Prof. Xiaoming Shi’s research lies broadly in atmospheric and climate sciences, focusing on advancing the understanding and modeling of multiscale processes in the Earth’s atmosphere. His interests include turbulence and convection in the gray zones, cloud–radiation and aerosol–convection interactions, and tropical and orographic precipitation systems. By integrating theory, high-resolution simulations, and machine-learning techniques, his group develops scale-adaptive parameterizations and hybrid AI–physics models to improve weather and climate prediction. Their goal is to achieve physically consistent, high-fidelity simulations that enhance the reliability of regional and global climate projections.

Research Themes under ENVR

• Climate Adaptation and Resilience (CARE)

Research Areas

• Mesoscale meteorology
• Planetary boundary layer
• Climate change
• AI Applications

Research Interests

• Extreme weather under climate change
• Turbulence and convection
• AI for weather and climate modeling
• Cloud dynamics
• Numerical simulations

Research Projects

• Developing a Scale-Adaptive Cumulus Parameterization for Simulating Tropical Convection Across Scales
• Estimating Tropical Cyclone Changes Due to Global Warming with Smart Dynamical Downscaling and Convection-Permitting Simulations
• Large Eddy Simulation Code in JAX: An Accelerated and Differentiable Atmospheric Model for Turbulence Parameterization Development
• Study of the regional earth system for sustainable development under climate change in the Greater Bay Area

Publications

Representative Publications

• Chen, J. and X. Shi, 2025: Impacts of Numerical Advection Schemes and Turbulence Modeling on Gray-Zone Simulation of a Squall Line, Monthly Weather Review, 153, 1001–1020, https://doi.org/10.1175/MWR-D-24-0174.1.
• Wang, Y., H. Li, X. Shi, J. Fung, 2025: Assessing the Impact of Cumulus Convection and Turbulence Parameterizations on Typhoon Precipitation Forecast, Geophysical Research Letters, 52, e2024GL112075. https://doi.org/10.1029/2024GL112075.
• Shi, X., Y. Liu, J. Chen, H. Chen, Y. Wang, Z. Lu, R.Q. Wang, J. Fung, C. W.W. Ng, 2024: Escalating Tropical Cyclone Precipitation Extremes and Landslide Hazards in South China under Global Warming. npj Climate and Atmospheric Science, 7, 107, https://doi.org/10.1038/s41612-024-00654-w.
• Qu, Y., and X. Shi, 2023: Can a Machine Learning–Enabled Numerical Model Help Extend Effective Forecast Range through Consistently Trained Subgrid-Scale Models?. Artificial Intelligence for the Earth Systems, 2, e220050, https://doi.org/10.1175/AIES-D-22-0050.1.
• Wang, Y., X. Shi, L. Lei, and J. C. Fung, 2022: Deep-Learning Augmented Data Assimilation: Reconstructing Missing Information with Convolutional Autoencoders, Monthly Weather Review, 150(8), 1977-1991. https://doi.org/10.1175/MWR-D-21-0288.1.
• Shi, X., 2020: Enabling Smart Dynamical Downscaling of Extreme Precipitation Events With Machine Learning, Geophysical Research Letters, 47, e2020GL090309. https://doi.org/10.1029/2020GL090309.

Full Publication List