Development of a Real-Time Analysis Product for Multi-Source Fusion of Snowacross China

Accurate and timely monitoring of spatiotemporal variations in snow is crucial for numerical weather prediction, climate change studies, and disaster weather forecasting. This study leverages multiple datasets, including thethe China Meteorological Administration (CMA) land surface data assimilation system(CLDAS-V2.0)atmospheric forcing data, Fengyun-4 snow cover data, in-situ snow depth observations, and land surface parameters. By employing snow simulation via CLM, Noah, and Noah-MP land surface models, the "EnSRF+DI" snow assimilation technique, TC covariance-based multi-model integration, and a multi-grid variational analysis method accounting for elevation, a multi-source merged snow analysis product has been developed for China at a spatial resolution of 6.25 km/hourly. Evaluation using in-situ observations demonstrates that the quality of this product is generally superior to international counterparts such as GLDAS and ERA5_Land snow depth. Compared to snow depth simulations from land surface models alone, the assimilation of Fengyun-4 satellite snow cover data reduces the root mean square error of snow depth estimates by 10%. The product is provided in NetCDF format, with temporal coverage starting from November 2021 in near real-time, and an approximate data volume of 45 MB per hour. It includes key variables such as snow depth, snow cover fraction, and snow depth change. This product has been accredited as a high-value meteorological dataset by the CMA and has been operationally applied in weather briefings, disaster weather monitoring, and snowmelt flood risk analysis.