Prof. Fuqing Zhang: Simultaneous ensemble-based state and parameter estimation for earth systems

Professor Fuqing Zhang: (Penn State)Professor Fuqing Zhang: (Penn State)We seek to develop and apply a generalized data assimilation framework using Ensemble-based Simultaneous State and Parameter Estimation (ESSPE) that will facilitate data-model integration and uncertainty quantification for the broad weather, climate and earth science communities. Through augmenting uncertain model parameters as part of the state vector, the ESSPE framework allows for simultaneous state and parameter estimation using an ensemble Kalman filter (EnKF) through assimilating large-volume in-situ and remotely sensed heterogeneous observations such as those from radiosondes, radars and satellites. The ESSPE framework can be applied to identify key physical processes and their impacts, to better represent and parameterize these processes in dynamical models, to design better observation strategies, to understand predictability and nonlinearity of these processes, and to facilitate generalization of the knowledge from smaller-scale process understanding to larger- and system-scale impacts and parameterizations. Tomorrow Professor Fuqing Zhang from Penn State university will give a seminar at NERSC. 

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 major contributions to the fundamental understanding of atmospheric dynamics and predictability, and he has revolutionized the analysis and prediction of severe weather and hurricanes through incorporating high-resolution radar and satellite observations into cloud-resolving, state-of-the-art numerical weather prediction models with advanced ensemble-based data assimilation methodologies that have been widely adopted by NOAA, and other agencies and researchers in the world. 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. 


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