个人简介：

郑中华博士现任英国曼彻斯特大学助理教授（博士生导师），方向为数据科学与环境分析。加入曼彻斯特大学前，他曾任美国国家大气研究中心（NCAR）高级研究项目（ASP）博士后研究员并开展独立研究。他的主要研究方向包括环境数据科学（机器学习、遥感数据、地球系统模式等）、城市气候与环境、空气质量和气溶胶。他的研究工作获得了美国国家科学基金会（通过NCAR博士后研究员基金）、亚马逊（AWS）以及微软的资助。郑中华博士本科毕业于浙江大学生物系统工程专业，于2020年12月获得美国伊利诺伊大学厄巴纳-香槟分校（UIUC）博士学位，专业领域为土木工程与环境工程（计算科学与工程）。他在读博期间曾连续三年在拜耳公司（作物科学事业部）以及美国橡树岭国家实验室担任数据科学实习生（Data Scientist/Research Intern）。

报告摘要：

We have entered the age of Data Science. Massive amounts of data from numerical simulations of 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: (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.