Dr. Jiali Wang is an atmospheric scientist in Environmental Science Division at Argonne. Jiali 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 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, 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 uncertainty quantification), 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.
- Climate variability assessment using statistics
- Climate extreme analysis using extreme value theory
- Uncertainty quantification for climate modeling
- Extreme climate impacts on other sectors (e.g. 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
Yu, G., J. Wang, Y. Feng, and D. Wright. 2021: Performance of Fire Danger Indices and Their Utility in Predicting Future Wildfire Danger over the Conterminous United States. Environ. Res. Lett., under review
Wang, J., Z. Liu, I. Foster, W. Chang, R. Kettimuthu, and V.R. Kotamarthi. 2021. Fast and accurate learned multiresolution dynamical downscaling for precipitation. Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2020-412, in review, 2021.
Pringle, W.J., J. Wang, K.J. Robert, V.R. Kotamarthi. 2020. Projected changes to cool-season storm tides in the 21st century along the Northeastern United States Coast. Earth’s Future. https://doi.org/10.1002/essoar.10505471.1
Byun, K., A. Sharma, J. Wang, J.L. Tank, A.F. Hamlet, 2020. Intercomparison of Dynamically and Statistically Downscaled Climate Change Projections over the Midwest and Great Lakes Region. Journal of Hydrometeorology. Under review
Bhatnagar, S., W. Chang, S. Kim, and J. Wang. 2020. Computer Model Calibration with Time Series Data using Deep Learning and Quantile Regression. Submitted to Siam/ASA Journal on Uncertainty Quantification. arXiv:2008.13066v2
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, 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