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Jiali Wang

Atmospheric & Earth Science 2


Jiali Wang is an assistant atmospheric scientist in Environmental Science Division at Argonne. Dr. Wang also holds a fellowship member with NAISE at Northwestern University, as well as CASE at University of Chicago. Dr. Wang received her Ph.D degree in atmospheric science in 2012, and has been working at Argonne since then. Dr. Wang specializes in physical understanding climate and extreme climate variabilities and their impacts on water/energy, and many other fields (e.g., infrastructure, ecology) through high-resolution numerical modeling, data analysis, as well as machine learning/deep learning. Extreme events she is interested in and has been working on include: flooding, winter storms, droughts/wildfires, wind gusts, and heatwaves, focusing on various climate zones of North America. Dr. Wang served as a primary investigator and co-investigator for projects supported by AT&T(climate, risk, and resilience), DOD (regional climate modeling and climate extremes), DOE (wind assessment), DHS (regional resiliency assessment program) as well as Argonne Laboratory Directed Research and Development (neighborhood scale hydrological modeling). Dr. Wang also had considerable experience with data analysis and modeling of urban climate.

Research Interests:

  • Climate variability assessment using statistics
  • Climate extreme analysis using extreme value theory
  • Uncertainty quantification for climate modeling
  • Extreme climate (e.g., flooding, winter storms, heatwaves, wildfires) impacts on other sectors (e.g. infrastructure, agriculture, ecology, and health)
  • Hydrological modeling; urban climate modeling
  • Machine learning and deep learning for climate science, e.g. development of climate model surrogates; downscaling climate model data.



2019: Pacesetter for excellence in achievement and performance which truly surpasses normal job expectations.





Brown, E., J. Wang, and Y. Feng: U.S. Wildfire Potential: a Historical View and Future Projection using High-resolution Climate Data, Environ. Res. Lett. https://​doi​.org/​10​.​1088​/​1748​-​9326​/​a​ba868., 2020.

Wang, J., Balaprakash, P., and Kotamarthi, R.: Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model, Geosci. Model Dev., 12, 42614274, 2019.

Wang, J., Wang, C., Rao, V., Orr, A., Yan, E., and Kotamarthi, R.: A parallel workflow implementation for PEST version 13.6 in high-performance computing for WRF-Hydro version 5.0: a case study over the Midwestern United States, Geosci. Model Dev., 12, 3523-3539, 2019. https://​doi​.org/​10​.​5194​/​g​m​d​-​12​-​3523​-2019.

Zobel, Z., J. Wang, D. J. Wuebbles, and V. R. Kotamarthi. 2018. Analyses for High‐Resolution Projections through the End of the 21st Century for Precipitation Extremes over the United States. Earth’s Future. https://​doi​.org/​10​.​1029​/​2018​E​F​000956

Chang, W., J. Wang, J. Marohnic, V. R. Kotamarthi, and E.J. Moyer, 2018: Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking. Climate Dynamics.  https://​doi​.org/​10​.​1007​/​s​00382​-​018​-​4294-0.

Zobel, Z., J. Wang, D. J. Wuebbles, and V. R. Kotamarthi. 2017:  High Resolution Dynamical Downscaling Ensemble Projections of Future Extreme Temperature Distributions for the United States. Earth’s Future. 5. https://​doi​.org/​10​.​1002​/​2017​E​F​000642.

Wang, J., J. Bessac, V.R. Kotamarthi, E. Constantinescu. 2017: Internal variability of a dynamically downscaled climate over North America. Climate Dynamics. DOI: 10.1007/s00382-017-3889-1

Zobel, Z., J. Wang, D. J. Wuebbles, and V. R. Kotamarthi. 2017. Evaluations of high-resolution dynamically downscaled ensembles over the contiguous United States. Climate Dynamics. doi:10.1007/s00382-017-3645-6

Jin, Z, Q. Zhuang, J. Wang, S. V. Archontoulis, Z. Zobel, and V. R. Kotamarthi, 2017: The combined and separate impacts of climate extremes on the current and future US rainfed maize and soybean production under elevated CO2. Global Change Biology. DOI: 10.1111/gcb.13617

Cai, H., J. Wang, Y. Feng, Q. Wang, Z. Qin, and J. Dunn, 2016: Consideration of Land Use Change-Induced Surface Albedo Effects in Life-Cycle Analysis of Biofuels. Energy and Environmental Science, DOI: 10.1039/C6EE01728B

Chang, W., M. Stein, J. Wang, V. R. Kotamarthi, and E. Moyer, 2016: Changes in Spatio-temporal Precipitation Patterns in Changing Climate Conditions. Journal of Climate.29, 8355-8376. DOI10.1175/JCLI-D-15-0844.1.

Wang, J., Y. Han, M. Stein, V. R. Kotamarthi, and W. K. Huang, 2016: Evaluation of dynamical downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model. Climate Dynamics. DOI: 10.1007/s00382-016-3000-3

Wang, J., F. N. U. Swati, M. L. Stein, and V. R. Kotamarthi, 2015: Model performance in spatiotemporal patterns of precipitation: New methods for identifying value added by a regional climate model, Journal of Geophysical Research, Atmosphere, 120, 12391259, doi:10.1002/2014JD022434 

Campos, E., and J. Wang, 2015: Numerical Simulation and Analysis of the April 2013 Chicago Floods, Journal of Hydrology, 531, 454-474

Wang, J., and V. R. Kotamarthi, 2014: Downscaling with a nested regional climate model in near-surface fields over the contiguous United States, Journal of Geophysical Research, Atmosphere, 119, 87788797, doi:10.1002/2014JD021696

Wang, J., and V. R. Kotamarthi, 2013: Assessment of Dynamical Downscaling in Near-Surface Fields with Different Spectral Nudging Approaches Using the Nested Regional Climate Model (NRCM), Journal of Applied Meteorology and Climatology, 52, 15761591