1 Abstract-The cloud water path (CWP) has an important influence on the radiative effects of clouds and the water cycle in the Earth’s atmospheric system, serving as a key parameter in physical cloud processes. In this study, a novel method for retrieving CWP by leveraging the advantages of multisource and multiband active and passive satellite observations is proposed. A retrieval model to retrieve CWP that using Himawari-8/AHI) thermal infrared channels is established by learning from active radar (CloudSat) measurements, the model enables continuous CWP retrieval throughout the day. Compared with all-day CloudSat-CWP, our CWP products has has a higher retrieval accuracy that that of MODIS. The distribution of the monthly average CWP product based on the Himawari-8 full-disk dataset resembles that of CloudSat observations, with the highest average CWPs in equatorial region, followed by the CWPs in midlatitude regions. This spatial pattern of CWP is possibly due to the prevalence of strong convective systems in these areas, which facilitate the formation and progression of deep clouds, leading to higher CWP values. This algorithm can offer valuable data support for atmospheric-related analyses and has been integrated into the Cloud Remote Sensing, Atmospheric Radiation, and Renewable Energy Application (CARE) platform for atmospheric remote sensing algorithms.

Large inaccuracies remain in the traditional convective cloud identification system over the plateau area struggles to capture mid- and low-level clouds due to the complex topographic effects influencing cloud pressure. Besides, the lack of efficient nighttime cloud-type products hinders progress in the research on the diurnal cycle and seasonal variation in convective clouds (including deep convection and cumulus clouds) over the Tibet Plateau (TP). In this study, we incorporated Shapley additive explanation (SHAP) tuning into the fundamental machine learning CatBoost Classifier technology, which was applied to a 24-h convective cloud detection algorithm utilizing cloud top temperature (CTT) and optical thickness data derived from the Himawari-8 infrared channels. This specifically tackles the problem of underestimating cumulus clouds in plateau areas. This innovative product enables capturing important processes of deep convection, especially for cumulus clouds, facilitating a comprehensive spatial-temporal analysis of the entire TP region. The results confirm that the new algorithm shows significant improvements in cumulus detection compared to the official cloud product of Himawari-8. In addition, the deep convective clouds have also improved from 35.85% to 63.05% for hit rate (HR) value. The analysis reveals a notable diurnal variation in convective cloud activity over the TP, predominantly occurring from noon to night. This finding underscores the influential heating role of the TP in convective activity.

Remote sensing of cloud properties based on multispectral or hyperspectral observations from satellites is important for earth radiation budget and climate change studies. Currently, most retrieval algorithms for the hyperspectral measurements are developed based on the O2-A band to derive cloud optical thickness (COT) and cloud top height (CTH) via the optimal estimation theory. Nevertheless, there are few studies on the retrieval of COT and CTH using the O4band, where the direct computation of slant column density and spectral information in the blue band provide a faster yet flexible inversion strategy. In this study, we develop a novel cloud retrieval algorithm based on neural networks using the O4band (CRANN-O4) for the simultaneous derivation of COT and CTH. CRANN-O4 employs a transfer learning strategy that combines the radiative transfer model (RTM) and multisource satellite data, for which the deep neural network module is pretrained based on the simulation data from RTM to enhance its adaptability and interpretability, following a fine-tuning scheme using multisource satellite data. To evaluate the CRANN-O4 performance, we apply CRANN-O4 to TROPOMI and make an intercomparison with its official products, which is generated based on the O2-A band. The results indicate that the CRANN-O4-derived spatial distributions of COT and CTH are generally similar to the official TROPOMI cloud product but are more consistent with the SNPP-VIIRS cloud product. The RMSEs of COT and CTH derived by CRANN-O4 are approximately 15.88 and 2.33 km, respectively, while those of the TROPOMI cloud product are 20.85 and 3.00 km, respectively. In addition, the validation of CRANN-O4-derived CTH using CALIOP measurements demonstrates better agreement than that of the TROPOMI official cloud product, with RMSE decreasing from 2.7 km to 2.2 km. The methodology presented in this study provides innovative insight into cloud parameter retrieval for hyperspectral instruments with O4channels, such as FY-3F/OMS.

Accurate cloud detection via satellites is important for cloud radiative forcing estimation and disaster weather monitoring. Current polar-orbiting satellite cloud observation are limited during early morning orbit and contain notable uncertainty due to dimness measurements in visible bands. FY-3E\MERSI-LL is the first early morning orbit satellite worldwide and can realize global cloud observation under early morning scenarios. In this study, a dynamic threshold cloud detection algorithm is proposed based on the FY-3E\MERSI-LL infrared channel, combined with auxiliary data such as sea surface temperature, land surface temperature, snow cover mask and terrain elevation. The algorithm can detect clouds against complex land surface background, but faces classification difficulties over some plateau, high-latitude and snow surface regions, especially during early morning observation periods. Compared to coincident Himawari-8 and GOES-16 cloud measurements in the Eastern and Western Hemispheres, respectively, our algorithm recognizes reasonable cloud distributions. Furthermore, Himawari-8 and GOES-16 cloud products are used for quantitative cloud algorithm evaluation. The results show that at low-middle latitudes (60°N-60°S), the average cloud and clear hit rates during the various seasons are 73.24% and 76.46%, respectively, the cloud leakage and false alarm rates are 14.46% and 8.15%, respectively, and the total accuracy (cloud and clear) is 77.33%. The algorithm performance is better over the ocean than over land. Ground site MPLCMASK products are also used to verify the FY-3E cloud results in middle- and high-latitude areas. This algorithm provides a cloud detection reference during early morning orbit based on infrared channels.

Clouds play an important role in the radiative energy balance of the Earth–atmosphere system. Compared with traditional optical satellite sensors, polarimetric sensors combine multi-angle, multi-polarization, and multispectral information, displaying the advantages of high spatial and temporal resolutions and global coverage. Such remote sensing measurements improve the accuracy of cloud properties retrieval. Due to the observation characteristics of passive satellites, even a tiny variation in position will result in a great change in the observation geometry. A large number of studies have shown that the scattering angle is very crucial for the polarization characteristics retrieval of reflected light. In this study, we analyze the dependence of the remote sensing retrieval implement of different cloud characteristics on the observed scattering angle coverage, considering both ice and water clouds. Three satellite sensors – POLarization and Directionality of the Earth’s Reflectance-3/Polarization and Anisotropy of Reflectance for Atmospheric Sciences coupled with Observations from a Lidar (POLDER-3/PARASOL), Directional Polarimetric Camera/ GaoFen-5 spacecraft (DPC/GF-5), and DPC/GF-5(02) – were selected to compare their scattering angle coverages and the number of angular measurements at equatorial, middle, and high latitudes. The requirements for angular polarized and nonpolarized observations varied depending on the retrieval of cloud properties. The impact of orbital characteristics and viewing settings was investigated for cloud detection, cloud phase classification, and cloud microphysical properties retrieval. Finally, an analytical model to comprehensively evaluate the effective angular measurements according to the orbital characteristics and viewing settings was developed to facilitate the future design of similar sensors for cloud remote sensing.

The Japan Meteorological Agency (JMA) successfully launched the Himawari-8 (H-8) new-generation geostationary meteorological satellite with the Advanced Himawari Imager (AHI) sensor on October 7, 2014. The H-8/AHI level-2 (L2) operational cloud property products were released by the Japan Aerospace Exploration Agency during September 2016. The Voronoi light scattering model, which is a fractal ice particle habit, was utilized to develop the retrieval algorithm called “Comprehensive Analysis Program for Cloud Optical Measurement” (CAPCOM-INV)-ice for the AHI ice cloud product. In this paper, we describe the CAPCOM-INV-ice algorithm for ice cloud products from AHI data. To investigate its retrieval performance, retrieval results were compared with 2000 samples of the ice cloud optical thickness and effective particle radius values. Furthermore, AHI ice cloud products are evaluated by comparing them with the MODIS collection-6 (C6) products. As an experiment, cloud property retrievals from AHI measurements, with an observation interval time of 2.5 min and ground-based rainfall observation radar data (the latter of which is supplied by the JMA, with a 1-km grid mesh), are used to investigate the generation processes of deep convective (DC) cloud in the vicinity of the Kyushu island, Japan. It revealed that AHI measurements have the capability of monitoring the growth processes, including variation of the cloud properties and the precipitation in the DC cloud.

The cloudbow structure is directly related to the retrieval of cloud droplet size distribution (droplet effective radius and effective variance). This study investigated the effect of the cloud optical thickness, ground surface albedo and the above-cloud absorbing dust layer on the cloudbow structure based on the modeled airborne directional polarimetric camera (DPC) measurements, which are simulated in 670 nm using Mie scattering theory and the vector radiative transfer mode. It is found that the polarized reflectance increase as the increase of the cloud optical thickness (COT) and saturate when COT=10. The absorbing dust layer's signal would cover the signal from the cloud layer as the aerosol optical thickness increased to 1. Additionally, the surface albedo has negligible effect on the cloudbow structure.

Launching in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument has been stable operating for more than 5 years on board the Suomi National Polar-orbiting Partnership (S-NPP) spacecraft. Some researchers indicated that its aerosol optical thickness (AOT) product had larger biases than MODIS over land. Although the VIIRS-AOT algorithm is based on MODIS Dark-Target algorithm, some differs exist, including cloud mask. In considering the algorithm independence and identification complexity, we develop a new quick cloud mask algorithm for aerosol retrieval. Based on the spatial variability test inherent from MODIS, we add a expand test to remove the pixels mixed with cloud or effected by neighboring pixels. The results illustrate that this new test can screen out the confident cloudy pixels that VIIRS algorithm regard as clear sky. Throughout hundreds test in different weather conditions, the algorithm perform well.