Public lecture series I:
Speaker: Professor Fuqing Zhang, 2018 Visiting Chair of GoCAS
Date: Thursday 13 September kl. 15:15-17:00
Venue: Euler, Department of Mathematical Sciences, Skeppsgränd 3, Göteborg
(Coffee and snacks will be served after the lecture)
Registration: Last day to sign up was September 6
Weather is an essential part of our daily lives, while the future of our earth and humanity may be significantly and irreversibly impacted by long-term climate changes. Improved weather and climate forecasts can have enormous socioeconomic benefits by better predicting the occurrence of natural disasters at all time scales which can save lives and protect property, and by providing guidance to devise improved policies, regulations, and infrastructures that can better monitor, counter, and adapt to climate changes and global warming. The past six decades we have seen tremendous improvements in weather and climate prediction since the first introduction of numerical weather prediction (NWP) models in Sweden in 1950s. Such advances have been accomplished through coordinated international effort in the investment of big science (advanced understanding of atmospheric physics, better numerical models ), big data (enhanced and accurate observing network including radars and satellites, better algorithms for better use of data) and big computing (billions of times more powerful computers). After a brief introduction on how numerical models work to predict the weather and climate through big data science and computing, this presentation will give an overview of our recent understandings with regards to the following fundamental questions: (1) what are the ultimate limits in the weather and climate prediction? (2) how reliable are our predictions at different spatial and temporal scales? (3) what ultimately limits our predictability of various weather and climate systems? (4) what does it take to maximize our predictive limits?
Dr. Fuqing Zhang is a professor jointly in the Department of Meteorology and the Department of Statistics at the Pennsylvania State University. He is also the founding director of the Penn State Center on Advanced Data Assimilation and Predictability Techniques. His research has revolutionized the analysis and prediction of severe weather and hurricanes through developing advanced data assimilation methodologies that have been widely adopted by forecasting agencies and researchers in the world. He has authored over 200 journal publications with a h-index of 51. He has received numerous honors for his research. Notably, he was the recipient of the 2009 American Meteorological Society's Clarence Leroy Meisinger Award, the 2015 American Meteorological Society’s Banner I. Miller Award, and the 2018 Penn State Faculty Scholar Medal in 2018. He is an elected fellow of both the American Meteorological Society and and the American Geophysical Union.