Improving Convection-Allowing Ensemble Forecasts via Multi-Source Remote Sensing Data Assimilation Through Stepwise Cloud Analysis Initialization: A Remote Sensing Case Study

The “spin-up” problem, in which convection-permitting models require hours to develop realistic clouds from large-scale initial fields, critically limits short-term severe weather forecasting. Cloud analysis can serve as a feasible approach to directly assimilate hydrometeor information from remote sensing retrievals. In this study, we leverage multi-source remote sensing data, including three-dimensional mosaic radar reflectivity, hourly averaged FY-2G satellite brightness temperature (black-body temperature, TBB), and FY-2G total cloud water products, within a stepwise cloud analysis initialization scheme. The scheme is implemented in a convective-scale ensemble forecasting system (CMA-Meso, 3 km resolution) for a heavy rainfall event. For each ensemble member, three-dimensional hydrometeor increments are independently generated from these remote sensing retrievals and gradually introduced over the first ten time steps, ensuring smooth coordination with the model’s dynamic thermal framework. Quantitatively, the scheme reduces near-surface Continuous Rank Probability Score (CRPS) errors, improves the overall predictive skill by 2.6–7.9% (maximum at the 12 h spin-up period), and increases ensemble spread by 2–5.8%, mitigating under-dispersion. Probabilistic precipitation forecasts show uniform area under the relative operating characteristic curve (AROC) improvements across all thresholds, 1.16–5.77% for light rain, 3.03–8.97% for moderate rain, and 6.00–12.07% for heavy rain, with these maxima consistently occurring at the 12 h spin-up time. Although Brier scores are marginally larger, these AROC gains confirm the enhanced discrimination of convective rainfall. At 500 hPa, CRPS reductions of 7.1–15.6% emerge after 24 h (largest 15.6% for geopotential height at 24 h), zonal wind CRPS is reduced by 2.2% at 12 h, and ensemble spread increases by 3.1–7.0% for all three variables. These improvements, particularly the pronounced benefits during the initial 12 h, demonstrate that the remote sensing-driven cloud analysis effectively shortens spin-up. Mechanistically, the gains arise from physically coordinated hydrometeor-latent heat perturbations and subsequent cloud radiation feedback that continuously regulate thermal-dynamic structures. This study establishes that assimilating diverse remote sensing data via cloud analysis is an effective approach for overcoming spin-up challenges in convective-scale ensembles.