Cloud contamination is a critical source of errors in the data assimilation of hyperspectral infrared radiance (IR). Therefore, it is necessary to filter out cloudy observations. In this study, we review and summarize the principles and research progress of cloud detection methods for the hyperspectral IR in the past two decades. Based on the impact of IR data utilization on cloud detection results, cloud detection methods are categorized into five types, namely clear field-of-view (FOV) detection, clear channel detection, three-dimensional cloud detection, cloud-clearing and deep learning methods. Clear FOV methods and clear channel methods aim to identify the purely clear FOVs and spectral channels that are not affected by clouds, respectively. Cloud-clearing methods are used to reconstruct clear-column radiance for cloudy observations. Deep learning cloud detection methods can quickly learn the mapping relationship between infrared hyperspectral radiation characteristics and FOV cloud distribution from a large amount of infrared radiative information with known FOV cloud labels. In this paper, we discuss and provide an outlook on the key issues in current hyperspectral IR cloud detection. Specifically, we analyze and summarize the factors affecting cloud detection, such as surface background information, vertical cloud distribution, hyperspectral IR channel selection, improvements in cloud detection algorithms and model applicability. The results indicate the use of deep learning methods offer advantages in detection accuracy and algorithm efficiency of hyperspectral IR cloud detection.

A gale occurred in Beijing on 30 May 2024, which led to fallen trees and damaged infrastructure. This event was primarily driven by surface divergent winds induced by strong convective downdrafts. During the occurrence and development of this gale, solar shortwave radiation and cloud-related variables played a crucial role in triggering, sustaining, and organizing convection. This study proposes a new diagnostic analysis approach for this gale focusing on shortwave radiation and cloud-related variables involved in the physical processes of gale development, based on the FY-4B L2 products and simulations from the Mesoscale Weather Numerical Forecast System of the China Meteorological Administration (CMA-MESO). The diagnostic analysis results of this case show that before cloud formation, the CMA-MESO simulates stronger shortwave radiation heating in the initial stages, leading to an overestimation of surface temperature rise. Additionally, the simulated cloud formation occurs slightly later than observed, with reduced cloud coverage, shorter cloud duration, and lower cloud top heights, resulting in a weaker convective intensity compared to observations. Furthermore, the CMA-MESO underestimates the temperature gradient between the middle and lower troposphere and predicts lower convective instability, which leads to weaker forecasts of convection organization. Ultimately, this study provides a theoretical basis and technical support for enhancing the ability of the CMA-MESO to simulate this gale by using the FY-4B L2 data products for diagnostic analysis.