EAPS

"Predictability and Dynamics of Weather and Climate at the Regional Scales | Ensemble-based data assimilation and parameter estimation"

Speaker: Fuqing Zhang, Professor of Meteorology, Director, Center for Advanced Data Assimilation and Predictability Techniques, Penn State, University

Abstract: Despite the inherent limit of predictability, there is still significant room for improving the practical predictability of severe weather and tropical cyclones through advanced data assimilation techniques, better use of exiting or future observations, and improved forecast models. Ensemble-based data assimilation is a state estimation technique that uses short-term ensemble forecasts to estimate flow-dependent background error covariance and is best known by varying forms of ensemble Kalman filters (EnKFs). The EnKF has recently emerged as one of the primary alternatives to the variational data assimilation methods widely used in both global and limited-area numerical weather prediction models. In addition to comparing the EnKF with variational methods, I will try to review recent advances and challenges in the development and applications of the EnKF, including its hybrid with variational methods, in limited-area models that resolve weather systems from convective to meso- and regional scales. Moreover, the EnKF can also be used to characterize and estimate uncertainties and errors in the forecast model through parameter estimation, as will be exemplified by both proof-of-concept experiments and real-data studies.