Impact of Ensemble Data Assimilation Perturbation and Singular Vector Perturbation on Ensemble Forecasts Across Spatial Scales

Initial perturbations play a crucial role in determining the performance of an ensemble prediction system (EPS). This study comprehensively compares three types of initial perturbations—those generated by ensemble data assimilation (EDA), singular vectors (SV), and their hybrid (EDA-SVINI)—using the China Meteorological Administration (CMA) global forecast model. In addition to conventional ensemble verification metrics, diagnostic tools such as kinetic energy (KE) spectrum analysis and spatial filtering are employed. Compared to the SV-based perturbations currently used operationally in the CMA global EPS (CMA-GEPS), EDA-based perturbations exhibit more smaller-scale structures and higher global perturbation KE, particularly in the tropics. Ensemble forecasting experiments reveal that the EDA method provides superior ensemble spread and perturbation KE in the tropics during the early forecast period. However, the SV method performs better in the extratropics throughout the forecast period and in the tropics during the mid-to-late forecast period. The EDA-SVINI approach improves the overall performance of CMA-GEPS, yielding better spread-error relationships and enhanced forecast skill compared to SV and EDA methods. Results from spatially filtered ensembles further show that EDA-SVINI combines the subsynoptic-scale and mesoscale advantages of EDA-based perturbations in the tropics, with the large-scale and synoptic-scale strengths of SV-based perturbations across the globe. This synergy leads to superior performance across spatial scales and lead times in both tropical and extratropical regions. Consequently, the EDA-SVINI method is planned for implementation in the next upgrade of the CMA-GEPS.