Fengyun-4 (FY-4) is a series of Chinese operational geostationary meteorological satellites, providing crucial data for weather forecasting, climate prediction, and environmental monitoring. Advanced Geostationary Radiation Imagers (AGRI) onboard the FY-4A and FY-4B satellites play a key role in observing the Earth’s surface, oceans, and atmosphere. However, their calibration stability is still uncertain, which clearly limits corresponding downstream applications. This study evaluates the long-term radiometric stability of AGRI reflective solar bands (RSBs) using a general cloud target (CT) calibration method, covering the periods from March 2018 to December 2024 for FY-4A/AGRI and from June 2022 to December 2024 for FY-4B/AGRI. By utilizing MODIS cloud products as references for cloud properties, we simulate the top-of-atmosphere (TOA) reflectances of CTs through the Discrete Ordinates Radiative Transfer (DISORT) model and compare results with observed reflectances to infer the instrumental calibration stability. Our results indicate that the radiometric responses of AGRI exhibit significant degradation in visible bands, while showing relatively smaller degradation rates in near- and shortwave-infrared bands. Specifically, the annual degradation rates for band 1 (0.47 μm) of FY-4A/AGRI and FY-4B/AGRI are 4.3% and 8.8% respectively. Both AGRIs demonstrate comparable degradation rates of approximately 3.5% for band 2 (0.65 μm). In contrast, bands 3 (0.83 μm), 5 (1.61 μm), and 6 (2.25 μm) show annual degradation rates around −1.0%, despite they exhibit notable fluctuations. The operational calibration of FY-4B/AGRI is more accurate than that of FY-4A/AGRI and with smaller fluctuations. By fitting the time series of relative errors between simulated and current calibrated reflectances, we calculate daily recalibration coefficients and effectively recalibrated the long-term data, with a calibration accuracy within ±3%. This study demonstrates that the CT-based calibration method can successfully track the radiometric stability of AGRI and provide a robust calibration solution to ensure data stability and accuracy.

Cloud liquid water path (LWP) quantifies liquid water amount within the atmosphere and is closely related to water cycle, weather, and climate. Passive microwave (MW) observations are powerful tools for retrieving LWP. An empirical relationship between the LWPs and MW brightness temperatures (BTs) can be obtained for conventional retrievals, which consider only the influence of LWP on BTs. However, besides LWP, the cloud vertical extent [e.g., cloud top height (CTH)] can affect MW emission, absorption, and corresponding channel BTs, but it is ignored in conventional retrievals. This study investigates the influences of CTH on MW LWP retrievals, and a CTH-dependent algorithm is developed using CTHs from infrared retrievals. Synthetic radiative transfer simulations are performed to quantify CTH effects on MW channel BTs and to establish the CTH-dependent retrieval coefficients. We use the Advanced MW Scanning Radiometer 2 (AMSR2) observations. Cloud products from Moderate Resolution Imaging Spectroradiometer (MODIS) are collocated to provide the necessary CTH information. Thus, we develop an LWP retrieval algorithm by combining AMSR2 BTs with MODIS CTHs. The results indicate that incorporating CTH information into LWP retrievals enhances the consistency between MW and visible/infrared retrievals. Specifically, the CTH-dependent algorithm showed an improvement in the intraclass correlation coefficient (ICC) and a reduction in mean relative differences (MRDs) by approximately 4% (from 18% to 14%) compared to AMSR2 operational retrievals. The CTH-dependent results are slightly more consistent with the MODIS results than the CTH-independent ones, though it remains important to note that the CTH-dependent retrievals introduce less differences compared to their CTH-independent retrievals.

While a variety of methods have been developed for estimating single-layer cloud base height (CBH), few studies have been introduced for retrieving overlapping CBH. To enhance the characterization of the vertical structure of overlapping clouds, which account for approximately a quarter of global clouds, this study presents an extrapolation algorithm for estimating overlapping CBH from passive radiometers. The algorithm relies on the continuity of cloud boundaries within a given region and develops four tests to identify appropriate single-layer cloud pixels for accurately inferring overlapping CBHs. The algorithm was applied to data from the aqua moderate-resolution imaging spectroradiometer (MODIS), and the results were validated against active cloud profiling radar (CPR)-cloud-aerosol Lidar with orthogonal polarization (CALIOP) measurements. The results indicate that the CBH retrievals derived from a single-layer cloud assumption are significantly biased in overlapping cloud cases. In contrast, the extrapolation algorithm provides more accurate retrievals of both upper layer ice CBH and lower layer water CBH. Specifically, the mean CBH bias for upper layer ice clouds is reduced from −2.4 to −0.9 km, while for lower layer water clouds, it is reduced from 3.8 to 1.6 km. By accurately extracting the vertical structure of overlapping clouds, this approach shows potential for improving cloud radiative forcing estimates, weather modification, and climate modeling.

Radiative calibration of satellite spectral radiometers is essential for their downstream applications. The Medium Resolution Spectral Imager (MERSI-II) is a key instrument of the Chinese polar orbit Fengyun-3D (FY-3D) satellite. However, its calibration performance has not been sufficiently studied, which limits its broad application. This study revealed the feasibility of a cloud-target method for assessing the MERSI-II calibration performance in solar bands. The top-of-atmosphere (TOA) reflectances for six MERSI-II reflective solar bands (RSBs) were numerically simulated using a rigorous forward radiative transfer method and cloud properties from well-collocated and well-calibrated Moderate Resolution Imaging Spectroradiometer (MODIS) operational cloud products with strict constraints. Only ice cloud targets were examined in the collocation due to their better homogeneity. The excellent agreement between our simulated reflectance and the MODIS reflectance (relative differences (RDs) of over 90% are within a 5% uncertainty range in six bands) validates our models. The simulated results in MERSI-II bands 1–4 showed reasonable agreements with the MERSI-II operational reflectance, i.e., mean RDs$\sim $15% and$\sim $12% (in the three years), respectively. More importantly, we removed these seasonal and degradation biases to improve the current calibration accuracy to a stable value within 3%. Due to its robust performance, our cloud-target-based calibration method can be applied to future MERSI-II sensors to monitor solar band stability.

In the above article[1], a difference in the definitions of our simulated reflectance (with respect to instantaneous TOA radiance) and the operational MERSI-II L1 reflectance (with respect to solar constant) causes errors in their direct comparisons.

In this study, a thermal infrared (TIR) based convolutional neural network (TIR-CNN), originally designed for retrieving cloud optical properties from TIR radiometry, is further developed to obtain global cloud base height (CBH) and cloud thermodynamic phase during both daytime and nighttime. It employs TIR radiances, cloud optical properties retrieved by TIR-CNN, altitude, landcover, and lifting condensation level as inputs to estimate global CBH and cloud phase for both single- and multi-layer clouds. This new model is trained, validated, and tested using radar-lidar products from CloudSat/CALIPSO. It provides global CBH with root-mean-square errors of 1.19 km and 1.91 km for single- and multi-layer clouds, respectively. A cloud layer classifier is trained to provide information on the quality of retrieved CBH, with total accuracies of 82% and 85% for single- and multi-layer clouds, respectively. In addition, the new model has remarkable accuracy in identifying the cloud phase within each pixel’s vertical column, particularly in differentiating mixed-phase clouds with an ice cloud top.

This study develops a machine learning (ML)-based multilayer cloud detection algorithm for the passive Advanced Himawari Imager (AHI) aboard the geostationary Himawari-8 satellite. AHI measurements in the 0.64-, 1.6-, 2.3-, 3.9-, 7.3-, 8.6-, 11.2-, and 12.4-$\mu \text{m}$channels, their combinations, geolocations, and observational geometries are used as predictors, and collocated active CloudSat and CALIPSO data are used to accurately label multilayer cloud pixels as the reference/truth of the predictand. We develop an ML-based daytime model (ML-Day) that utilizes all the aforementioned predictors and an all-time one (ML-All) that excludes the solar channel-dependent variables. Among four ML algorithms, the random forest (RF) performs slightly better than the artificial neural network, K-nearest neighbor, and support vector machines. By comparing with the merged CloudSat and CALIPSO product, the ML-Day model correctly identifies ~89% single-layer clouds and ~70% multilayer clouds, outperforming the Moderate Resolution Imaging Spectroradiometer (MODIS) operational multilayer cloud product (~80% and ~40% given by Marchantet al.). The success rates of ML-All for single-layer and multilayer clouds also reach ~85% and ~64%, respectively. The misclassification of our algorithm is mostly caused by missing optically thin clouds, a drawback of most radiometers without the 1.38-$\mu \text{m}$channel. Furthermore, with multilayer cloud pixels well detected by our algorithm, the AHI operational cloud top height retrievals are found to be larger biased due to multilayer cloud occurrence and might be improved by considering cloud vertical structures.

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).

Cloud top properties (CTPs) are important satellite products. However, the failures of CTPs derived from the Advanced Geostationary Radiation Imager of FengYun-4A (FY-4A/AGRI) have been occasionally reported by users. To this end, the feasibility of the operational CTP algorithm has been reviewed. First, this study reveals the slight differences in brightness temperature (BT) maps of thermal infrared (TIR) bands between two different imagers. Further analyses found that the nonretrieved pixels of CTP products from FY-4A/AGRI usually have a negative value down to −13 K in 10.8–13.5-$\mu \text{m}$BT difference (BTD$_{10.8-13.5\,\mu \text {m}}$), and the joint distribution related to BTD$_{10.8-13.5\,\mu \text {m}}$and BTD$_{10.8-12\,\mu \text {m}}$is well separated from those successfully retrieved ones. These findings are confirmed by the simulations of the radiative transfer forward model (approaching −12 K or lower) and the cross-validation between the products from AGRI/FY-4A and the Infrared Atmospheric Sounding Interferometer of Meteorological Operational Satellite Program-Satellite B. In essence, the bias at the 13.5-$\mu \text{m}$band is mainly affected by the relatively low accuracy and stability at the low-temperature end. Based on these findings, we have proposed a novel method to estimate calibration-related measurement biases of TIR bands and track their in-orbit stability. The statistical study based on the FY-4A/AGRI observations reveals significant daily and diurnal variations in performance and provides an insight into its stability at the TIR band ($13.5~\mu \text{m}$).

Random forests (RFs), an advanced machine learning (ML) method, was used here to develop a robust and rapid quantitative precipitation estimates (QPEs) algorithm for the new-generation geostationary satellite of Himawari-8. In this algorithm, the global precipitation measurement (GPM) product has been employed to train QPE prediction model. The real-time multiband infrared brightness temperature from Himawari-8, combined with the spatiotemporally matched numerical weather prediction (NWP) data from the global forecast system, have been used as predictor variables for QPE. Among the variables used in RF learning model, total precipitable water and$K$-index from NWP data have the highest rankings, indicating the importance of atmospheric environment for QPE. To enhance the accuracy of RF models or to optimize model training, a sample-balance technique has been utilized to adjust the ratios of samples in nonprecipitation/precipitation classification and quantitative precipitation regression data sets. Further sensitivity and validation analyses help determine the optimal RF classification and regression models for predicting nonprecipitation/precipitation pixel and rain rate. The selected RF classification model is found to predict precipitation area with an accuracy of 0.87. For predicted QPE product, the mean-absolute-error and root-mean-square error of RF regression model are 0.51 and 2.0 mm/h, respectively. Overall, the RF ML algorithm has a higher detection rate over homogenous ocean surface as compared with over land. Meanwhile, this RF algorithm tends to underestimate rain rate, especially in the presence of heavy rainfall. Despite this, it still produces a reasonable pattern of rainfall area and intensity, which are highly consistent with GPM observations.

Near-infrared (NIR) channels, such as the 1.6- and 2.13-μm channels of Moderate Resolution Imaging Spectroradiometer (MODIS), play an important role in inferring cloud properties because of their sensitivity to cloud amount and particle size. Instead of the 2.13-μm channel, which has shown great success on MODIS, the central wavelength of the Visible Infrared Imaging Radiometer Suite (VIIRS) is shifted to 2.25 μm. This paper investigates the influences of NIR channels (i.e., 2.13 and $2.25 μm) on cloud optical and microphysical property retrievals and reveals the potential applications of the 2.25-μm channel to cloud thermodynamic phase and multilayer cloud detections by combining with the 1.6-μm channel. Rigorous radiative transfer simulations are performed to provide theoretical reflectance at the channels of interest, and MODIS and VIIRS observations are used for case studies. Our results indicate a minor influence of the 2.25-μm channel on cloud optical depth and effective particle size retrievals. In combination with the 1.6-μm channel, the 2.25-μm channel provides additional information indicating cloud phases. However, the 1.6- and 2.13-μm channels do not show any sensitivity to cloud phase. Furthermore, by considering the infrared-based cloud phase results, the 1.6- and 2.25-μm channel combination becomes possible to infer multilayer clouds. Case studies based on simultaneous MODIS and VIIRS observations demonstrate the capability of the 1.6-2.25-μm channel combination for determining cloud phase and multilayer clouds. Collocated satellite-based active lidar observations further validate these advantages of the 2.25-μmu channel over the original 2.13-μm channel.