Data Assimilation Meetings at Reading

Date Meeting type Speakers
21 July 2015
1L61
Invited speaker Fuqing Zhang (Penn State University)
Advances and challenges in regional ensemble and hybrid data assimilation
Despite the inherent limit of mesocale 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.

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