Dr. Jiali Wang is an atmospheric scientist in Environmental Science Division at Argonne. Dr. Wang received her Ph.D degree in atmospheric science in 2012, and has been working at Argonne since then. She also holds a fellowship member with NAISE at Northwestern University, as well as CASE at University of Chicago. Dr. Wang specializes in physical understanding climate and extreme climate variabilities and their impacts on water, energy, and infrastructure through high-resolution numerical modeling, data analysis, as well as machine learning. Extreme events she has been working on include: flooding, tropical and extra-tropical storms, droughts/wildfires, wind gusts, and heatwaves over North America, including Great Lakes region, coastal urban area, as well as Caribbean islands. Dr. Wang served as a principle investigator and co-investigators for projects supported by DoE EERE, Office of Science (projects: wind resource uncertainty quantification; extreme weather impacts on offshore wind energy; Great Lakes regional modeling; Puerto Rico 100% renewable energy), DoD (projects: regional climate modeling and climate extremes), DHS (projects: regional resiliency assessment program), industrial sectors such as AT&T, Pacific PG&E (projects: climate change induced risk, and resilience), as well as Argonne Laboratory Directed Research and Development.
- Climate variability assessment using statistics
- Climate extreme and risk assessment
- Uncertainty quantification for climate modeling
- Climate change impacts on critical infrastructure, agriculture, ecology, and health
- Hydrological modeling;
- Urban climate modeling
- Machine learning and deep learning for climate science
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
Wang, Jiali, Yun Qian, William Pringle, TC Chakraborty, Zhao Yang and Pengfei Xue. 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. (2022).
Projected changes in extreme streamflow and inland flooding in the mid-21st 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.
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
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
Chakraborty, T., Wang, J., Qian, Y., Pringle, W., Yang, Z., Xue, P. Urban versus lake impacts on heat stress and its disparities in a shoreline city. Geohealth, under revision. https://doi.org/10.21203/rs.3.rs-1818535/v1
T. Chakraborty, J. Wang, 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.
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.
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.
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, Brandi, Jeremy Feinstein, Jiali Wang, Julie Bessac, Eugene Yan and Veerabhadra Kotamarthi. 2022. Projected U.S. Drought Extremes Through the 21st 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 21st Century Along the Northeastern United States Coast. Earth’s Future, 9(7), e2020EF001940.
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.
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.
Ebenstein, R., G. Agrawal, J. Wang, J. Boley and R. Kettimuthu. 2018: FDQ: Advance Analytics Over Real Scientific Array Datasets,” 2018 IEEE 14th 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 21st 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.
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
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
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 21st century over North America, Earth’s Future, 3, doi:10.1002/2015EF000304.
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
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
Feng, Yan, Romit Maulik, Jiali Wang, Prasanna Balaprakash, Whitney Huang, Vishwas N Rao, Pengfei Xue, et al.“Characterization of Extremes and Compound Impacts: Applications of Machine Learning and Interpretable Neural Networks .” https://doi.org/10.2172/1769686
Wang, Jiali, Rao Kotamarthi, Virendra Ghate, Bethany Lusch, Prasanna Balaprakash, N Justin Wozniak, Xingqiu Yuan, US Department of Energy (DOE) - Office of Science, “A Hybrid Climate Modeling System Using AI-assisted Process Emulators.” Feb 15, 2021. https://doi.org/10.2172/1769645