dc.description.abstract | The background error covariance (COV) in 3DVAR does not have the flow-dependent quality. It will have a higher possibility to fit better to the realistic atmosphere data if the COV in other systems is flow-dependent. This study selects two data assimilation systems with the flow-dependent quality. EAKF is a kind of ensemble Kalman filter with the background error based on a calculation of various member spreads predicted by the perfect nonlinear model, therefore contributing to the flow-dependent quality. On the other hand, 4DVAR is a kind of four-dimensional variational method based on the model adjoint. Compared to 3DVAR, 4DVAR contains an implicit flow-dependent quality. In this study, we attempt to compare the performances of these two assimilation systems with the WRF model.
The first assimilation starts at 0000 UTC 19 Oct. 2010 as the Typhoon Megi is west of Philippines. Due to that fact that the NCEP global analysis has already contained the observations of GPSRO and GTS, EAKF has been conducted for six hours in order to avoid the influences of GPSRO and GTS, which also allows the initial field to have a better balance with the model, and then this forecast field is used as the initial field to proceed the next assimilation. After each cycle, the model simulation is integrated for 72 hours using the ensemble mean for the EAKF experiments.
For the 4DVAR experiments, the WRF model with the NCEP analysis at 1800 UTC 18 Oct. 2010 is integrated for three hours to obtain the initial field as the background with assimilation of observation data in next six hours to obtain the analysis field which is then used as the initial field for a simulation of three hours. The simulated field is then integrated for 72 hours for comparison with EAKF experiments. In total, there are five cycles for both EAKF and 4DVAR, separated by six hours.
On verification of the analyses, EAKF is found to be better than 4DVAR. The model results after cycle run show that the simulated typhoon track from EAKF is improving as cycling increases, however the 4DVAR performances are less improved at later cycles. EAKF also gives a weaker intensity of Megi than 4DVAR. For impact of observation data, EAKF shows bigger benefits of GPSRO in typhoon predictions than 4DVAR.
For the sensitivity of spin-up period in EAKF experiments, it was found that the simulated typhoon track is improved as the spin-up of 6 h for the EAKF experiments is increased to 72 h. However, an increase in the ensemble members doesn’t lead to significant improvement on both model analysis and typhoon forecasts.
| en_US |