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Uncertainties in Atmospheric Numerical Modeling
Uncertainties in Atmospheric Numerical Modeling
教师介绍

本讲教师:赵纯
所属学科:理科
人  气:120

课程介绍
报告人简介:赵纯,教授,博士生导师,中国科学技术大学地球和空间科学学院大气科学专业。2005年本科毕业于中国科学技术大学,2009年于美国佐治亚理工大学获得大气科学博士学位,之后在美国能源部太平洋西北国家实验室从事研究工作。2016年入选“青年千人”计划回国任教。长期从事区域及全球模式的开发和应用,主要专注如何利用数值模拟的方法来研究区域大气污染、气候、极端天气以及它们之间的相互作用。高度参与了WRF-Chem模式的开发及其在大气污染及气溶胶、辐射、云、雨相互作用方面的应用。同时也参与开发和应用全球变尺度模式来研究大气中多尺度之间的相互作用。截止2019年8月已在Nature Geoscience、Nature Communication、PNAS、ACP、JGR等国际SCI期刊上发表文章80余篇,SCI总引2000余次,H因子27。长期担任多个学术期刊评审和基金评审专家,并多次主持国际会议专题会场,目前担任JGR-Atmosphere副主编。2017年曾获“求是”杰出青年学者奖。 报告内容简介:Numerical modeling is a very useful approach to understand atmospheric processes, and also for weather and climate prediction. Since 1950s, atmospheric model becomes more and more important for research of atmospheric science. It also becomes a critical component of Earth system model. Nowadays, it has been widely used for understanding the mechanisms of extreme events, climate change, air pollution, and etc. However, atmospheric model has never been perfect, and includes many simplified and uncertain parameterizations of complex and multi-scale atmospheric processes. Therefore, it is necessary to understand the uncertain sources of atmospheric model so that simulation result can be better interpreted. In this seminar, some uncertainties associated with modeling atmospheric aerosol and its climatic impact will be discussed. One uncertainty quantification (UQ) framework will also be introduced to investigate the sensitivity of modeling results to the selected parameters. These studies can provide useful and practical guidance on the improvement of physical and chemical parameterizations to reduce model uncertainties in simulating extreme events, climate change, air quality, and etc. Numerical modeling is a very useful approach to understand atmospheric processes, and also for weather and climate prediction. Since 1950s, atmospheric model becomes more and more important for research of atmospheric science. It also becomes a critical component of Earth system model. Nowadays, it has been widely used for understanding the mechanisms of extreme events, climate change, air pollution, and etc. However, atmospheric model has never been perfect, and includes many simplified and uncertain parameterizations of complex and multi-scale atmospheric processes. Therefore, it is necessary to understand the uncertain sources of atmospheric model so that simulation result can be better interpreted. In this seminar, some uncertainties associated with modeling atmospheric aerosol and its climatic impact will be discussed. One uncertainty quantification (UQ) framework will also be introduced to investigate the sensitivity of modeling results to the selected parameters. These studies can provide useful and practical guidance on the improvement of physical and chemical parameterizations to reduce model uncertainties in simulating extreme events, climate change, air quality, and etc.

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