Zheng Wu

Zheng Wu

Assistant Professor
Department of Atmospheric & Environmental Sciences
CV143.34 KB


ETEC 423

PhD, Atmospheric Sciences, University of Utah, 2019
MS, Atmospheric Sciences, University of Utah, 2017
BS, Atmospheric Sciences, Sun Yat-sen University, China, 2015

Zheng Wu


My research strives to unravel the complexities of large-scale atmospheric dynamics, global remote teleconnections, and their roles in atmospheric predictability across a broad spectrum of timescales. To achieve this, I employ a multifaceted approach, incorporating theoretical diagnostics, numerical simulations using general circulation models (GCMs), and data-driven approaches, with a particular emphasis on Artificial Intelligence (AI) methods. One topic of my research delves into the realm of stratosphere-troposphere coupling and its impacts on stratospheric variability using statistical-based methods and dynamical analyses on both reanalysis data and climate model simulations. To better understand the stratospheric dynamics and its interplay with the troposphere, I modified and improved an idealized GCM from the Geophysical Fluid Dynamics Laboratory (GFDL) by introducing a more realistic asymmetric forcing, which resulted in a more faithful representation of circulation and stratospheric variability when compared to previous studies. This improved representation of circulation and stratospheric variability allows further exploration of tropics-extratropics
interactions, stratosphere-troposphere coupling, and the atmospheric circulation response to global warming.

In recent years, my research has extended to incorporate machine learning (ML) techniques and their applications to improve our understanding of atmospheric circulation and dynamics and the predictability of subseasonal and seasonal forecasts. ML/AI methods have shown substantial potential in expediting weather and climate simulations and bolstering forecast accuracy across various spatial and temporal scales. Beyond improving model prediction skill, my research is dedicated to understanding whether ML/AI models generate good predictions for scientifically sound reasons.

To this end, my research focuses on developing and applying advanced (explainable / interpretable / trustworthy) AI methods to investigate the sources of predictability of climate extreme events, such as drought, floods, heatwaves, cold air outbreaks, etc.; to determine the biases and misrepresentations of the dynamical processes and teleconnections in climate models; and to evaluate the future projections of atmospheric circulation and extreme events. My research goals are to extend the horizon of predictability, evaluate the impacts of global warming on regional weather, climate, and ecosystems, and provide insights for climate mitigation and adaptation.

Research Interests

My research interests lie in understanding the dynamical processes in the Earth’s climate system, global remote connections, predictability of weather phenomena and extreme events, and future projections of atmospheric circulation and extremes. My research is centered at the intersection between atmospheric and climate sciences and Artificial Intelligence. This interdisciplinary approach aims to propel our comprehension of atmospheric and climate dynamics and predictability under current and future climates.