（Department of Meteorology and Climate Science, San Jose State University）
Clouds and aerosols contribute to some of the largest uncertainties in climate change prediction. Understanding cloud properties and their interactions with aerosols is a critical step towards improving the fidelity of climate models. This work examines different types of clouds, such as cirrus clouds and mixed-phase clouds, over wide geographical locations from polar regions, midlatitudes to tropics. Multi-scale observations are used to understand cloud formation and evolution, including aircraft-, ship- and ground-based and spaceborne satellite observations. Novel methods are developed to compare high-resolution observations with global climate model simulations. Our results show that the cirrus clouds in the Southern Hemisphere are more sensitive to increases of aerosol concentrations, and several global climate models underestimate the impacts of aerosols on ice and mixed-phase clouds. The latter part of this talk will focus on air pollution in the Bay Area, specifically fine particulate matter that imposes danger to public health. A fusion method of ground-monitored and satellite-derived PM2.5 data is developed to produce long-term publicly available dataset for surface PM2.5 estimates in California, including emissions from large wildfire events.
Brief introduction to the speaker:
Dr. Minghui Diao is an Associate Professor in the Department of Meteorology and Climate Science at San Jose State University (SJSU). She received the B.S. degree at Peking University and the Ph.D. degree at Princeton University. Some of the awards that she received include the NCAR ASP Faculty Fellowship, Lawrence Livermore National Laboratory Faculty Sabbatical Fellowship, SJSU Early Career Investigator Award, NASA NESSF Graduate Fellowship, and the Princeton Francis Upton Fellowship (the highest graduate fellowship given by the School of Engineering and Applied Mathematics).