Assimilating Vortex Position with an Ensemble Kalman Filter

These observations of vortex position, which in practice might be available from geostationary satellite or radar imagery, can be easily assimilated with an ensemble Kalman filter (EnKF) given an operator that computes the position of vortex in the background forecast. The simple linear updating scheme used in the EnKF begins to degrade if the background forecast errors of the vortex position are comparable to the vortex size. Observations of the vortex shape and intensity can be assimilated along with the position to extend its effectiveness.

Assimilation of position observation as well as shape and intensity over time produces an analysis in which the vortex is, within observational uncertainty, in the correct location and does not surfer from spurious transient evolution in short-term forecasts. Simulations using a simple 2D barotropic model show that the track forecast initialized with the EnKF analysis is improved.