The vertical distribution of dust layers can influence dust transport, radiative forcing, deposition and ultimately, surface particulate matter mass concentration. Although many dust layer height (ALH) products from passive satellite measurements have been developed, most of them are applicable on dark surfaces only. Here, building on the absorbing aerosol optical centroid height (AOCH) retrieval from hyperspectral O2A and B absorption band measurements of TROPOspheric Monitoring Instrument (TROPOMI) for dark target, we further develop dust AOCH retrieval over bright surfaces. Key updates include: (a) the thresholds in cloud mask tests are refined with consideration of the different spectral characteristics of bright surface reflectance; (b) the assumption of Lambertian surface is modified to the Ross-Li Bidirectional Reflectance Distribution Function (BRDF) model to consider the angular dependence of surface reflectance. The validation against the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) for several dust plumes over Saharan Desert illustrates that TROPOMI AOCH has ~1 km uncertainty and ~0.1 km mean bias, better than ~1 km underestimated dust layer mean altitude (ALT) from the Infrared Atmospheric Sounder Interferometer (IASI). With this implement of bright surfaces, our algorithm is ready for global retrieval and will be applicable for similar hyperspectral instrument in the future.

The multiangle polarimetric (MAP) measurement provides abundant information about aerosol microphysical properties, but its physical retrieval methods of aerosols usually rely on time-consuming optimal iterative calculations. This study introduces a robust and efficient MAP aerosol retrieval over eastern China based on a data-driven deep learning (DL) method. By directly training the function relationship between Polarization and Directionality of the Earth’s Reflectances (POLDER) measurements and matched aerosol products in typical Aerosol Robotic Network (AERONET) sites with the deep belief network (DBN) methods, aerosol optical depth (AOD), fine mode AOD (FAOD), coarse mode AOD (CAOD), and single scattering albedo (SSA) can be retrieved reliably. Ground validation shows very high accuracy for POLDER-3 DBN AOD (${R} = 0.917$) and FAOD (${R} = 0.942$) compared with AERONET results. Despite a decrease in retrieval accuracy, DBN CAOD and spectral SSA exhibit very consistent variations with ground inversions. In particular, POLDER-3 DBN retrievals over eastern China perform better than generalized retrieval of aerosol and surface properties (GRASP) products with optimized method. Our results demonstrate that DBN can well model the complex functional relationships between MAP measurements and aerosol optical/microphysical parameters. With the striking advantage in computational efficiency and modeling ability, the DL methods, such as DBN, have an enormous potential in operational aerosol retrieval of the emerging MAP satellite instruments.

The Multi-angle Imaging SpectroRadiometer (MISR) measurement with a large range of scattering angles provides valuable information about aerosol microphysical properties. The current MISR algorithm utilizes pre-defined aerosol mixtures in lookup tables (LUT) to infer aerosol types and microphysical parameters, which performs well globally but remains subject to considerable uncertainties in regional scales. To make efficient use of MISR measurement, we developed a physics-informed Deep Learning (PDL) method to retrieve aerosol optical/microphysical parameters over land in eastern China. By combining the physical constraint of radiative transfer simulation and modeling ability of DL methods, each aerosol parameter can be modeled with the whole used MISR measurements separately with high computational efficiency. PDL Aerosol Optical Depth (AOD) and fine AOD(FAOD) have high correlation coefficients (R>0.95) with Aerosol Robotic Network (AERONET) observations, with 89% and 81% values falling into expected error (EE) envelope of ± (0.05+20%AODAERONET) respectively. Despite only a slightly higher accuracy than recent MISR Version 23 products, PDL retrievals have solved the underestimation problem of AOD and FAOD at moderate-high values (>0.4). Besides better constraint of abnormal values in coarse AOD(CAOD), PDL algorithm significantly improves retrieval accuracy of MISR Single Scattering Albedo (SSA). With reliable and robust performance, PDL algorithm provides a flexible and efficient aerosol retrieval framework for emerging multi-angle polarimetric measurements.

The AErosol RObotic NETwork (AERONET) inversion data over East Asia (72°E – 135°E, 30°N – 50°N) in spring from 2018 to 2020 were collected to characterize the microphysical and single scattering properties of the dust aerosols in Asia, and a climatological aerosol model was then built up for the Asian dust. A bimodal log-normal size distribution is assumed for the climatological Asian dust model, and the non-spherical effect was considered for the coarse mode only. The microphysical and single scattering properties of dust aerosols are parameterized by the 675-nm AOD and wavelength in the Asian dust aerosol model. The parameterization is based on the fitting or interpolation of the AERONET data. A lookup table was generated based on the Asian dust model via the UNified Linearized Vector Radiative Transfer Model (UNL-VRTM) for retrieving the dust plume height from Earth Polychromatic Imaging Camera (EPIC) measurements.

The multi-angle polarimetric (MAP) instruments have been a focus of recent satellite missions dedicated to enhanced detection of global aerosol microphysical properties. Considering that satellite observations can hardly infer all the unknowns of atmosphere and surface, it’s crucial to know how many and which aerosol parameters can be accurately retrieved from these different MAP measurements as well as their uncertainties. In this study, we present a comprehensive insight into the information content of POLDER-3 and 3MI observations for aerosol retrievals and estimate posterior errors of corresponding parameters based on Bayesian theory. The total degree of freedom for signal (DFS) of aerosol retrievals is around 6-8 from POLDER-3, and is raised by ~1.8-3.5 with 3MI. The retrieval accuracy of volume concentration and effective radius are high (<4%) in the fine-dominant case for both POLDER-3 and 3MI, but get much lower (~8% and ~15%) in coarse-dominant conditions. Furthermore, the advanced 3MI measurements can upgrade the retrieval uncertainties of POLDER-3 by ~50%. Though additional shortwave infrared bands of 3MI provide more information regarding coarse particles, the influence of aerosols on surface BRDF leads to a decrease of the total DFS. With a prior assumption that variations of refractive index depending on wavelength, satellite retrieval accuracy of the real (<0.03) and imaginary part (<0.003) reaches close levels with that of ground-based Sun photometers. Our results can provide a fundamental reference for MAP satellite retrieval of aerosol microphysical properties.

We present an algorithm for retrieving aerosol layer height (ALH) and aerosol optical depth (AOD) for smoke and dust over vegetated land and water surfaces from measurements of the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR). Our algorithm uses EPIC atmospheric window bands to determine AOD and then takes advantage of oxygen A and B bands to derive ALH. We applied this algorithm on several dust and smoke events. Validation shows our results are of high accuracy.

BACKGROUND: Recent advances in data analysis methods based on principles of Mendelian Randomisation, such as Egger regression and the weighted median estimator, add to the researcher's ability to infer cause-effect links from observational data. Now is the time to gauge the potential of these methods within specific areas of biomedical research. In this paper, we choose a study in metabolomics as an illustrative testbed. We apply Mendelian Randomisation methods in the analysis of data from the DILGOM (Dietary, Lifestyle and Genetic determinants of Obesity and Metabolic syndrome) study, in the context of an effort to identify molecular pathways of cardiovascular disease. In particular, our illustrative analysis addresses the question whether body mass, as measured by body mass index (BMI), exerts a causal effect on the concentrations of a collection of 137 cardiometabolic markers with different degrees of atherogenic power, such as the (highly atherogenic) lipoprotein metabolites with very low density (VLDLs) and the (protective) high density lipoprotein metabolites. RESULTS: We found strongest evidence of a positive BMI effect (that is, evidence that an increase in BMI causes an increase in the metabolite concentration) on those metabolites known to represent strong risk factors for coronary artery disease, such as the VLDLs, and evidence of a negative effect on protective biomarkers. CONCLUSIONS: The methods discussed represent a useful scientific tool, although they assume the validity of conditions that are (at best) only partially verifiable. This paper provides a rigorous account of such conditions. The results of our analysis provide a proof-of-concept illustration of the potential usefulness of Mendelian Randomisation in genomic biobank studies aiming to dissect the molecular causes of disease, and to identify candidate pharmacological targets.