Aerosol properties, including aerosol optical depth (AOD), aerosol absorption optical depth (AAOD), single scattering albedo (SSA), and fine mode fraction (FMF), are essential in studying aerosol climate effects. Spectral multiangle polarimetry (MAP) has been recognized as a promising technique for comprehensive retrievals of global aerosol optical properties from space. As one of the very few MAP sensors in space, the Directional Polarimetric Camera (DPC) onboard the GaoFen (GF)-5 satellite has great potential to provide these critical aerosol parameters. However, retrievals of aerosol parameters from DPC, especially SSA and AAOD, still remain limited. This study introduces a machine-learning algorithm using the eXtreme gradient boosting (XGBoost) model to retrieve AOD, AAOD, SSA, FMF, as well as surface albedo (expressed as the directional hemispherical reflectance, DHR) over land from DPC multiangle reflectances and degree of linear polarization (DOLP), using AERONET aerosol measurements and Moderate Resolution Imaging Spectroradiometer (MODIS) DHR data as the training target. Cross-validation indicates high retrieval accuracy, with correlations exceeding 0.75 for all parameters under sufficient aerosol loading. Notably, the accuracy of SSA retrieval is comparable to that of the Polarization and Directionality of the Earth’s Reflectance (POLDER) products, with 73% of the independently retrieved 670-nm SSA falling within the ±0.03 error envelope (EE) when 670-nm AOD is above 0.30. Gridded products also effectively capture the spatial and seasonal variability of aerosol properties worldwide, such as in regions dominated by biomass burning and dust. This study confirms the capability of DPC for aerosol property retrievals, which could serve as an important technique and data source for global aerosol and climate monitoring.

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.

The Chinese early-morning-orbiting Fengyun-3E (FY-3E) satellite fills the 6-h initial observation window for data assimilation in numerical weather prediction (NWP). The low-light band (LLB) on the medium-resolution spectral imager low light (MERSI-LL) of FY-3E can detect extremely low radiances at night, significantly enhancing nighttime observation capabilities as well as elevating data assimilation quality by improving the nighttime cloud mask algorithm. However, severe and nonlinear stray light contamination affects most nocturnal FY-3E/MERSI-LL LLB images, particularly those from the Southern Hemisphere, hindering further visualization applications. The analysis concluded that the stray light is closely associated with the refraction and reflection of sunlight entering the MERSI-LL, solar zenith angle (SZA), and detector number. To obtain clear and enhanced images, this study designed a fully automated and adaptive stray light correction and enhancement algorithm for the nocturnal low-light images of FY-3E/MERSI-LL. Three typical stray-light-contaminated scenarios were categorized for all nighttime images. The restored results showed that after processing, the “fog” stray light and stripes were essentially removed, and the details became richer and more prominent, significantly improving the visual effect and usability of the images. This algorithm is simple, efficient, and highly applicable, and will be integrated into the processing system of the FY-3E satellite to support near real-time applications of LLB images. However, some strong or unusual stray light still affects the local continuity of the images. Future low-light imagers of FY-3 satellites will feature more sophisticated instruments to reduce incident stray light in their optical system.

Satellite observations are widely used in large-scale aerosol monitoring. In the past 20 years, aerosol compositions have changed substantially worldwide, especially over developing regions, such as East and South Asia, which leads to long-term trends in aerosol single scattering albedo (SSA). However, major satellite aerosol retrieval algorithms, such as the widely used dark-target (DT) algorithm by the moderate-resolution imaging spectroradiometer (MODIS), apply the same set of aerosol models over time with almost a constant SSA, which may have defects in capturing the actual aerosol optical depth (AOD) trend. In this work, we use MODIS-Aqua observations as an example to investigate the impact of SSA trends on the satellite-retrieved AOD trend. A series of sensitivity experiments using radiative transfer simulations with different surface conditions and aerosol loadings indicate that ±0.06 SSA change may lead to 30% AOD retrieval uncertainties, and increased surface albedo and aerosol loading would enhance this error. An analysis at four representative sites in the aerosol robotic network (AERONET) where significant long-term SSA trends are observed, namely Beijing, Kanpur, Barcelona, and Carpentras, shows that the operational MODIS products at all four sites suffer from distinct biases in the AOD trends. By replacing the prescribed SSAs with those observed by AERONET in the MODIS algorithm, the retrieved AOD trend biases are largely corrected. This study points out the importance of aerosol scattering and absorption properties in MODIS-like AOD retrieval algorithms. It is, therefore, urgent to establish long-term monitoring of SSA globally.

The uncertainty of water cloud feedback on radiative forcing is one of the largest obstacles to producing confident projections of the global climate. Sufficient measurements of water clouds are crucial to addressing this issue. However, existing techniques based on remote sensing or in situ instruments face limitations in data capacity attributed to the short lifetime, high temporal variability, and complex vertical structure of water clouds. In this study, taking advantage of a dual-field-of-view (dual-FOV) high-spectral-resolution lidar (HSRL), we developed a novel algorithm to obtain diurnal simultaneous profiles of water cloud optical and microphysical properties with high temporal-spatial resolution. This technique does not rely on the widely used subadiabatic assumption about the vertical structure of water clouds. The retrieval algorithm, validated by simulations and cloud radar measurements, was applied to field experiment data collected at the Beijing and Hangzhou sites in China. The relationship functions between water cloud properties are presented to enhance our understanding of the underlying processes. Furthermore, the vertical distributions of retrieved properties are compared to the subadiabatic assumption. The dual-FOV HSRL technique enables comprehensive observations, enhancing our understanding of water clouds and providing significant insights into the interactions among clouds, aerosols, precipitation, and radiation.