Regional hybrid three‐dimensional variational assimilation and forecast experiments based on multiscale ensemble members

Abstract Multiscale ensemble members can more accurately characterize the dynamic background error covariance structures in hybrid three‐dimensional variational (Hybrid‐3DVAR) assimilations, contributing to improving the analysis accuracy of meso‐ and small‐scale systems. On the basis of multiscale ensemble members from the China Meteorological Administration Global Ensemble Prediction System (CMA‐GEPS) and Regional Ensemble Prediction System (CMA‐REPS), this study analyses multiscale spread characteristics and integrates them to construct a dynamic background error covariance suitable for Hybrid‐3DVAR analysis, aiming to capture the flow dependence of background error covariance in assimilation analysis. Furthermore, a multiscale Hybrid‐3DVAR assimilation scheme is designed via the CMA mesoscale prediction system (CMA‐MESO V6.0), and four groups of analysis‐forecast experiments are conducted for the heavy rainfall events in North China, including 3DVAR, single‐scale Hybrid‐3DVAR, and sequential multiscale Hybrid‐3DVAR. The key findings include the following: (1) The 12‐h forecast ensemble members from the CMA‐GEPS and CMA‐REPS exhibit similar spread structures, both of which effectively capture the flow‐dependent characteristics of weather system errors. However, global ensemble errors demonstrate smoother spatial distributions, whereas regional members show larger spread with finer error structures in areas of intense convective development. (2) Horizontal and vertical correlation coefficients calculated from global ensemble members exhibit smoother patterns and larger correlation scales, whereas those from regional ensemble members are more localized with smaller correlation scales and contain more spurious noise in long‐distance correlations than global ensemble members. (3) The sequential multiscale Hybrid‐3DVAR assimilation experiment exhibits the smallest analysis and forecast biases and root mean squared error (RMSE) in the wind, humidity, and temperature fields. It also shows higher Equitable Threat Scores (ETS) for rainfall forecasts across all intensity levels, along with a notable reduction in false alarms for rainstorms with greater magnitudes.