugokencky, E. J., Sweeney, C., Bianco, L., et al. (2012). Seasonal variations in N2O emissions from central California. Geophysical Research Letters, 39(16), n/a-n/a. https://doi.org/10.1029/2012gl052307.
Jeong, S., Zhao, C., Andrews, A. E., Bianco, L., Wilczak, J. M., & Fischer, M. L. (2012). Seasonal variation of CH4 emissions from central California. Journal of Geophysical Research: Atmospheres (1984–2012), 117(D11), n/a-n/a. https://doi.org/10.1029/2011jd016896.
Zhao, C., Andrews, A. E., Bianco, L., Eluszkiewicz, J., Hirsch, A., MacDonald, C., et al. (2009). Atmospheric inverse estimates of methane emissions from Central California. Journal of Geophysical Research, 114(D16), 14531–13. https://doi.org/10.1029/2008jd011671.
Other work (selected)
Kemp, J. M., Millstein, D., Gorman, W., Jeong, S., & Wiser, R. (2025). Electric transmission value and its drivers in United States power markets. Nature Communications, 16(1), 8055. https://doi.org/10.1038/s41467-025-63143-5.
Simley, E., Millstein, D., Jeong, S., & Fleming, P. (2023). The value of wake steering wind farm flow control in US energy markets. Wind Energy Science, 9(1), 219–234. https://doi.org/10.5194/wes-9-219-2024.
Millstein, D., Jeong, S., Ancell, A., & Wiser, R. (2023). A database of hourly wind speed and modeled generation for US wind plants based on three meteorological models. Scientific Data, 10(1), 883. https://doi.org/10.1038/s41597-023-02804-w.
Wang, Y., Millstein, D., Mills, A. D., Jeong, S., & Ancell, A. (2022). The cost of day-ahead solar forecasting errors in the United States. Solar Energy, 231, 846–856. doi: 10.1016/j.solener.2021.12.012, https://www.sciencedirect.com/science/article/pii/S0038092X21010616?via%3Dihub.
Jeong, S, Millstein, D., Levinson, R. (2021). Modeling potential air temperature reductions yielded by cool roofs and urban irrigation in the Kansas City Metropolitan Area. Urban Climat, https://doi.org/10.1016/j.uclim.2021.100833.
Millstein, D., Wiser, R., Mills, A. D., Bolinger, M., Seel, J., & Jeong, S. (2021). Solar and wind grid system value in the United States: The effect of transmission congestion, generation profiles, and curtailment. Joule. https://doi.org/10.1016/j.joule.2021.05.009.
Mills, A., Wiser, R., Millstein, D., Carvallo, J. P., Gorman, W., Seel, J., & Jeong, S. (2021). The impact of wind, solar, and other factors on the decline in wholesale power prices in the United States. Applied Energy, 283, 116266. https://doi.org/10.1016/j.apenergy.2020.116266.
Hamilton, S. D., Millstein, D., Bolinger, M., Wiser, R., & Jeong, S. (2020). How Does Wind Project Performance Change with Age in the United States? Joule, 4(5), 1004–1020. https://doi.org/10.1016/j.joule.2020.04.005.
Wiser, R., Millstein, D., Bolinger, M., Jeong, S., & Mills, A. (2020). The hidden value of large-rotor, tall-tower wind turbines in the United States. Wind Engineering, 0309524X2093394. https://doi.org/10.1177/0309524x20933949.
Millstein, D., Dobson, P., & Jeong, S. (2020). The Potential to Improve the Value of U.S. Geothermal Electricity Generation Through Flexible Operations. Journal of Energy Resources Technology, 143(1). https://doi.org/10.1115/1.4048981.
Mills, A. D., Millstein, D., Jeong, S., Lavin, L., Wiser, R., & Bolinger, M. (2018). Estimating the value of offshore wind along the United States’ Eastern Coast. Environmental Research Letters, 13(9), 094013. https://doi.org/10.1088/1748-9326/aada62.
Image credit: https://unsplash.com/photos/tgrV1o779zQ.