Development of a Four-Dimensional Diagnostic Analysis Model for Assessing Ensemble Forecast Uncertainty

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 EPS 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.