Ensemble filtering with displacement errors

Michael Ying

NCAR, Boulder, Colorado

Seminar Date: 
12. February 2020 - 13:00 - 14:00

An outstanding issue for multiscale weather prediction is the choice of data assimilation methods. Since small scale features rapid error growth that gives rise to nonlinearity, data assimilation methods based on linearization, such as the ensemble Kalman filter (EnKF), performs suboptimally. Position error of convective clouds among ensemble members is one of the common causes of nonlinearity and has been a major challenge for data assimilation. Previous studies have made some progress in developing nonlinear optimization methods to reduce position errors.

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