时 间：2023年3月31 14:30     地 点：环境楼C348

Dr. Zhonghua Zheng

Assistant Professor in Oata Science & Environmentol Analytics

The University of Manchester (UoM), United Kingdom

Zhonghua Zheng(郑中华)博士现任英国曼彻斯特大学数据科学与环境分析助理教授、博士生导师。他的主要研究方向包括环境数据科学、城市气候与环境、空气质量和气溶胶。他毕业于美国伊利诺伊大学厄巴纳-香槟分校(UIUC)，获土木工程与环境工程(计算科学与工程)博士学位、计算机科学硕士学位。他于 2021年在美国哥伦比亚大学从事博士后研究工作，师从Arlene M.Fiore 教授和 Oaniel M. Westervelt 助理教授。随后于 2022 年入选美国国家大气研究中心(NCAR)高级研究项目(ASP)博士后项目，开展独立研究。郑博士在曼彻斯特大学共同创建的实验室目前正在招收博士生、访问学生和学者。详情请见 https://m-edol.github.io/opp#chinese(招生主页)和 https://zhonghuqzheng.com/(个人主页)。

Abstract

We have entered the age of Data Science. Massive amounts of data from numerical simulationsof the Earth system are now common in atmospheric and 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: (1) representation of urban environments in ESMs to predict climate extremes, and (ll) 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 mode and automated machine learning (AutoML). These efforts culminate in an improved understanding of the role of Data Science in atmospheric and environmental research.