Dr. Fuqing Zhang is a professor in the Department of Meteorology and Department of Statistics at the Pennsylvania State University. He also directs the Penn State Center on Advanced Data Assimilation and Predictability Techniques. He is currently serving as the visiting Distinguished Chair of the Gothenburg Chair Programme for Advanced Studies in Sweden. He has made fundamental contributions to atmospheric dynamics, predictability, and data assimilation. He has authored over 200 peer-reviewed journal publications with a h-index of 51. A 2017 data analytic study conducted by the Chinese Academy of Sciences ranked Professor Fuqing Zhang as #1 of the world top-50 most impactful scientists in the category of “Meteorology and Atmospheric Science” over the period from 2011-2015 based on the citation database provided by ISI Web of Science.
He has given over 280 invited or keynote talks at various institutions and professional meetings. He has given US congressional briefings on science’s impacts on weather prediction and economy, and his research has been featured in published interviews by Nature, Science, Reuters, Washington Post, and other science or media outlets. He is one of the three editors for the most recent, 6-volume edition of the Encyclopedia of Atmospheric Sciences, along with editorship for various journals including Monthly Weather Review, Atmospheric Science Letters, Journal of Meteorological Research, and Science China. He also served on various advisory boards and expert panels for numerous organizations which include NSF, NASA, NOAA, UK Met Office, Office of Naval Research, American Meteorological Society, WMO, and National Academies, as well as serving as consultant for several weather-related private businesses. He has mentored more than 50 graduate students and postdoctoral scholars who are now becoming emerging leaders in their respective professions including university professors, government researchers, and private-sector innovators.
He has received numerous awards for his research. Notably, in 2009, he was the sole recipient of the American Meteorological Society's 2009 Clarence Leroy Meisinger Award "for outstanding contributions to mesoscale dynamics, predictability and ensemble data assimilation." In 2015, he received the American Meteorological Society’s Banner I. Miller Award “for valuable insights into incorporating real-time airborne Doppler radar measurements via ensemble data assimilation, leading to improvements in forecasts of tropical cyclone track and intensity.” He is an elected fellow of both the American Meteorological Society and the American Geophysical Union.
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?
Arranged date for the seminar talk: Sep 27, 2018