报告题目：
Coupling Data Science and Numerical Simulations to Empower Atmospheric and Environmental Research
报 告 人：郑中华 博士
报告时间：2023年9月20日（周三）上午9:30
报告地点：气象楼1115会议室
主 持 人：陆春松教授 杨璟副教授
报告人简介：
Dr. Zhonghua Zheng is a Lecturer (equivalent in level to Assistant Professor in the USA) in Data Science and Environmental Analytics at The University of Manchester, United Kingdom. His research interests include environmental data science, urban climate and environment, and air quality and aerosol properties. He received a Master of Computer Science degree and a Ph.D. in Environmental Engineering in Civil Engineering with a concentration in Computational Science and Engineering, both from the University of Illinois Urbana-Champaign (UIUC). He also holds an M.S. in Agricultural and Biological Engineering from UIUC, and a B.Eng. from Zhejiang University (China).
报告简介：
We have entered the age of Data Science. Massive amounts of data from numerical simulations of the Earth system are now common in atmosphericand environmental research. However, state-of-the-art Earth System Models (ESMs) are subject to limitations because of the multiscale nature of the Earth system, where processes on scales smaller than the computational grid resolution remain unresolved and can only rely on simplified representations. These simplified representations introduce large yet frequently poorly characterized uncertainties in climate simulations. Therefore, making sense and making use of these simulation data remains a fundamental challenge.
In this talk, I will share my vision of coupling Data Science and numerical simulations to create a suite of tools for addressing and overcoming the limitations induced by the model representations, focusing on two high-impact applications areas: (I) representation of urban environments in ESMs to predict climate extremes, and (II) representation of aerosol particles in the atmosphere as important players that modulate climate and impact human health. I will then introduce the framework of evaluating the information content of satellite data for PM2.5 (air quality) estimates using the simulations from a chemical transport model and automated machine learning (AutoML). These efforts culminate in an improved understanding of the role of Data Science in atmospheric and environmental research.
大气物理学院
2023年9月19日