Data assimilation (DA) of satellite visible radiance observations holds great potential to improve the forecasting skills of numerical weather prediction (NWP) models, yet several challenges remain. This review synthesizes recent progress in visible radiance DA and critically assesses its impacts on the analyses and forecasts of cloud and precipitation. As two major components of DA, observation operators [e.g., the Community Radiative Transfer Model (CRTM) and the Radiative Transfer for TOVS (RTTOV)] and DA methods (e.g., variational and ensemble-based approaches) have both received significant research efforts. On one hand, improvements in observation operators, particularly in radiative transfer solvers and cloud optical parameterizations, have enhanced both computational accuracy and efficiency for Top-of-Atmosphere (TOA) visible radiance simulations. However, there are still challenges in generating representative visible images due to uncertainties in cloud optical parameterizations and simplifications of three-dimensional (3D) radiative effects. On the other hand, variational DA methods are constrained by the quasi-static assumption in background error covariances, whereas ensemble Kalman filter (EnKF)-based methods offer greater flexibility at the current stage. Nevertheless, both methods are limited by the nonlinear and non-Gaussian nature of moist physics processes. EnKF-based methods also suffer from limitations in vertical localization. Although particle filters are theoretically well suited to nonlinear and non-Gaussian problems, the strong nonlinearity in observation operators severely limits the representativeness of resampled particles. Future efforts should focus on incorporating 3D radiative effects, refining cloud optical parameterizations, designing DA methods specifically for nonlinear and non-Gaussian problems in visible radiance assimilation, and designing adaptive vertical localization strategies. Addressing these challenges will facilitate more effective application of visible radiance DA in cloud and precipitation forecasting.

Cloud effective radius (CER) is a fundamental microphysical property of clouds, critical for understanding cloud formation and radiative effects. Satellite spectral imagers, widely utilized in passive remote sensing, facilitate the monitoring of cloud characteristics, including CER, over extended time periods and spatial scales. Various observational methods have been employed to evaluate satellite cloud property products; however, in situ measurement evaluations of CER remain limited, particularly for products over China. This study utilized aircraft observations provided by the China Meteorological Administration Weather Modification Centre to evaluate the CER retrieved by Fengyun-4A Advanced Geosynchronous Radiation Imager (AGRI) and Himawari-8 Advanced Himawari Imager (AHI). Three flights were selected from the aircraft dataset for evaluation, involving flights through non-precipitating stratiform clouds with stable, high-quality measurements. Rigorous data selection and collocation procedures were employed to ensure a comprehensive comparison. Satellite retrievals from heterogeneous cloud fields were excluded, and representative in-cloud aircraft measurements were identified through multi-parameter filtering. The flight trajectory was adjusted to account for horizontal cloud movement corresponding to time differences between observations from different platforms. Additionally, in situ measurements from different vertical layers were adjusted to a comparable position near the cloud top. Results indicate that CER retrieved from satellites is generally overestimated compared to in situ measurements. For AGRI, the average difference (AD) is 2.90 µm, with a root mean square difference (RMSD) of 3.53 µm. For AHI, the AD is 2.92 µm, and the RMSD is 3.59 µm. To enhance future validation and evaluation of remote sensing results, factors such as instrument calibration, flight patterns, and cloud conditions will be carefully considered. Increasing the number of cases should further reduce errors associated with individual instances, enabling more precise assessments.

The subject of “atmospheric radiation” includes not only fundamental theories on atmospheric gaseous absorption and the scattering and radiative transfer of particles (molecules, cloud, and aerosols), but also their applications in weather, climate, and atmospheric remote sensing, and is an essential part of the atmospheric sciences. This review includes two parts (Part I and Part II); following the first part on gaseous absorption and particle scattering, this part (Part II) reports the progress that has been made in radiative transfer theories, models, and their common applications, focusing particularly on the contributions from Chinese researchers. The recent achievements on radiative transfer models and methods developed for weather and climate studies and for atmospheric remote sensing are firstly reviewed. Then, the associated applications, such as surface radiation estimation, satellite remote sensing algorithms, radiative parameterization for climate models, and radiative-forcing related climate change studies are summarized, which further reveals the importance of radiative transfer theories and models.

Atmospheric radiation is a major branch of atmospheric physics that encompasses the fundamental theories of atmospheric absorption, particle scattering (aerosols and clouds), and radiative transfer. Specifically, the simulations of atmospheric gaseous absorption and scattering properties of particles are the essential components of atmospheric radiative transfer models. Atmospheric radiation has important applications in weather, climate, data assimilation, remote sensing, and atmospheric detection studies. In Part I, a comprehensive review of the progress in the field of gas absorption and particle scattering research over the past 30 years with a particular emphasis on the contributions from Chinese scientists is presented. The review of gas absorption includes the construction of absorption databases, the impact of different atmospheric absorption algorithms on radiative calculations, and their applications in weather and climate models and remote sensing. The review on particle scattering starts with the theoretical and computational methods and subsequently explores the optical modeling of aerosols and clouds in remote sensing and atmospheric models. Additionally, the paper discusses potential future research directions in this field.