Abstract. This study introduces FYAI, a global, long-term atmospheric ice water path (IWP) and suspended ice water path (SIWP) dataset spanning 2010–2024, derived from passive microwave observations (MWHS-I/II) onboard China's Fengyun-3 series satellites. The dataset is generated using a machine learning framework featuring a lightweight multilayer perceptron architecture enhanced with gated residual units. This design robustly handles the inherent uncertainties in satellite brightness temperatures and the spatial mismatch between passive microwave footprints and active radar/lidar training data. By establishing rigorous spatiotemporal collocation with CloudSat 2C-ICE products, FYAI provides two operational product levels adhering to standard Earth observation data processing definitions: (1) Level-2 (L2) products, offering instantaneous orbital-resolution IWP and SIWP at a nominal 15 km nadir resolution for 2010–2024; and (2) Level-3 (L3) products, comprising monthly global gridded composites at 1°×1° resolution (2010–2024). FYAI bridges the gap between instantaneous pixel-level precision and broad spatiotemporal coverage, offering a comprehensive, decadal-scale record of global atmospheric ice content. This dataset, specifically designed to support long-term climate analysis and model validation, is openly available in netCDF4 format for community use (https://doi.org/10.11888/Atmos.tpdc.303143, Yang et al., 2025)
Yang et al. (Thu,) studied this question.