Research on Short-term Model Ensemble Precipitation Forecasting Correction Technology Based on Hierarchical Cross-level Weighted Scoring

Based on the 1-hourly and 3-hourly updated precipitation products within 24 hours from three models (CMA-GD-R3, CMA-SH3, and CMA-MESO, hereinafter referred to as the original models) and the precipitation observations from regional automatic weather stations in Hunan Province, this study develops a novel verification metric named Cross-magnitude Weight (CMW) on the basis of traditional precipitation verification methods. A short-term ensemble correction and forecasting model (STDA-CMW) is designed, which incorporates time-lagging and spatial shifting techniques optimized by CMW, to achieve more effective hourly-updated quantitative precipitation forecasts for the next 12 hours. The proposed model is compared with the three original models and three sets of comparative experiments designed to validate the effectiveness of each module of STDA-CMW (namely, the scheme without the spatial shifting module, the scheme without the heavy precipitation fusion module, and the scheme replacing CMW optimization with the traditional TS score optimization), in order to examine the independent contributions of each module. The results are as follows. (1) For general precipitation 0.1, 20) mm·h⁻¹, the STDA-CMW model achieves a TS score (0.325) superior to all original models and comparative models. All three enhancement modules (spatial shifting module, CMW optimization module, and heavy precipitation fusion module) make positive contributions, among which the CMW optimization module contributes the most, significantly outperforming the traditional TS score optimization, while the contributions of the spatial shifting module and the heavy precipitation fusion module are comparable. (2) For heavy precipitation (≥20 mm·h⁻¹), the STDA-CMW model outperforms the original models and comparative models in terms of TS score (0.011), false alarm rate (0.971), mean absolute error (25.141), and CMW (-0.004). All three modules make positive contributions, but the forecast performance can surpass that of all original models only when the three modules are integrated simultaneously. (3) The contributions of the three modules have distinct focuses. The spatial shifting module primarily improves the hit rate of heavy precipitation; the heavy precipitation correction module enhances both the hit rate and reduces the false alarm rate; the CMW optimization module mainly reduces the false alarm rate of heavy precipitation and makes a significantly greater contribution to the magnitude of heavy precipitation than the other two modules. The above findings provide a feasible pathway for short-term precipitation correction based on rapidly updated mesoscale numerical models, demonstrating relative advantages in reducing false alarm rates and mean absolute error—key operational early warning metrics. The three enhancement modules contribute differently to short-term precipitation forecasting, and optimal heavy precipitation forecasts can only be achieved when the three modules are integrated synergistically.