Jiali Wang

Biography

At Argonne National Laboratory for 13 years, Dr. Jiali Wang works at the intersection of extreme weather, water, energy, and infrastructure, using high-resolution (e.g., convection-permitting) modeling, data analysis, and machine learning. She also holds joint appointment with the Northwestern-Argonne Institute of Science and Engineering (NAISE) at Northwestern University and the University of Chicago’s Center for Advanced Study of the Environment (CASE). She has published 60+ peer-reviewed papers—15 as lead author and about 30 as a major contributing (including advising) author. Jiali served as principal or co-investigator on projects funded by the Department of Energy, the Department of Defense, the Department of Homeland Security, industry partners, and Argonne’s Laboratory Directed Research and Development.

Honors and Awards:

2019: Argonne Pacesetter Award
2019: HPC Innovation Excellence Award for Risk and Resiliency of Infrastructure Southeastern USA for AT&T
2019: R&D 100 Finalist for the Climate Risk and Resilience Analysis technology
2020: Argonne Director’s Award for developing a methodology and analysis of climate impacts to AT&T’s infrastructure
2021: Impact Argonne Award
2022: Impact Argonne Award
2023: Impact Argonne Award
2024: Delivering Impact Award for Argonne Commercialization Excellence
2024: The Secretary of Energy Achievement Award for Puerto Rico Grid Resilience and Transition to 100% Renewable Energy Study

Publications:

2026

Jung, C., Xue, P., Huang, C., Pringle, W., Biswas, M., Nain, G., and Wang, J.: Fully Coupled High-Resolution Atmosphere-Ocean-Wave Simulations of Hurricane Henri (2021): Implications for Offshore Load Assessments, Wind Energ. Sci., https://doi.org/10.5194/wes-2025-47 accepted, 2026.

Deskos, G., Wang, J., Arwade, S., Fisher, M., Hirth, B., Guo Larsén, X., Lundquist, J. K., Myers, A., Pang, W., Pringle, W. J., Rogers, R., Sanchez-Gomez, M., Sun, C., Yamaguchi, A., and Veers, P.: Grand Challenges in Designing Resilient Wind Energy Systems in Areas Prone to Tropical Cyclones, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2026-32, in review, 2026.

2025

Jackson, R., O’Brien, J., Wang, J., Fytanidis, D. K., Kotamarthi, R., Muradyan, R., Grover, M., Raut, B., Collis, S., Tuftedal, M., Anderson, G., Wagner, T. J., Nesbitt, S. W., Tan, H., Wefer, D., & Hammond, M. (2025). The thermodynamic and kinematic structure of the planetary boundary layer for a summer lake-breeze day in Chicago. Journal of Geophysical Research: Atmospheres. [In review]. DOI: 10.22541/essoar.176486914.42086968/v1

Fytanidis, D. K., Tan, H., Kotamarthi, R., Wang, J., Martilli, A., O’Brien, J., Muradyan, P., Collis, S., & Negri, M. C. (2025). How urban heterogeneity and turbulence shape street-level heat exposure. Communications Earth & Environment. [In review]. https://doi.org/10.21203/rs.3.rs-8151742/v1

Ma, X., Dong, X., Tarrant, A., Yang, L., Kotamarthi, R., Wang, J., Yan, F., Kettimuthu, R. 2025. Diffusion-Based, Data-Assimilation-Enabled Super-Resolution of Hub-height Winds. https://arxiv.org/abs/2510.03364.

Wang, J., Xue, P., Peco, K., Huang, C., Pringle, W.J. et al. Influence of Lake Ice Biases in Reanalysis Data on Downscaled Climate Simulations over the Great Lakes Region. ESS Open Archive . September 29, 2025.DOI: 10.22541/essoar.175916613.30361389/v1

Wang, J., Z. Yang, T. Moustakes, H. Tan, R. Kotamarthi, A. Martili, R. Jackson, P. Muradyan, S. Collis, J. O’Brien, D. Niyogi, and A Sharma. 2025. Compounding Effects of Lake and Urbanization on Summer Precipitation in the Greater Chicago. Urban Climate, https://doi.org/10.1016/j.uclim.2025.102597

Kotamarthi, R., J. Wang, J. Stock, D. K. Fytanidis, T. Munson, D. Niyogi, P. Muradyan, R. Jackson, J. O’Brien, M. Grover, S. Collis, M. A. Gonzalez-Meler, J. Lee, A. Sharma, S. Wang, B. Kaludi, M. Kaplan, S. W. Nesbitt, A. Martilli, R., Jacob, & Cristina Negri. 2025. Artificial Intelligence-Enabled Digital Twin for U.S. Cities. Bulletin of the American Meteorological Society. https://doi.org/10.1175/BAMS-D-25-0229.1

Tobias-Tarsh, L., Jung, C., Wang, J., Bobde, V., Akinsanola, A. A., and Kotamarthi, V. R.: Evaluation of North Atlantic Tropical Cyclones in a Convection-Permitting Regional Climate Simulation, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-1805, 2025.

Peco, K., Wang, J., Jung, C., Sever, G., Sheridan, L., Feinstein, J., Kotamarthi, R., Draxl, C., Young, E., Purkayastha, A., and Kumler, A.: Evaluation of a High-Resolution Regional Climate Simulation for Surface and Hub-height Wind Climatology over North America, Wind Energ. Sci. https://doi.org/10.5194/wes-2025-13, accepted, 2025.

Zeng, Z., Kim, J.-H. (Jeannie), Tan, H., Hu, Y., Cameron-Rastogi, P., Villa, D., New, J., Wang, J., & Muehleisen, R. T. (2025). A review of future weather data for assessing climate change impacts on buildings and energy systems. Renewable and Sustainable Energy Reviews, 212, 115213.
https://doi.org/10.1016/j.rser.2024.115213

Pringle, W. J., Huang, C., Xue, P., Wang, J., Sargsyan, K., Kayastha, M. B., Chakraborty, T. C., Yang, Z., Qian, Y., & Hetland, R. D. Coupled Lake-Atmosphere-Land Physics Uncertainties in a Great Lakes Regional Climate Model. Journal of Advances in Modelling Earth Systems. DOI: 10.1029/2024MS004337.

Liu, J., Kyle, C., Wang, J. Kotamarthi, R., Koval, W., Dukic V., Dwyer, D., Climate change drives reduced biocontrol of the invasive spongy moth. Nat. Clim. Chang. 15, 210–217 (2025). https://doi.org/10.1038/s41558-024-02204-x

2024

Sheridan, L. M., Wang, J., Draxl, C., Bodini, N., Phillips, C., Duplyakin, D., Tinnesand, H., Rai, R. K., Flaherty, J. E., Berg, L. K., Jung, C., and Young, E.: Performance of wind assessment datasets in United States coastal areas, Wind Energ. Sci. [preprint], https://doi.org/10.5194/wes-2024-115, accepted, 2025.

Draxl, C., Wang, J., Sheridan, L., Jung, C., Bodini, N., Buckhold, S., Aghili, C.T., Peco, K., Kotamarthi, R., et al. 2024. WTK-LED: The WIND Toolkit Long-Term Ensemble Dataset. Golden, CO: National Renewable Energy Laboratory. NREL/TP-5000-88457. https://www.nrel.gov/docs/fy25osti/88457.pdf. 

Wang, J., Hendricks, E., Rozoff, C. M., Churchfield, M., Zhu, L., Feng, S., Pringle, W. J., Biswas, M., Haupt, S. E., Deskos, G., Jung, C., Xue, P., Berg, L. K., Bryan, G., Kosovic, B., & Kotamarthi, R. Modeling and Observations of North Atlantic Cyclones: Implications for U.S. Offshore Wind Energy. Journal of Renewable and Sustainable Energy. https://doi.org/10.1063/5.0214806

Wang, J., Deskos, G., Pringle, W. J., Haupt, S. E., Feng, S., Berg, L. K., Churchfield, M., Biswas, M., Musial, W., Muradyan, P., Hendricks, E., Kotamarthi, R., Xue, P., Rozoff, C. M., & Bryan, G. (2024). Impact of Tropical and Extratropical Cyclones on Future U.S. Offshore Wind Energy. Bulletin of the American Meteorological Society. DOI: 10.1175/BAMS-D-24-0080.1.

Yang, Z., Wang, J., Qian, Y., Chakraborty, T. C., Xue, P., Pringle, W. J., Huang, C., Kayastha, M. B., Huang, H., Li, J., & Hetland, R. Summer Convective Precipitation Changes over the Great Lakes Region under a Warming Scenario. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/2024JD041011

Kayastha, M. B., Huang, C., Wang, J., Qian, Y., Yang, Z., Chakraborty, T. C., Pringle, W. J., Hetland, R. D., & Xue, P. (2024). How will Future Climate Reshape Devastating Lake-Effect Snow Storms? Earth’s Future, 10.1029/2024EF004622.

Huang, H., Qian, Y., Bisht, G., Wang, J., Chakraborty, T., Hao, D., Li, J., Thurber, T., Singh, B., Yang, Z., Liu, Y., Xue, P., Sacks, W. J., Coon, E., and Hetland, R.: WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions, Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, 2025.

Lan, F., Gamelin, B., Yan, L., Wang, J., Wang, B., Guo, H. (2024). Topological Characterization and Uncertainty Visualization of Atmospheric Rivers. Published on Computer Graphics Forum, https://doi.org/10.1111/cgf.15084.

González, D. J. X., Morello-Frosch, R., Liu, Z., Willis, M. D., Feng, Y., McKenzie, L. M., Steiger, B. B., Wang, J., Deziel, N. C., & Casey, J. A. (2024). Wildfires increasingly threaten oil and gas wells in the Western United States with disproportionate impacts on marginalized populations. One Earth. In print.

Akinsanola, A. A., Jung, C., Wang, J., & Kotamarthi, V. R. (2024). Evaluation of precipitation across the contiguous United States, Alaska, and Puerto Rico in multi-decadal convection-permitting simulations. Scientific Reports, 14(1), 1238.

2023

Zeng, Z., Kim, J. H., Tan, H., Hu, Y., Rastogi, P., Wang, J., & Muehleisen, R. (2023). A critical analysis of future weather data for building and energy modeling. In Proceedings of the Building Simulation 2023: 18^th Conference of IBPSA, 2023.
https://doi.org/10.26868/25222708.2023.1272.

Wang, J, Qian, Y., Pringle, W., Chakraborty, T., Yang, Z., and Xue, P.2023.Contrasting Effects of Lake Breeze and Urbanization on Heat Stress in Chicago Metropolitan Area.Urban Climate, 48, p.101429.

Pal, S., Wang, J., Feinstein J., Yan E., Kotamarthi, V. R. Projected changes in extreme streamflow and inland flooding in the mid-21^st century over Northeastern United States using ensemble WRF-Hydro simulations, Journal of Hydrology: Regional Studies, 47, 2023, https://doi.org/10.1016/j.ejrh.2023.101371.

Chakraborty, T., Wang, J., Qian, Y., Pringle, W., Yang, Z., Xue, P. 2023. Urban versus lake impacts on heat stress and its disparities in a shoreline city. Geohealth, DOI: 10.1029/2023GH000869

Chakraborty, T., Wang, J., Z. Yang, Y. Qian, W. Pringle, and P. Xue. 2023. Future population-adjusted heat stress extremes over the Great Lakes Region. Earth’s Future, Authorea. March 09, 2023. DOI: 10.22541/essoar.167839993.34040630/v1. Under revision.

Yu, G., Feng, Y., Wang, J., and Wright D., 2023. Performance of Fire Danger Indices and Their Utility in Predicting Future Wildfire Danger over the Conterminous United States. Earth’s Future, DOI: 10.1029/2023EF003823

Kayastha, M. B., Huang, C., Wang, J., Pringle W.J., Chakraborty, TC., Yang Z., Hetland, R., Qian Y., Xue, P. Insights on Simulating Summer Warming of the Great Lakes: Understanding the Behavior of a Newly Developed Coupled Lake-Atmospheric Modeling System. Journal of Advances in Modeling Earth Systems. 15 (7), e2023MS003620

Yang, Z., Qian, Y.,Xue, P., Wang, J., Pringle, W., Li, J., and Chen, X. Moisture Sources of Precipitation in the Great Lakes Region: Climatology and Recent Changes. Geophysical Research Letters. https://doi.org/10.1029/2022GL100682

Chuxuan Li, Guo Yu, J. Wang, Daniel Horton. Toward improved regional hydrologicalmodel performance using a novel soil data-informed calibration method. Water Resources Research, p.e2023WR034431.

Jiang, P., Yang Z., Wang, J., Huang, C., Xue, P., Chakraborty, TC., Chen, X., and Qian Y., Efficient Super-Resolution of Near-Surface Climate Modeling Using the Fourier Neural Operator. Journal of Advances in Modeling Earth Systems, 15 (7), e2023MS003800.

Yan L, Guo H, Peterka T, Wang B, Wang J. TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclones. IEEE Transactions on Visualization and Computer Graphics. 2023 Nov 6.

Puleikis, K., and Wang, J., Puerto Rico Historical Climate Analysis: A closer look at complex tropical terrain. Technical report: https://doi.org/10.2172/1974354

2022

Tan, H., Kotamarthi, R., Wang, J., Qian, Y., & Chakraborty, T. C. (2022). Impact of different roofing mitigation strategies on near-surface temperature and energy consumption over the Chicago metropolitan area during a heatwave event. Science of The Total Environment, 160508.

Maulik, R., Rao, V., Wang, J., Mengaldo, G., Constantinescu, E., Lusch, B., & Kotamarthi, R. (2022). Efficient high-dimensional variational data assimilation with machine-learned reduced-order models. Geoscientific Model Development, 15(8), 3433-3445.

Wang, J., P. Xue, W. Pringle, Z. Yang and Y. Qian. 2022. Impacts of Lake Surface Temperature on the Summer Climate Over the Great Lakes Region. Journal of Geophysical Research, https://doi.org/10.1029/2021JD036231.

Wu, Q., J. Bessac, W. Huang and J. Wang. 2022. Station-wise statistical joint assessment of wind speed and direction under future climates across the United States. Advances in Statistical Climatology, Meteorology and Oceanography, 8, 205–224.

Byun, K., Sharma, A., Wang, J., Tank, J. L., & Hamlet, A. F. (2022). Intercomparison of Dynamically and Statistically Downscaled Climate Change Projections over the Midwest and Great Lakes Region. Journal of Hydrometeorology, 23(5), 659-679.

Bhatnagar, S., Chang, W., Kim, S., & Wang, J. (2022). Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression. SIAM/ASA Journal on Uncertainty Quantification, 10(1), 1-26.

2021

Feng, Y., R. Maulik, J. Wang, P. Balaprakash, W. Huang, V.N. Rao, P. Xue, et al. 2021: Characterization of Extremes and Compound Impacts: Applications of Machine Learning and Interpretable Neural Networks. US Department of Energy (DOE) - Office of Science. https://doi.org/10.2172/1769686

Wang, J., R. Kotamarthi, V. Ghate, B. Lusch, P. Balaprakash, J. Wozniak, X. Yuan, 2021: A Hybrid Climate Modeling System Using AI-assisted Process Emulators. US Department of Energy (DOE) - Office of Science. https://doi.org/10.2172/1769645. 

Li, C., Handwerger, A. L., Wang, J., Yu, W., Li, X., Finnegan, N. J., & Horton, D. E. (2021). Augmentation and Use of WRF-Hydro to Simulate Overland Flow-and Streamflow-Generated Debris Flow Hazards in Burn Scars. Natural Hazards and Earth System Sciences, 22, 2317–2345, 20221-47.

Gamelin, B., Feinstein, J., Wang, J., Bessac, J., Yan, E. and Kotamarthi, V. 2022. Projected U.S. Drought Extremes Through the 21^st Century with Vapor Pressure Deficit. Scientific Reports 12, Article number: 8615.

Wang, J., Liu, Z., Foster, I., Chang, W., Kettimuthu, R., & Kotamarthi, V. R. (2021). Fast and accurate learned multiresolution dynamical downscaling for precipitation. Geoscientific Model Development, 14(10), 6355-6372.

Pringle, W. J., Wang, J., Roberts, K. J., & Kotamarthi, V. R. (2021). Projected Changes to Cool‐Season Storm Tides in the 21^st Century Along the Northeastern United States Coast. Earth’s Future, 9(7), e2020EF001940.

2020

Schwarzwald, K., Poppick, A., Rugenstein, M., Bloch-Johnson, J., Wang, J., McInerney, D., & Moyer, E.J. 2020. Changes in future precipitation mean and variability across scales. Journal of Climate. 1-55.

Brown, E., J. Wang, and Y. Feng, 2020: 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/aba868.

2019

Wang, J., Balaprakash, P., and Kotamarthi, R., 2019: Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model, Geosci. Model Dev., 12, 4261–4274.

Wang, J., Wang, C., Rao, V., Orr, A., Yan, E., and Kotamarthi, R., 2019: 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. https://doi.org/10.5194/gmd-12-3523-2019.

2018

Ebenstein, R., G. Agrawal, J. Wang, J. Boley and R. Kettimuthu. 2018: FDQ: Advance Analytics Over Real Scientific Array Datasets,” 2018 IEEE 14^th International Conference on e-Science (e-Science), 2018, pp. 453-463, doi: 10.1109/eScience.2018.00134.

Zobel, Z., J. Wang, D. J. Wuebbles, and V. R. Kotamarthi. 2018: Analyses for High‐Resolution Projections through the End of the 21^st Century for Precipitation Extremes over the United States. Earth’s Future. https://doi.org/10.1029/2018EF000956

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/s00382-018-4294-0.

2017

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/10.1002/2017EF000642.

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

2016

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.DOI: 10.1175/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

2015

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, 1239–1259, 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 (2015), High-resolution dynamically downscaled projections of precipitation in the mid and late 21^st century over North America, Earth’s Future, 3, doi:10.1002/2015EF000304.

2014

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, 8778–8797, doi:10.1002/2014JD021696

2013

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, 1576–1591