星载红外高光谱资料在数值预报业务中具有关键作用。鉴于红外光谱辐射易受云层强烈干扰，星载红外高光谱通道的晴空判识已成为资料同化中不可或缺的核心技术环节。以国外HIRS、AIRS、IASI和CrIS等探测器数据以及我国HIRAS和GIIRS资料为研究对象，系统梳理了星载红外大气探测资料应用中晴空像元/通道判识方法的发展脉络，归纳总结了多类晴空判识技术，主要包括：基于单一光谱特征与跨光谱特征的晴空通道判识、基于主成分分析与机器学习的晴空像元判识，以及我国风云卫星红外高光谱资料同化过程中自主研发的晴空通道判识方法。研究表明，三维晴空判识方法通过有效同化云顶以上的红外高光谱晴空资料，已成为提升资料同化效能的关键技术；在高光谱探测器像元尺度上，基于跨光谱资料反演云参数的匹配判识方法，相较于高光谱资料自身三维晴空判识，在多相态云环境中表现出更高的判识精度；此外，该方法还为红外高光谱资料的机器学习晴空像元判识和全天空同化提供了足够精确的先验样本。结合当前研究进展与业务应用现状，针对全天空、全光谱星载红外高光谱资料同化中晴空判识技术面临的挑战，提出以跨光谱匹配算法构建的晴空通道判识样本为基础，进一步发展融合机器学习的三维晴空通道判识方法，这将成为星载红外高光谱资料同化技术的重要发展方向，并为逐步增强其在数值预报中的应用价值提供更有力的技术支撑。

Spaceborne infrared hyperspectral data have become a cornerstone of modern Numerical Weather Prediction (NWP) systems, enabling high-resolution atmospheric profiling and improved forecast accuracy. However, the utility of these data is significantly constrained by cloud interference, as infrared spectral radiation is strongly attenuated or scattered by cloud particles. Consequently, clear-sky identification—specifically, the discrimination of cloud-free pixels and channels—has become an essential preprocessing step in data assimilation, ensuring that only reliable observations are integrated into NWP models. This review provides a systematic overview of the evolution of clear-sky identification methods for spaceborne infrared atmospheric sounding data, covering both foreign and domestic sensor systems. It critically evaluates techniques applied to datasets from prominent foreign missions, such as the High-Resolution Infrared Sounder (HIRS), Atmospheric Infrared Sounder (AIRS), Infrared Atmospheric Sounding Interferometer (IASI), and Cross-track Infrared Sounder (CrIS), alongside domestic advancements using the Hyperspectral Infrared Radiation Sounder (HIRAS) and the Global Infrared Imager and Interferometer Sounder (GIIRS) aboard China’s Fengyun satellites. The methodologies are categorized into three distinct technological frameworks: Spectral Feature-Based Approaches:①Early techniques rely on single-spectral thresholding, flagging channels as clear-sky based on predefined radiance thresholds sensitive to cloud absorption or emission. More advanced variants employ cross-spectral consistency checks, leveraging the spectral dependence of cloud properties across multiple wavelength bands to enhance discrimination accuracy. For example, IASI’s cloud-clearing algorithm combines shortwave and longwave infrared channels to identify consistent clear-sky signatures.②Data-Driven and Machine Learning Techniques: Principal Component Analysis (PCA) has been widely applied to reduce the dimensionality of hyperspectral datasets, enabling the extraction of latent variables that distinguish clear-sky from cloudy conditions. More recently, machine learning models—including random forests, support vector machines, and deep neural networks—have demonstrated superior performance in pixel-level clear-sky classification. These models learn complex nonlinear relationships between spectral features and cloud states, achieving higher precision in heterogeneous cloud environments. For instance, AIRS has adopted neural networks to improve clear-sky identification in regions with thin cirrus clouds.③Domestic Innovations in Assimilation Systems: China’s Fengyun satellite program has developed specialized clear-sky identification schemes tailored to the HIRAS and GIIRS instruments. These methods integrate physical constraints from radiative transfer models with statistical learning, optimizing clear-sky channel selection for regional NWP models over the Tibetan Plateau and monsoon-affected areas. Such innovations have significantly enhanced the utilization of domestic hyperspectral data in operational assimilation systems. The review highlights two transformative technologies:①Three-Dimensional (3D) Clear-Sky Identification. By incorporating vertical atmospheric structure from NWP model forecasts, 3D methods enable the assimilation of clear-sky data above cloud tops, extending the utility of hyperspectral observations in partially cloudy conditions. This approach has been shown to improve upper-tropospheric humidity analysis in Arctic NWP systems.②Cross-Spectral Matching with Cloud Parameter Inversion: At the pixel scale, matching hyperspectral observations with cloud properties derived from complementary sensors (e.g., microwave radiometers or visible imagers) has proven particularly effective in multi-phase cloud environments. Compared to standalone 3D methods, this hybrid approach achieves a 15%~20% improvement in clear-sky identification accuracy over ice-water mixed clouds, as demonstrated in CrIS data applications. The review identifies key challenges in all-sky and full-spectrum assimilation, including the handling of sub-pixel cloud heterogeneity, spectral bias in multi-sensor datasets, and computational scalability for real-time operations. To address these, a novel framework is proposed: a machine learning-enhanced 3D clear-sky identification model, trained on cross-spectral matching datasets. By fusing physical radiative transfer principles with data-driven learning, this approach promises to unlock the full potential of spaceborne infrared hyperspectral data, offering robust technical support for next-generation NWP systems and advancing global weather forecasting capabilities.