Tao Zhang

Brookhaven National Laboratory

Environmental Science and Technologies Department
Bldg. 815, Room 2-17
P.O. Box 5000
Upton, NY 11973-5000

(631) 344-8915
tzhang@bnl.gov

Expertise | Research | Education | Publications | Awards

Expertise

• Machine learning parameterization 
• Machine learning PDE solver 
• Uncertainty quantification

Research Activities

• Scientific Associate: 2023-present, Brookhaven National Laboratory
• Research Associate: 2017-2022, Brookhaven National Laboratory

Education

• Department of Computer Science and Technology, Tsinghua University, PhD, 2017
• School of Software Engineering, Tianjin University, B.E., 2011

Selected Publications

• Zhang T, Morcrette C, Zhang M, et al (2024) A Fortran-Python Interface for Integrating Machine Learning Parameterization into Earth System Models. https://doi.org/10.5194/gmd-2024-79
• Zhang T, Li L, López-Marrero V, et al (2024) Emulator of PR-DNS: Accelerating Dynamical Fields With Neural Operators in Particle-Resolved Direct Numerical Simulation. Journal of Advances in Modeling Earth Systems 16:. https://doi.org/10.1029/2023ms003898
• Atif M, López-Marrero V, Zhang T, et al (2023) Towards Accelerating Particle-Resolved Direct Numerical Simulation with Neural Operators
• Yao Z, Zhang T, Wu L, et al (2023) Physics-Informed Deep Learning for Reconstruction of Spatial Missing Climate Information in the Antarctic. Atmosphere 14:658. https://doi.org/10.3390/atmos14040658
• Chen M, Fu H, Zhang T, Wang L (2023) ResU-Deep: Improving the Trigger Function of Deep Convection in Tropical Regions With Deep Learning. Journal of Advances in Modeling Earth Systems 15:. https://doi.org/10.1029/2022ms003521
• Song J, Tong G, Chao J, et al (2023) Data driven pathway analysis and forecast of global warming and sea level rise. Scientific Reports 13:. https://doi.org/10.1038/s41598-023-30789-4
• Wu D, Wu L, Zhang T, et al (2022) Short-Term Rainfall Prediction Based on Radar Echo Using an Improved Self-Attention PredRNN Deep Learning Model. Atmosphere 13:1963. https://doi.org/10.3390/atmos13121963
• Liu W, Liu Y, Zhang T, et al (2022) Use of physics to improve solar forecast: Part II, machine learning and model interpretability. Solar Energy 244:362–378. https://doi.org/10.1016/j.solener.2022.08.040
• Zhang T, Lin W, Vogelmann AM, et al (2021) Improving Convection Trigger Functions in Deep Convective Parameterization Schemes Using Machine Learning. Journal of Advances in Modeling Earth Systems 13:. https://doi.org/10.1029/2020ms002365
• Wu L, Zhang T, Qin Y, Xue W (2020) An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3). Geoscientific Model Development 13:41–53. https://doi.org/10.5194/gmd-13-41-2020
• Zhang T, Lin W, Lin Y, et al (2019) Prediction of Tropical Cyclone Genesis from Mesoscale Convective Systems Using Machine Learning. Weather and Forecasting 34:1035–1049. https://doi.org/10.1175/waf-d-18-0201.1
• Zhang T, Zhang M, Lin Y, Xue W, Lin W, Yu H, He J, Xin X, Ma H-Y, Xie S, Zheng W (2018) Automatic tuning of the Community Atmospheric Model CAM5.3 by using short-term hindcasts with an improved downhill simplex optimization method. doi: 10.5194/gmd-2018-87
• Xu H, Zhang T, Luo Y, et al (2018) Parameter calibration in global soil carbon models using surrogate-based optimization. Geoscientific Model Development 11:3027–3044. https://doi.org/10.5194/gmd-11-3027-2018
• Xie F, Xue W, Li L, et al (2017) Quantification of the responses of equatorial Pacific surface wind to uncertain cloud-related parameters in GAMIL2. Atmospheric Science Letters 18:458–465. https://doi.org/10.1002/asl.789
• Zhang T, Li L, Lin Y, Xue W, Xie F, Xu H, Huang X (2015) An automatic and effective parameter optimization method for model tuning. Geoscientific Model Development Discussions 8:3791–3822. doi: 10.5194/gmdd-8-3791-2015
• Zhang T, Sun X, Xue W, et al (2015) ParSA: High-throughput scientific data analysis framework with distributed file system. Future Generation Computer Systems 51:111–119. https://doi.org/10.1016/j.future.2014.10.015
• Xin X, Xue W, Zhang M, et al (2014) How much of the NAO monthly variability is from ocean–atmospheric coupling: results from an interactive ensemble climate model. Climate Dynamics 44:781–790. https://doi.org/10.1007/s00382-014-2246-x
• Zhang J, Xue W, Zhang M, et al (2014) Climate impacts of stochastic atmospheric perturbations on the ocean. International Journal of Climatology 34:3900–3912. https://doi.org/10.1002/joc.3949

Awards & Recognition

• BNL Spotlight Award, 2024
• Schlumberger Calculating Earth Sciences Scholarship, 2014
• IBM Chinese Excellent Student Scholarship, 2011