Retrieval Of Aerosol Properties Research Articles

Cloud detection from multi-angular polarimetric satellite measurements using a neural network ensemble approach

Abstract. This paper describes a neural network cloud masking scheme from PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) multi-angle polarimetric measurements. The algorithm has been trained on synthetic measurements and has been applied to the processing of 1 year of PARASOL data. Comparisons of the retrieved cloud fraction with MODIS (Moderate Resolution Imaging Spectroradiometer) products show overall agreement in spatial and temporal patterns, but the PARASOL neural network (PARASOL-NN) retrieves lower cloud fractions. Comparisons with a goodness-of-fit mask from aerosol retrievals suggest that the NN cloud mask flags fewer clear pixels as cloudy than MODIS (∼ 3 % of the clear pixels versus ∼ 15 % by MODIS). On the other hand the NN classifies more pixels incorrectly as clear than MODIS (∼ 20 % by NN, versus ∼ 15 % by MODIS). Additionally, the NN and MODIS cloud mask have been applied to the aerosol retrievals from PARASOL using the Remote Sensing of Trace Gas and Aerosol Products (RemoTAP) algorithm. Validation with AERONET shows that the NN cloud mask performs comparably with MODIS in screening residual cloud contamination in retrieved aerosol properties. Our study demonstrates that cloud masking from multi-angle polarimeter (MAP) aerosol retrievals can be performed based on the MAP measurements themselves, making the retrievals independent of the availability of a cloud imager.

Efficient single-scattering lookup table for lidar and polarimeter phytoplankton studies.

Coupled atmosphere and ocean remote sensing retrievals of aerosol, cloud, and oceanic phytoplankton microphysical properties, such as those carried out by the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, involve single-scattering calculations that are time consuming. Lookup tables (LUTs) exist to speed up these calculations for aerosol and water droplets in the atmosphere. In our new Lorenz-Mie lookup table, we tabulate single scattering by an ensemble of coated isotropic spheres representing oceanic phytoplankton at wavelengths from 0.355 µm. The lookup table covers phytoplankton particles with radii in the range of 0.15-100 µm at an increase of up to 104 in computational speed compared to single-scattering calculations. The allowed complex refractive indices range from 1.05 to 1.24 for the shell's real part, from 10-7 to 0.3 for the shell's imaginary part, from 0 to 0.001 for the core's imaginary part, and equal to 1.02 for the core's real part. We show that we precisely compute inherent optical properties for the phytoplankton size distributions ranging up to 5 µm for the effective radius and up to 0.6 for the effective variance. We test wavelengths from 0.355 to 1.065 µm and find that all the inherent optical properties of interest agree with the single-scattering calculations to within 1% for 99.9% of cases. We also provide an example of using the lookup table to reproduce the phytoplankton optical datasets listed in the PANGAEA database for synthetic hyperspectral algorithm development. The table together with C++, Fortran, MATLAB, and Python codes to apply different complex refractive indices and phytoplankton size distributions is freely available online.

Assessment of the spectral misalignment effect (SMILE) on EarthCARE's Multi-Spectral Imager aerosol and cloud property retrievals

Abstract. The Multi-Spectral Imager (MSI) on board the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) will provide horizontal information about aerosols and clouds. These measurements are needed to extend vertical cloud and aerosol property information, which is obtained from EarthCARE's active sensors, in order to obtain a full three-dimensional view of cloud and aerosol conditions. Mesoscale weather systems, in particular, will be characterized. The discovery of a non-compliance of the MSI visible–near-infrared–shortwave infrared (VNS) camera’s visible (VIS) and shortwave infrared (SWIR1) channels regarding a spectral central wavelength (CWVL) shift across-track of up to 14 nm (VIS) and 20 nm (SWIR1) led to the need for an analysis regarding its impact on MSI Level-2A aerosol and cloud products. A significant influence of the spectral misalignment effect (SMILE) on MSI retrievals is identified due to the spectral variation in gas absorption, surface reflectance, and aerosol and cloud properties within the spectral ranges of these MSI bands. For example, the VIS channel is positioned in close proximity to the red edge of green vegetation and is impacted by residual absorption of water vapor and ozone. Small central wavelength variations introduce uncertainties due to the rapid change in surface reflectance for conditions with low optical thickness. The present central wavelength shift in the VIS towards shorter wavelengths than at nadir introduces a relative error in transmission of up to 3.3 % due to the increasing influence of water vapor and ozone absorption. We found relative errors in the top-of-atmosphere (TOA) signal due to the SMILE of up to 30 % for low optical thickness over a land surface in that band. Since the magnitude of the impact strongly depends on the underlying surface and atmospheric conditions, we conclude that accounting for the SMILE in Level-2 retrievals or correcting the Level-1 signal will improve MSI aerosol and cloud product quality.

Satellite Multi‐Angle Observations of Wildfire Smoke Plumes During the CalFiDE Field Campaign: Aerosol Plume Heights, Particle Property Evolution, and Aging Timescales

AbstractWildfire‐related aircraft field campaigns frequently offer opportunities to validate remote‐sensing retrievals of aerosol properties and other quantities derived from satellite‐borne‐instrument observations. Satellite instruments often provide regional context‐imagery for more sparsely sampled aircraft and surface‐based measurements. However, aerosol amount, particle type, aerosol plume height and the associated wind vector products retrieved from the NASA Earth Observing System's Multi‐angle Imaging SpectroRadiometer (MISR) instrument have matured sufficiently that these quantities can also contribute substantially to a campaign data set, in regional context. This is especially useful when such measurements are not acquired at all from the suborbital platforms. During NOAA's California Fire Dynamics Experiment (CalFiDE), aircraft operations were coordinated with MISR overpasses on two occasions: for the Rum Creek fire on 30 August 2022, and for the Mosquito fire on 08 September. MISR‐retrieved aerosol properties show distinctly different patterns of black and brown smoke particle distributions and inferred plume evolution in the two cases. This paper presents the satellite‐retrieved results that complement the field observations, demonstrating what such measurements can offer, and contributing material for detailed fire dynamics and chemistry studies when combined with the CalFiDE suborbital observations and models in continuing studies.

Simultaneous retrieval of aerosol and ocean properties from PACE HARP2 with uncertainty assessment using cascading neural network radiative transfer models

Abstract. The University of Maryland, Baltimore County (UMBC) Hyper-Angular Rainbow Polarimeter (HARP2) will be on board NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in January 2024. In this study we systematically evaluate the retrievability and uncertainty of aerosol and ocean parameters from HARP2 multi-angle polarimeter (MAP) measurements. To reduce the computational demand of MAP-based retrievals and maximize data processing throughput, we developed improved neural network (NN) forward models for spaceborne HARP2 measurements over a coupled atmosphere and ocean system within the FastMAPOL retrieval algorithm. To this end, a cascading retrieval scheme is implemented in FastMAPOL, which leverages a series of NN models of varying size, speed, and accuracy to optimize performance. Two sets of NN models are used for reflectance and polarization, respectively. A full day of global synthetic HARP2 data was generated and used to test various retrieval parameters including aerosol microphysical and optical properties, aerosol layer height, ocean surface wind speed, and ocean chlorophyll a concentration. To assess retrieval quality, pixel-wise retrieval uncertainties were derived from error propagation and evaluated against the difference between the retrieval parameters and truth based on a Monte Carlo method. We found that the fine-mode aerosol properties can be retrieved well from the HARP2 data, though the coarse-mode aerosol properties are more uncertain. Larger uncertainties are associated with a reduced number of available viewing angles, which typically occur near the scan edge of the HARP2 instrument. Results of the performance assessment demonstrate that the algorithm is a viable approach for operational application to HARP2 data after the PACE launch.

The Detection of Desert Aerosol Incorporating Coherent Doppler Wind Lidar and Rayleigh–Mie–Raman Lidar

Characterization of aerosol transportation is important in order to understand regional and global climatic changes. To obtain accurate aerosol profiles and wind profiles, aerosol lidar and Doppler wind lidar are generally combined in atmospheric measurements. In this work, a method for calibration and quantitative aerosol properties using coherent Doppler wind lidar (CDWL) is adopted, and data retrieval is verified by contrasting the process with synchronous Rayleigh–Mie–Raman lidar (RMRL). The comparison was applied to field measurements in the Taklimakan desert, from 16 to 21 February 2023. Good agreements between the two lidars was found, with the determination coefficients of 0.90 and 0.89 and the root-mean-square error (RMSE) values of 0.012 and 0.013. The comparative results of continuous experiments demonstrate the ability of the CDWL to retrieve aerosol properties accurately.

Evaluation of the Accuracy of the Aerosol Optical and Microphysical Retrievals by the GRASP Algorithm from Combined Measurements of a Polarized Sun-Sky-Lunar Photometer and a Three-Wavelength Elastic Lidar

The versatile Generalized Retrieval of Aerosol and Surface Properties (GRASP) algorithm exploits the advantages of synergic ground-based aerosol observations such as radiometric (sensitive to columnar aerosol optical and microphysical properties) and lidar (sensitive to vertical distribution of the optical properties) observations. The synergy is possible when the complementary data is mutually constrained by GRASP parametrization that includes, for the first time ever, the degree of linear polarization (DoLP) parameter measured by a polarized sun-sky-lunar AERONET photometer (380, 440, 500, 675, 870, 1020, and 1640 nm) in synergy with the vertical profiles from an elastic lidar (355, 532, and 1064 nm). First, a series of numerical tests is performed using simulated data generated using a climatology of data and ground-based measurements. The inversions are performed with and without random noise for five different combinations of input data, starting from the AERONET-like dataset and increasing to the complex one by adding more information for three aerosol scenarios: I—high aerosol optical depth (AOD) with dominant coarse mode; II—low AOD with dominant coarse mode; III—high AOD with dominant fine mode. The inclusion of DoLP improves (i) the retrieval accuracy of the fine-mode properties when it is not dominant; (ii) the retrieval accuracy of the coarse-mode properties at longer wavelengths and that of the fine-mode properties at shorter wavelengths; (iii) the retrieval accuracy of the coarse-mode real part of the refractive index (up to 36% reduction), but has no effect on the retrieval of the imaginary part; (iv) reduces up to 83% the bias of the sphere fraction (SF) retrieval in coarse-mode dominated regimes; and (v) the root mean square error (RMSE) of the retrieval for most of the parameters in all scenarios. In addition, the addition of more photometer channels in synergy with a three-wavelength elastic lidar reduces the RMSE for the real part (67% in the coarse mode) and the imaginary part (35% in the fine mode) of the refractive index, the single scattering albedo (38% in the fine mode), the lidar ratio (20% in the coarse mode), and the SF (43%).

Retrieval of aerosol properties from zenith sky radiance measurements

Abstract. This study explores the potential to retrieve aerosol properties with the GRASP algorithm (Generalized Retrieval of Atmosphere and Surface Properties) using as input measurements of zenith sky radiance (ZSR), which are sky radiance values measured in the zenith direction, recorded at four wavelengths by a ZEN-R52 radiometer. To this end, the ZSR measured at 440, 500, 675 and 870 nm by a ZEN-R52 (ZSRZEN), installed in Valladolid (Spain), is employed. This instrument is calibrated by intercomparing the signal of each channel with coincident ZSR values simulated (ZSRSIM) at the same wavelengths with a radiative transfer model (RTM). These simulations are carried out using the GRASP forward module as RTM and the aerosol information from a co-located CE318 photometer belonging to AERONET (AErosol RObotic NETwork) as input. The dark signal and the signal dependence on temperature are characterized and included in the calibration process. The uncertainties for each channel are quantified by an intercomparison with a co-located CE318 photometer, obtaining lower values for shorter wavelengths; they are between 3 % for 440 nm and 21 % for 870 nm. The proposed inversion strategy for the aerosol retrieval using the ZSRZEN measurements as input, i.e. so-called GRASP-ZEN, assumes the aerosol as an external mixture of five pre-calculated aerosol types. A sensitivity analysis is conducted using synthetic ZSRZEN measurements, pointing out that these measurements are sensitive to aerosol load and type. It also assesses that the retrieved aerosol optical depth (AOD) values in general overestimate the reference ones by 0.03, 0.02, 0.02 and 0.01 for 440, 500, 675 and 870 nm, respectively. The calibrated ZSRZEN measurements, recorded during 2.5 years at Valladolid, are inverted by the GRASP-ZEN strategy to retrieve some aerosol properties like AOD. The retrieved AOD shows a high correlation with respect to independent values obtained from a co-located AERONET CE318 photometer, with determination coefficients (r2) of 0.86, 0.85, 0.79 and 0.72 for 440, 500, 675 and 870 nm, respectively, and finding uncertainties between 0.02 and 0.03 with respect to the AERONET values. Finally, the retrieval of other aerosol properties, like aerosol volume concentration for total, fine and coarse modes (VCT, VCF and VCC, respectively), is also explored. The comparison against independent values from AERONET presents r2 values of 0.57, 0.56 and 0.66 and uncertainties of 0.009, 0.016 and 0.02 µm3 µm−2 for VCT, VCF and VCC, respectively.

Comparison of the Performance of the GRASP and MERRA2 Models in Reproducing Tropospheric Aerosol Layers

Two approaches, based on Generalized Retrieval of Aerosol and Surface Properties (GRASP) and Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) models, are investigated for reproducing aerosol layers in the troposphere. The GRASP algorithm is supplied with synergistic LIDAR and sunphotometer measurements to obtain aerosol extinction profiles. MERRA-2 is an atmospheric reanalysis coupling model that includes an external mixture of sea salt, dust, organic carbon, black carbon, and sulfate aerosols. A data set from Racibórz observatory, obtained with LIDAR and a sunphotometer in the 2017–2020 period, is analysed with GRASP along with the closest grid point data given by MERRA-2. The models demonstrate satisfactory agreement, yet some discrepancies were observed, indicating the presence of biases. For vertically integrated profiles, the correlation coefficient (R) between aerosol optical thickness was calculated to be 0.84, indicating a strong linear relationship. The Pearson correlation coefficient calculated between profiles for the selected altitude sectors varies between 0.428 and 0.824, indicating moderate to good agreement at all altitudes. GRASP shows denser aerosol layers in the mid-troposphere, while MERRA-2 gives higher aerosol extinctions throughout the high troposphere to low stratosphere region. Moreover, GRASP does not provide vertical variability in the extinction profile near the ground, due to a lack of data in the LIDAR’s incomplete overlap range. Lastly, the aerosol layer identification and type recognition are validated with statistical analysis of air mass backward trajectories with endpoints spatially and temporally collocated with individual identified layers. These reveal potential source regions that are located within areas known to be significant sources for the different identified aerosol types.

A robust and flexible satellite aerosol retrieval algorithm for multi-angle polarimetric measurements with physics-informed deep learning method

The multi-angle polarimetric (MAP) satellite measurements provide abundant information concerning aerosol optical/microphysical properties. In this study, we present a robust and flexible aerosol retrieval algorithm for MAP measurements based on physics-informed deep learning (PDL) method. Different from optimized inversion that needs iterative Radiative Transfer (RT) calculations of all the unknowns, the PDL method can model the whole MAP observations with each retrieved aerosol parameter separately with the pre-training of RT simulations. Furthermore, the training of PDL can make full use of the prior information from ground-based aerosol inversions and satellite surface products, and provides an effective constraint to avoid unphysical values. To examine performance of PDL algorithm, we retrieve aerosols over eastern China from POLDER-3 measurements during 2007–2009. Comparison with AERONET products shows high correlations (R > 0.91) for both POLDER-3 PDL Aerosol Optical Depth (AOD) and fine AOD. Despite lower correlations caused by a small portion of poor retrievals, PDL coarse AOD and Single Scattering Albdeo (SSA) is very consistent with AERONET results. Also, PDL retrievals perform well as the best estimates of optimized methods such as GRASP (Generalized Retrieval of Aerosol and Surface Properties). With an outstanding performance in accuracy and efficiency, the flexible PDL algorithm exhibits great potential for operational retrieval of MAP satellite measurements.

Concept, absolute calibration, and validation of a new benchtop laser imaging polar nephelometer

Abstract. Polar nephelometers provide in situ measurements of aerosol angular light scattering and play an essential role in validating numerically calculated phase functions or inversion algorithms used in space-borne and land-based aerosol remote sensing. In this study, we present a prototype of a new polar nephelometer called uNeph. The instrument is designed to measure the phase function, F11, and polarized phase function, -F12/F11, over the scattering range of around 5 to 175∘, with an angular resolution of 1∘ at a wavelength of 532 nm. In this work, we present details of the data processing procedures and instrument calibration approaches. uNeph was validated in a laboratory setting using monodisperse polystyrene latex (PSL) and di-ethyl-hexyl-sebacate (DEHS) aerosol particles over a variety of sizes ranging from 200 to 800 nm. An error model was developed, and the level of agreement between the uNeph measurements and Mie theory was found to be consistent within the uncertainties in the measurements and the uncertainties in the input parameters for the theoretical calculations. The estimated measurement errors were between 5 % and 10 % (relative) for F11 and smaller than ∼ 0.1 (absolute) for -F12/F11. Additionally, by applying the Generalized Retrieval of Aerosol and Surface Properties (GRASP) inversion algorithm to the measurements conducted with broad unimodal DEHS aerosol particles, the volume concentration, size distribution, and refractive index of the ensemble of aerosol particles were accurately retrieved. This paper demonstrates that the uNeph prototype can be used to conduct accurate measurements of aerosol phase function and polarized phase function and to retrieve aerosol properties through inversion algorithms.

Direct aerosol optical depth retrievals using MODIS reflectance data and machine learning over East Asia

Anthropogenic aerosols have rapidly increased since the industrial revolution and are harmful to human health. Moderate Resolution Imaging Spectroradiometer (MODIS) data are critical for retrieving aerosol properties worldwide. However, current MODIS aerosol optical depth (AOD) products require extensive computations and a precalculated lookup table. This study proposes assumption-free high-resolution AOD retrieval models based on the light gradient boosting machine method using MODIS data and ground-based observations over East Asia. The models were developed with three spatial resolutions: 250 m, 500 m, and 1 km. The results showed that 77.8% of the 250 m AOD values were within the MODIS expected error (EE) range, while 76.5%, 76.3%, and 70.08% of the 500 m, 1 km, and Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD values were within the EE range, respectively. Furthermore, an analysis of the time series and detailed spatial distribution of the proposed model-derived AOD based on data from the Korea–United States Air Quality campaign demonstrated the excellent quality of the 250 m AOD via further validation using a spatially independent dataset. The Shapley Additive exPlanations analysis identified the sensor zenith angle and top-of-atmosphere reflectance of the blue band as the key contributors to the models. In addition, while MAIAC has limited spatial coverage, the spatial frequency of the proposed direct AOD retrieval was nearly 1.5-times higher than that of the MAIAC AOD. Our findings confirmed that machine learning-based high-resolution AOD estimates can be obtained using only satellite data.

The classification of atmospheric hydrometeors and aerosols from the EarthCARE radar and lidar: the A-TC, C-TC and AC-TC products

Abstract. The EarthCARE mission aims to probe the Earth's atmosphere by measuring cloud and aerosol profiles using its active instruments, the Cloud Profiling Radar (CPR) and ATmospheric LIDar (ATLID). The correct identification of hydrometeors and aerosols from atmospheric profiles is an important step in retrieving the properties of clouds, aerosols and precipitation. Ambiguities in the nature of atmospheric targets can be removed using the synergy of collocated radar and lidar measurements, which is based on the complementary spectral response of radar and lidar relative to atmospheric targets present in the profiles. The instruments are sensitive to different parts of the particle size distribution and provide independent but overlapping information in optical and microwave wavelengths. ATLID is sensitive to aerosols and small cloud particles, and CPR is sensitive to large ice particles, snowflakes and raindrops. It is therefore possible to better classify atmospheric targets when collocated radar and lidar measurements exist compared to using a single instrument. The cloud phase, precipitation and aerosol type within the column sampled by the two instruments can then be identified. ATLID-CPR target classification (AC-TC) is the product created for this purpose by combining the ATLID target classification (A-TC) and CPR target classification (C-TC). AC-TC is crucial for the subsequent synergistic retrieval of cloud, aerosol and precipitation properties. AC-TC builds upon previous target classifications using CloudSat and CALIPSO synergy while providing richer target classification using the enhanced capabilities of EarthCARE's instruments, specifically CPR's Doppler velocity measurements to distinguish snow and rimed snow from ice clouds and ATLID's lidar ratio measurements to objectively discriminate between different aerosol species and optically thin ice clouds. In this paper, we first describe how the single-instrument A-TC and C-TC products are derived from ATLID and CPR measurements. Then the AC-TC product, which combines the A-TC and C-TC classifications using a synergistic decision matrix, is presented. Simulated EarthCARE observations based on combined cloud-resolving and aerosol model data are used to test the processors generating the target classifications. Finally, the target classifications are evaluated by quantifying the fractions of ice and snow, liquid clouds, rain, and aerosols in the atmosphere that can be successfully identified by each instrument and their synergy. We show that radar–lidar synergy helps better detect ice and snow, with ATLID detecting radiatively important optically thin cirrus and cloud tops, while CPR penetrates most deep and highly concentrated ice clouds. The detection of rain and drizzle is entirely due to C-TC, while that of liquid clouds and aerosols is due to A-TC. The evaluation also shows that simple assumptions can be made to compensate for when the instruments are obscured by extinction (ATLID) or surface clutter and multiple scattering (CPR); this allows for the recovery of the majority of liquid cloud not detected by the active instruments.

Instantaneous aerosol and surface retrieval using satellites in geostationary orbit (iAERUS-GEO) – estimation of 15 min aerosol optical depth from MSG/SEVIRI and evaluation with reference data

Abstract. Geostationary meteorological satellites are unique tools to monitor atmospheric aerosols from space. The observation of the Earth several times per hour allows these types of imaging systems to provide high-temporal-resolution observations of these suspended particles, which are of interest for research and operational topics, including climate, air quality, numerical weather prediction, and volcanic risk management. However, some challenges need to be addressed to achieve the sub-daily retrieval of aerosol properties mainly due to the varying sensitivity of geostationary imagers to aerosols during the day. In this article we propose a new algorithm named iAERUS-GEO (instantaneous Aerosol and surfacE Retrieval Using Satellites in GEOstationary orbit) that estimates the diurnal evolution of aerosol optical depth (AOD) over land and ocean from the Meteosat Second Generation (MSG) satellite. This is achieved by the use of an optimal-estimation method combined with several aerosol models and other features, including the daily retrieval of the surface reflectance directionality using Kalman filtering. AOD estimates provided by iAERUS-GEO every 15 min – the acquisition frequency of the Spinning Enhanced Visible InfraRed Imager (SEVIRI) on MSG – are assessed with collocated reference aerosol observations. First, comparison to AERONET ground-based data proves the overall satisfactory accuracy of iAERUS-GEO over land, with the exception of some higher biases found over bright surfaces and for high scattering angles. The confidence measure provided by iAERUS-GEO is proved useful to filter these less satisfactory retrievals that generally arise due to a low information content on aerosols provided by SEVIRI. Second, comparison to the GRASP/POLDER satellite product shows similar scores for the two aerosol data sets, with a significantly larger number of retrievals for iAERUS-GEO. This added value – which we illustrate here by inspecting the sub-daily variation in AOD over selected regions – allows geostationary satellites to break the temporal barrier set by traditional aerosol remote sensing from the low Earth orbit. Furthermore, the aerosol retrievals presented in this work are expected to be improved in the near future thanks to the enhanced sensing capabilities of the upcoming Meteosat Third Generation Imager mission.

The PACE-MAPP algorithm: Simultaneous aerosol and ocean polarimeter products using coupled atmosphere-ocean vector radiative transfer

We describe the PACE-MAPP algorithm that simultaneously retrieves aerosol and ocean optical parameters using multiangle and multispectral polarimeter measurements from the SPEXone, Hyper-Angular Rainbow Polarimeter 2 (HARP2), and Ocean Color Instrument (OCI) instruments onboard the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observing system. PACE-MAPP is adapted from the Research Scanning Polarimeter (RSP) Microphysical Aerosol Properties from Polarimetry (RSP-MAPP) algorithm. The PACE-MAPP algorithm uses a coupled vector radiative transfer model such that the atmosphere and ocean are always considered together as one system. Consequently, this physically consistent treatment of the system across the ultraviolet, (UV: 300–400 nm), visible (VIS: 400–700 nm), near-infrared (NIR: 700–1100 nm), and shortwave infrared (SWIR: 1100–2400 nm) spectral bands ensures that negative water-leaving radiances do not occur. PACE-MAPP uses optimal estimation to simultaneously characterize the optical and microphysical properties of the atmosphere’s aerosol and ocean constituents, find the optimal solution, and evaluate the uncertainties of each parameter. This coupled approach, together with multiangle, multispectral polarimeter measurements, enables retrievals of aerosol and water properties across the Earth’s oceans. The PACE-MAPP algorithm provides aerosol and ocean products for both the open ocean and coastal areas and is designed to be accurate, modular, and efficient by using fast neural networks that replace the time-consuming vector radiative transfer calculations in the forward model. We provide an overview of the PACE-MAPP framework and quantify its expected retrieval performance on simulated PACE-like data using a bimodal aerosol model for observations of fine-mode absorbing aerosols and coarse-mode sea salt particles. We also quantify its performance for observations over the ocean of dust-laden scenes using a trimodal aerosol model that incorporates non-spherical coarse-mode dust particles. Lastly, PACE-MAPP’s modular capabilities are described, and we discuss plans to implement a new ocean bio-optical model that uses a mixture of coated and uncoated particles, as well as a thin cirrus model for detecting and correcting for sub-visual ice clouds.

The retrieval of aerosol optical properties based on a random forest machine learning approach: Exploration of geostationary satellite images

Aerosol optical properties are among the most fundamental parameters in atmospheric environmental studies. Satellite aerosols retrievals that are based on deep learning or machine learning approach have been widely discussed in remote sensing studies, but the flexible random forest (RF) model has not received much attention in the retrieval of geostationary satellite, like Himawari-8. Thus, the Himawari-8 aerosol retrieval achieved by RF model requires further investigation and optimization. Based on the radiative transfer equation, this study proposed a RF model driven by a differential operator, which quantifies a simple linear relationship between aerosol optical depth (AOD) and top-of-atmosphere (TOA) reflectance enhancement. The spectral information of aerosols is achieved by independent TOA reflectance comparison between images rather than one result from multiple band synthesis. The method allows simple feature inputs and shows weak dependence on auxiliary data. It also achieves simultaneous retrievals over different surfaces and maintains mathematical correlation between spectral AODs and Angstrom Exponents (AE). The model performance was evaluated using a series of comprehensive temporal and spatial validation analyses. A sample-based tenfold cross-validation (10-CV) shows that the new method can simultaneously improve the estimation of aerosol properties, with considerably high correlation coefficients (R2) of 0.85 for AODs at the 0.50 μm wavelengths, a mean absolute error (MAE) of 0.08, a root mean square error (RMSE) of 0.13 and >70% of the samples fell within the AOD expected error (EE). The high accuracy of the spectral AOD retrievals also exhibits good performance on AE calculations, with at least 2/3 of the samples falling within the EE. The site based 10-CV also evaluates the spatial predictions on AODs at the 0.50 μm wavelength, with R2 of 0.67, MAE of 0.12 and RMSE of 0.18. It also has outperformed the Himawari operational aerosol products and appeared to be comparable to other popular machine learning models with better AE retrievals in some typical regions. Two typical regional pollution cases also highlight the advantages of the new aerosol monitoring approach. The 5 km resolution aerosol retrievals exhibit good spatial coverage and performance when describing the regional pollution levels and types. The proposed method improves the performance of RF in retrieving aerosol properties from geostationary satellites and also offers a new prospective for aerosol remote sensing using machine learning approaches.

Remote sensing of aerosol water fraction, dry size distribution and soluble fraction using multi-angle, multi-spectral polarimetry

Abstract. A framework to infer volume water fraction, soluble fraction and dry size distributions of fine-mode aerosol from multi-angle, multi-spectral polarimetry retrievals of column-averaged ambient aerosol properties is presented. The method is applied to observations of the Research Scanning Polarimeter (RSP) obtained during two NASA aircraft campaigns, namely the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) and the Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex). All aerosol retrievals are statistically evaluated using in situ data. Volume water fraction is inferred from the retrieved ambient real part of the refractive index, assuming a dry refractive index of 1.54 and by applying a volume mixing rule to obtain the effective ambient refractive index. The uncertainties in inferred volume water fraction resulting from this simplified model are discussed and estimated to be lower than 0.2 and decreasing with increasing volume water fraction. The daily mean retrieved volume water fractions correlate well with the in situ values with a mean absolute difference of 0.09. Polarimeter-retrieved ambient effective radius for daily data is shown to increase as a function of volume water fraction as expected. Furthermore, the effective variance of the size distributions also increases with increasing effective radius, which we show is consistent with an external mixture of soluble and insoluble aerosol. The relative variations of effective radius and variance over an observation period are then used to estimate the soluble fraction of the aerosol. Daily results of soluble fraction correlate well with in situ-observed sulfate mass fraction with a correlation coefficient of 0.79. Subsequently, inferred water and soluble fractions are used to derive dry fine-mode size distributions from their ambient counterparts. While dry effective radii obtained in situ and from RSP show similar ranges, in situ values are generally substantially smaller during the ACTIVATE deployments, which may be due to biases in RSP retrievals or in the in situ observations, or both. Both RSP and in situ observations indicate the dominance of aerosol with low hygroscopicity during the ACTIVATE and CAMP2Ex campaigns. Furthermore, RSP indicates a high degree of external mixing of particles with low and high hygroscopicity. These retrievals of fine-mode water volume fraction and soluble fraction may be used for the evaluation of water uptake in atmospheric models. Furthermore, the framework allows us to estimate the variation in the concentration of fine-mode aerosol larger than a specific dry radius limit, which can be used as a proxy for the variation in cloud condensation nucleus concentrations. This framework may be applied to multi-angle, multi-spectral satellite data expected to be available in the near future.

Efficient single-scattering look-up table for lidar and polarimeter water cloud studies.

Combined lidar and polarimeter retrievals of aerosol, cloud, and ocean microphysical properties involve single-scattering cloud calculations that are time consuming. We create a look-up table to speed up these calculations for water droplets in the atmosphere. In our new Lorenz-Mie look-up table we tabulate the light scattering by an ensemble of homogeneous isotropic spheres at wavelengths starting from 0.35 µm. The look-up table covers liquid water cloud particles with radii in the range of 0.001-500 µm while gaining an increase of up to 104 in computational speed. The covered complex refractive indices range from 1.25 to 1.36 for the real part and from 0 to 0.001 for the imaginary part. We show that we can precisely compute inherent optical properties for the particle size distributions ranging up to 100 µm for the effective radius and up to 0.6 for the effective variance. We test wavelengths from 0.35 to 2.3 µm and find that the elements of the normalized scattering matrix as well as the asymmetry parameter, the absorption, backscatter, extinction, and scattering coefficients are precise to within 1% for 96.7%-100% of cases depending on the inherent optical property. We also provide an example of using the look-up table with in situ measurements to determine agreement with remote sensing. The table together with C++, Fortran, MATLAB, and Python codes to interpolate the complex refractive index and apply different particle size distributions are freely available online.

Evaluation of the vertically-resolved aerosol radiative effect on shortwave and longwave ranges using sun-sky photometer and ceilometer measurements

The aerosol radiative effect (ARE) is one of the atmospheric components still affected by large uncertainty. One of the causes is related to the fact that the longwave (LW) component is usually neglected, even though it is necessary for an accurate quantification of the ARE together with the shortwave component (SW). In this study we have developed a methodology based on the GAME (Global Atmospheric Model) radiative transfer model (RTM) that allows to obtain the radiative effect of the atmospheric aerosol for both spectral ranges in an automated way. The microphysical and optical properties necessary to feed the RTM have been obtained through the GRASP (Generalized Retrieval of Aerosol and Surface Properties) algorithm, with the combination of ceilometer and sun-sky photometer data. Data measured in Granada (Spain) during 2017 have been used for the evaluation and implementation of this methodology. According to the results, the ARE in the SW spectral range (ARESW) varies between 0 and − 50 Wm−2 for most of the data, whereas the ARE in the LW range (ARELW) varies between 0 and 5 Wm−2, at heights near the surface. In general, the obtained results agree with those found in the literature, with negative values in the SW range (cooling effect) and positive values in the LW (heating effect). The seasonal analysis shows that, for both components, the ARE is more important during the spring and summer seasons, when the aerosol load is greater, as expected. The analysis of the aerosol heating rate (AHR) shows positive values in the SW and negative values in the LW range. The majority of the AHRSW data varies between 0 and 1 Kd−1 during the year whereas the AHRLW does it between 0 and − 0.15 Kd−1. The seasonal analysis of the AHR shows that the greater monthly average values are found during spring, however there is not much variability along the year, with the exception of February, under the effects of an extreme dust intrusion. The mineral dust particles in this event cause an ARESW of −130 Wm−2 and an ARELW of 23 Wm−2 (ARELW/ARESW = 17%), thus pointing out that the LW component should not be neglected for coarse mode particles. Additionally, it is observed that the vertical distribution of the aerosol layers strongly influences the ARE and the AHR obtained profiles, affecting the way the atmospheric cooling/heating occurs in the vertical coordinate.

Algorithm for vertical distribution of boundary layer aerosol components in remote-sensing data

Abstract. The vertical distribution of atmospheric aerosol components is vital to the estimation of radiative forcing and the catalysis of atmospheric photochemical processes. Based on the synergy of ground-based lidar and sun-photometer in Generalized Aerosol Retrieval from Radiometer and Lidar Combined data (GARRLiC), this paper developed a new algorithm to get the vertical mass concentration profiles of fine-mode aerosol components for the first time. Retrieval of aerosol properties was achieved based on the sky radiance at multiple scatter angles, total optical depth (TOD) at 440, 675, 870, and 1020 nm, and lidar signals at 532 and 1064 nm. In addition, the internal mixing model and normalized volume size distribution (VSD) model were established according to the absorption and water solubility of the aerosol components, to separate the profiles of black carbon (BC), water-insoluble organic matter (WIOM), water-soluble organic matter (WSOM), ammonium nitrate-like (AN), and fine aerosol water (AW) content. Results showed a reasonable vertical distribution of aerosol components compared with in situ observations and reanalysis data. The estimated and observed BC concentrations matched well with a correlation coefficient up to 0.91, while there was an evident overestimation of organic matter (OM = WIOM + WSOM, NMB = 0.98). Moreover, the retrieved AN concentrations were closer to the simulated results (R = 0.85), especially in polluted conditions. The BC and OM correlations were relatively weaker, with a correlation coefficient of ∼ 0.5. Besides, the uncertainties caused by the input parameters (i.e., relative humidity (RH), volume concentration, and extinction coefficients) were assessed using the Monte Carlo method. The AN and AW had smaller uncertainties at higher RH. Herein, the proposed algorithm was also applied to remote-sensing measurements in Beijing with two typical cases. In the clean condition with low RH, there were comparable AN and WIOM, but peaking at different altitudes. On the other hand, in the polluted case, AN was dominant and the maximum mass concentration occurred near the surface. We expected that the algorithm could provide a new idea for lidar inversion and promote the development of aerosol component profiles.