An Optimization Approach for Specific Humidity Profiles Derived from FY-4B GIIRS

Specific humidity profile retrievals from the Geostationary Interferometric Infrared Sounder (GIIRS) onboard Fengyun-4B (FY-4B) are often degraded by cloud contamination, while strict quality control flags further limit data usability. To address these issues, an optimization framework for FY-4B GIIRS specific humidity profiles was developed using ERA5 reanalysis data spanning January 2023 to January 2024. Guangxi and the Beibu Gulf were selected as the study domain. Independent ERA5 datasets, not involved in model construction, were used as a benchmark to evaluate performance. The results indicate that the original FY-4B GIIRS specific humidity profiles tend to underestimate moisture relative to ERA5. For profiles with quality flags of 0 and 1, the Bias, Root Mean Square Error (RMSE), and Mean Relative Error (MRE) range of −3~0 g/kg, 0~4 g/kg, and 17~53%, respectively. After optimization, the bias is effectively reduced to 0 g/kg. RMSE shows an average reduction of 15% within the 700~300 hPa layer, while the most notable improvement in MRE occurs between 1000 and 920 hPa. For lower-quality data (Flags 2–3), the bias, RMSE, and MRE span −12~0 g/kg, 0~13 g/kg, and 48~140%, respectively. Following optimization, the bias range narrows to −6~0 g/kg. RMSE decreases by 20~40% from the near-surface layer up to 400 hPa, and MRE is reduced by 40% below 300 hPa. A case study of Typhoon “Peipah” further demonstrates the model’s effectiveness. At stations experiencing intense rainfall, the optimized specific humidity profiles show markedly improved accuracy, and the Mean Absolute Errors (MAEs) of derived forecast-related physical variables are substantially reduced. Overall, the proposed optimization model significantly enhances both the accuracy and practical usability of FY-4B GIIRS specific humidity profiles, providing more reliable data support for monitoring severe weather events such as typhoons and heavy rainfall.