Assimilation of GPM Microwave Imager Radiance data with the WRF hybrid 3DEnVar system for the prediction of Typhoon Chan-hom (2015)

Abstract

The impact of assimilating radiances from the global satellite precipitation program (GPM) Microwave Imager (GMI) on the track prediction of typhoon Chan-hom was studied with a hybrid three-dimensional ensemble-variational (3DEnVar) data assimilation (DA) system. It is found that the data assimilation of clear-sky GMI radiance is able to depict the structure of typhoon well by warming the typhoon inner core area. The bias correction coefficients obtained by the off-line model along with the variational bias correction method can reduce the bias. GMI radiance data assimilation experiment can effectively adjust the core area of Typhoon “Chan-hom” and systematically correct the typhoon position in the background of the model. The sensitivity of the hybrid 3DEnVar DA behaviors on the choice of the ensemble members is also investigated. Results show that the 3DEnVar Global Ensemble Forecast System (3DEnVar_GEFS)-based ensemble spread is essentially dominated in the vicinity of Typhoon Chan-hom, which is less disturbed by random errors. In general, the track forecasts from 3DEnVar_RCV match better with the best track by adding Gaussian noise randomly based on the background error than those from the 3DVAR, and 3DEnVar_GEFS.