To evaluate the validity of cloud top height (CTH) retrievals from FY-4A, the first of China’s next-generation geostationary meteorological satellite series, the retrievals are compared to those from Himawari-8, CloudSat, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Moderate Resolution Imaging Spectroradiometer (MODIS) operational products from August to October 2017. Regarding CTHs from CloudSat, CALIPSO, and MODIS as truth, the results show that the performance of FY-4A CTH retrievals is similar to that of Himawari-8. Both FY-4A and Himawari-8 retrieve reasonable CTH values for single-layer clouds, but perform poorly for multi-layer clouds. The mean bias error (MBE) shows that the mean value of FY-4A CTH retrievals is smaller than that of Himawari-8 for single-layer clouds but larger for multi-layer clouds. For ice crystal clouds, both FY-4A and Himawari-8 obtain the underestimated CTHs. However, there is a tendency for FY-4A and Himawari-8 to overestimate the CTH values of CloudSat and CALIPSO mainly for low level liquid water clouds. The temperature inversion near the tops of water clouds may result in an overestimation of CTHs. According to the MBE change with altitude, FY-4A and Himawari-8 overestimate the CTHs mainly for clouds below 3 km, and the overestimation is slightly more apparent in Himawari-8 data than that in FY-4A values. As the cloud optical thickness (COT) increases, the CTH bias of FY-4A CTH retrievals gradually decreases. Two typical cases are analyzed to illustrate the differences between different satellites’ CTH retrievals in detail.

Under the assumption that clouds are homogeneous and single-layered (SL), most current operational cloud top height (CTH) products derived from passive radiometers may largely underestimate the CTH of overlapping clouds. This article proposes a statistics-based extrapolation algorithm for retrieving the CTHs of overlapping clouds using only existing cloud property products available for most operational radiometers, and the method is successfully employed for the advanced himawari imager (AHI) observations. Because regional clouds within the same “system” have relatively continuous geometric properties, especially CTH, due to similar atmospheric conditions, upper-layer ice cloud CTHs (ITHs) and lower-layer water cloud CTHs (WTHs) are inferred using the CTH retrievals of well-chosen neighboring SL ice and water clouds, respectively. The proposed algorithm uses the latest machine-learning-based model to reasonably distinguish overlapping clouds from SL clouds, and optimizes the extrapolation by considering three physical constraints on neighboring, cloud phase, and cloud optical thickness (COT). Validated using active observations from CloudSat and cloud-aerosol Lidar and infrared pathfinder satellite observation (CALIPSO), our algorithm improves the AHI CTH mean bias for overlapping clouds from −5.1 to −2.6 km. More importantly, the algorithm provides CTH information of underlying water clouds that are unavailable from existing radiometer-based products. With the simultaneous retrieval of ITH and WTH, this algorithm increases our capability to detect the vertical structures of overlapping clouds and better evaluate the cloud radiative effects (CREs).

In recent years, there has been a preliminary research on monitoring rainfall information based on polarimetric Global Navigation Satellite System (GNSS) signals, which is a quite novel concept. After previous theoretical research on monitoring rain based on polarimetric phase shift of GNSS signals, the paper aims to detect rain using polarimetric GNSS signals from a ground-based experiment. Firstly, a conical horn antenna specially designed for receiving dual-polarized (H, horizontal, and V, vertical) GNSS signals was developed, and an experimental system for polarimetric GNSS rain detection was built. Then, taking Global Positioning System (GPS) satellites as signal source, a ground-based experiment was carried out at a mountain in Nanjing, where heavy rain tends to occur frequently in rainy season. Additionally, a data processing algorithm mainly following Padullés et al. (2016) to solve the problems of quality control, unlocking, hardware effect, phase ambiguity, multipath effect was applied independently to this ground-based data from the polarimetric GNSS rain detection system. Also, the multi-source data from nearby weather radar and weather stations was used for verification. Results from 14 GPS satellites show that the obtained phase shift is zero in all no-rain days while it is not zero during rainy days, which is in accordance with the actual situation. Compared with weather radar and rain gauges’ data, the results verify that the phase shift is caused by rain. Besides, when individual cases are examined, many show that their tendencies of accumulated phase shift are quite similar to that of a weather station’s rainfall data, even some correlation coefficients are up to 0.99. These demonstrate the reliability of our experimental system and the feasibility of the data processing algorithm. This study will provide technical support for future spaceborne experiment, which has promising applications in global rain monitoring.

Cloud is complicated due to its various types and distribution on different layers. Various classification principles and overlapping of clouds on multiple layers make it difficult to classify clouds correctly. A cloud classification method based on random forest (RF) for FengYun-4A (FY-4A) is presented in this paper, aiming at obtaining cloud classification products with high temporal resolution and extensive coverage. The method classifies clouds into multi-layer clouds and 8 categories of single-layer clouds (Deep convective cloud, Nimbostratus, Cumulus, Stratocumulus, Stratus, Altocumulus, Altostratus and Cirrus). Additionally, multi-layer clouds are further classified into 12 categories of two-layer clouds (combinations of 8 single-layer clouds). CloudSat cloud classification products are used as the target class and to evaluate the method. In order to classify clouds correctly, it is necessary to capture cloud properties comprehensively. Thus we perform comparative pre-experiments to analyse the effects of FY-4A cloud products on cloud classification. It is demonstrated that clouds products can improve cloud classification. Therefore, cloud optical thickness (COT), cloud effective radius (CER) and cloud top height (CTH) are used as dataset together with multispectral data in cloud classification. Cloud classification models based on different algorithms and different channels combinations are compared. The results show that RF models perform better than K-Nearest Neighbour (KNN) models and Back Propagation Neural Network (BPNN) models in cloud classification, and models using all channels’ data perform better than models using data of selected channels combination. The method can provide cloud types and distribution for a FY-4A scan full disk in low time cost. Especially, it gives more specific types for multi-layer clouds, which can provide reference for subsequent research on cloud classification and FY-4A satellite.

Cloud base height (CBH) is crucial for parameterizing the cloud vertical structure (CVS), but knowledge concerning the temporal and spatial distribution of CBH is still poor owing to the lack of large-scale and continuous CBH observations. Taking advantage of high temporal and spatial resolution observations from the Advanced Himawari Imager (AHI) on board the geostationary Himawari-8 satellite, this study investigated the climatology of CBH by applying a novel CBH retrieval algorithm to AHI observations. We first evaluated the accuracy of the AHI-derived CBH retrievals using the active measurements of CVS from the CloudSat and CALIPSO satellites, and the results indicated that our CBH retrievals for single-layer clouds perform well, with a mean bias of 0.3 ± 1.9 km. Therefore, the CBH climatology was compiled based on AHI-derived CBH retrievals for single-layer clouds for the time period between September 2015 and August 2018. Overall, the distribution of CBH is tightly associated with cloud phase, cloud type, and cloud top height and also exhibits significant geographical distribution and temporal variation. Clouds at low latitudes are generally higher than those at middle and high latitudes, with CBHs peaking in summer and lowest in winter. In addition, the surface type affects the distribution of CBH. The proportion of low clouds over the ocean is larger than that over the land, while high cloud occurs most frequently over the coastal area. Due to periodic changes in environmental conditions, cloud types also undergo significant diurnal changes, resulting in periodic changes in the vertical structure of clouds.

Satellite observations of artificial light sources at night are increasing. Clouds, as the most important radiative modulators in the Earth‐atmosphere system, significantly influence the observed values and spatial distribution of the artificial light coming from the ground. However, the mechanisms by which clouds affect the radiative transmission of ground‐based light sources remain poorly understood. Consequently, many quantitative remote sensing applications at night are restricted to cloud‐free conditions. Conversely, ground‐based light can cause anomalous radiation distributions in clouds, hindering studies on their characteristics. In this study, we employed Monte Carlo radiative transfer simulations to investigate nighttime light radiation through complex three‐dimensional cloud structures. The simulations incorporated clouds modeled using large eddy simulation (LES) and custom cloud configurations with varying radii, thickness, base heights, and relative positions between clouds and lighting to elucidate the underlying physical mechanisms. The results indicate that cloud layers exhibit dual effects on radiance distribution: They reduce nadir radiance through absorption and scattering while amplifying surrounding brightness via scattering. Additionally, increasing cloud thickness attenuates overhead radiance, whereas it enhances pronounced peaks at cloud boundaries. Low clouds intercept and modulate upward radiance more effectively than high clouds due to geometric constraints. Source displacement breaks radiance symmetry, enhancing cloud edge brightness. Multiple scattering within clouds redistributes ground‐based light signals, expanding compact point sources into extended radiance patterns observable by satellites.