Preliminary study on ensemble transform Kalman filter-based initial perturbation scheme in GRAPES global ensemble prediction.

Initial perturbations scheme is one of the important problems in the ensemble prediction. In this paper, an ensemble initial perturbations scheme for GRAPES is developed in terms of the ensemble transform Kalman filter (ETKF) method, and a new GRAPES global ensemble prediction system (GEPS) is constructed. The spherical simplex 14-member ensemble prediction experiments, using the simulated observation network and error characteristics of simulated observations and innovation-based inflation, are carried out over almost two months. The structural characters, perturbation amplitudes, and growth characters of the ETKF initial perturbations are investigated, and the qualities and abilities for the ensemble initial perturbations are analyzed. The preliminary experimental results indicate that the ETKF-based GRAPES ensemble initial perturbations could identify main normal structures of analysis error variance and reflect on the perturbation amplitudes. The initial perturbations and the spread are reasonable. The initial perturbation variance, which is equaled approximately to the forecast error variance, is found to respond to temporal changes in the observational spatial variations in simulated observational network density. The perturbations generated through the simplex method are also shown to exhibitvery high degree of consistency between initial analysis and short-range forecast perturbations. The appropriate growth and spread of ensemble perturbations can be maintained in 96　h lead time. The statistical results for 52　d ensemble forecasts show that the forecast scores of ensemble average for the Northern Hemisphere is higher than that of the control forecast. Potential additional benefits of the ETKF based initial scheme when using more ensemble members, a real-time observational network and a more appropriate inflation factor will be explored in future.