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—generated by ensemble data assimilation (EDA), singular vectors (SV), and their hybrid EDA–SV—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 with 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–SV approach improves the overall performance of CMA-GEPS, yielding better spread–error relationships and enhanced forecast skill compared with SV and EDA methods. Results from spatially filtered ensembles further show that EDA–SV 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–SV method is planned for implementation in the next upgrade of the CMA-GEPS.

Madden–Julian oscillation (MJO) is one of the dominant sources of extended-range atmospheric predictability. However, many atmospheric numerical models fail to predict MJO owing to limitations in representing air–sea interaction processes. This study investigated the role of sea surface temperature (SST) forcing in improving MJO predictability using China Meteorological Administration uncoupled Global Ensemble Prediction System (CMA-GEPS), extending forecasts from 16 to 35 days for the first time by applying different SST forcing schemes. Three SST forcing schemes are examined: (1) CTL SST, which uses fixed SST forcing, (2) E-Folding SST, which adjusts initial analyses toward observed climatology, relying primarily on the historical observations, and (3) Two-Tiered SST, which combines the bias-corrected SST analysis and predictions from the CMA coupled prediction system. The results confirmed that each SST forcing scheme is reasonably designed, with the Two-Tiered scheme providing better SST forcing information and more realistic equatorial wave features. For MJO prediction, the Two-Tiered scheme exhibits superior skill, particularly with strong MJO initialization, extending predictability to 20.6 days, i.e., 4.7 days longer than the CTL run and 3.4 days longer than the E-Folding test. Furthermore, the Two-Tiered scheme outperforms CTL and E-Folding over the western Indian Ocean, but it underperforms over the Maritime Continent. It exhibits poorer eastward propagation characteristics and larger MJO errors due to larger SST forcing errors, which mainly from the analysis-related errors of the coupled model. Larger SST forcing errors increase the water vapor biases and amplify MJO prediction errors, indicating the need for optimized SST forcing. Additionally, the Two-Tiered scheme significantly enhances anomaly correlation coefficient (ACC) skills for 500-hPa geopotential height, with the improvement closely linked to the enhancement in MJO forecast skill. Overall, even the one-way SST forcing that includes multi-faceted information of the coupled model can provide guidance for extended-range prediction with atmospheric-only numerical models.

This paper reviews the development of ensemble weather forecast and the primary techniques employed in the main ensemble prediction systems (EPSs) designed by China and other countries. Here, the emphasis is placed on the advancements in the China Meteorological Administration (CMA) global and regional ensemble prediction systems (i.e., CMA-GEPS and CMA-REPS), with particular attention to operational technologies such as initial and model perturbation methods and the applications of ensemble forecast. Through comparative verification with EPSs from other leading international numerical weather prediction (NWP) centers, CMA’s EPSs demonstrate forecast skills comparable to its global counterparts. As EPSs progress to convective scales and coupled systems between sea, land, air, and ice, the paper addresses some key challenges in ensemble forecast technologies across the aspects of operation, science, integration of artificial intelligence (AI), merging of weather and climate models, and challenging user requirements. Finally, a summary of conclusions and future perspectives on ensemble forecast are provided.

Evaluating whether an ensemble prediction system (EPS) can accurately represent forecast uncertainty is a key aspect of model development and ensemble forecast applications. In this study, a four-dimensional diagnostic analysis model for assessing ensemble forecast uncertainty is proposed, by analyzing the relationship between the ensemble spread and root-mean-square error (RMSE) of the ensemble mean in terms of their temporal evolution (one-dimensional) and spatial distribution (three-dimensional), together with use of the linear variance calibration (LVC) method. Based on this model and the daily operational forecast data of the China Meteorological Administration (CMA) global EPS (CMA-GEPS) in December 2022–November 2023, characteristics of the CMA-GEPS forecast uncertainty are diagnosed and analyzed, and compared against the state-of-the-art operational global EPS of ECMWF. Generally, there is a deficiency in CMA-GEPS, which underestimates the forecast uncertainty, especially in the tropics. However, at certain initialization times in some seasons and over some locations, the spread appears greater than the RMSE, indicating an overestimation of forecast uncertainty. Moreover, CMA-GEPS performs better in capturing the forecast uncertainty of lower-level variables than upper-level variables; and in comparison with the mass and thermal fields, the forecast uncertainty of the dynamic field is better represented. Diagnostic analysis using the LVC method reveals that the relevance between the ensemble variance and the ensemble mean error variance of CMA-GEPS increases with forecast lead time, and the problem of underestimated forecast uncertainty is continuously alleviated. In addition, ECMWF EPS behaves distinctly better than CMA-GEPS in representing the forecast uncertainty and its growth process, the reasons for which are discussed and elucidated from the perspective of shortcomings in the methods to generate the initial and model perturbations, the ensemble size, and the forecast model adopted by CMA-GEPS.