Retrieving the Atmospheric Water Vapor Profile Combining FY-4A/GIIRS and Ground-Based GNSS PWV in Hong Kong Region

Rapid and accurate retrieval of vertical water vapor distributions is important for numerical weather forecasting, climate research, and disaster management. Traditional methods, such as radiosondes and microwave radiometers, often suffer from limited spatial and temporal resolution, high operational costs, and delayed data availability, leading to variable accuracy. The FY-4A meteorological satellite has introduced the Infrared Hyperspectral Atmospheric Vertical Sounder sensor to geostationary orbit for the first time, which offers significant advantages in terms of observational efficiency and cost compared to ground-based GNSS. This study employs a 1-D convolutional neural network (1D-CNN) to reconstruct specific humidity at 21 pressure levels, ranging from 1000 to 100 hPa. The 1D-CNN integrates two data sources: radiative data from 560 water vapor channels on FY-4A/geostationary interferometric infrared sounder (GIIRS) and ground-based GNSS-derived precipitable water vapor (GNSS-PWV) data. The combined use of FY-4A/GIIRS and GNSS-PWV data significantly improves the accuracy of water vapor vertical profile retrievals compared to radiosonde data from Hong Kong. Under clear-sky conditions, this combination achieves a root mean square error (RMSE) reduction of 25.64% overall, with a 30.68% reduction at heights from the surface to 600 hPa, as well as an average bias (BIAS) reduction of 32.98%, compared to retrievals using FY-4A/GIIRS data alone. Under cloudy conditions, the results showed an average reduction of 23.66% in RMSE and 50% in BIAS. The results demonstrate that integrating ground-based GNSS-derived PWV data can effectively increase the accuracy of FY-4A/GIIRS water vapor vertical profile retrieval.