Abstract Geostationary hyperspectral infrared sounder observations provide two distinctive advantages: high temporal resolution and fixed observation geometry. However, conventional application frameworks have tended to focus on the quality of individual observations at a single time step and a single footprint without considering the temporal and spatial connections, thereby not able to fully exploit these unique strengths. Based on FY‐4A/GIIRS observations, this study establishes a dynamic error characterization framework that employs multidimensional grouping across temporal, spatial, and detector arrays to statistically model the varying observation errors. Built upon this framework, key parameters for quantitative applications, radiance bias correction coefficients, observation error covariance matrices, and channel selection indexes, are derived dynamically. These parameters, which remain static in conventional approaches, are made dynamically adaptive to temporal and spatial variations within this framework. This geostationary hyperspectral sounder's methodology effectively utilizes 98% of the spectral channels, achieving a 0.14 K reduction in boundary layer temperature error and a 4.26% cut in overall humidity error.
Huang et al. (Tue,) studied this question.