Aerosol Robotic Network Data Research Articles

Assessment and Improvement of MISR Angstrom Exponent and Single-Scattering Albedo Products Using AERONET Data in China

Mapping the components, size, and absorbing/scattering properties of particle pollution is of great interest in the environmental and public health fields. Although the Multi-angle Imaging SpectroRadiometer (MISR) can detect a greater number of aerosol microphysical properties than most other spaceborne sensors, the Angstrom exponent (AE) and single-scattering albedo (SSA) products are not widely utilized or as robust as the aerosol optical depth (AOD) product. This study focused on validating MISR AE and SSA data using AErosol RObotic NETwork (AERONET) data for China from 2004 to 2014. The national mean value of the MISR data (1.08) was 0.095 lower than that of the AERONET data. However, the MISR SSA average (0.99) was significantly higher than that of AERONET (0.89). In this study, we developed a method to improve the AE and SSA by narrowing the selection of MISR mixtures via the introduction of the following group thresholds obtained from an 11-year AERONET dataset: minimum and maximum values (for the method of MISR_Imp_All) and the top 10% and bottom 10% of the averaged values (for MISR_Imp_10%). Overall, our improved AE values were closer to the AERONET AE values, and additional samples (MISR_Imp_All: 28.04% and 64.72%, MISR_Imp_10%: 34.11% and 73.13%) had absolute differences of less than 0.1 and 0.3 (defined by the expected error tests, e.g., EE_0.1) compared with the original MISR product (18.46% and 50.23%). For the SSA product, our method also improved the mean, EE_0.05, and EE_0.1 from 0.99, 16.13%, and 56.45% (MISR original product) to 0.96, 40.32%, and 70.97% (MISR_Imp_All), and 0.94, 54.84%, and 90.32% (MISR_Imp_10%), respectively.

Trend Analysis of Aerosol Optical Depth and &#197ngstr&#246m Exponent Anomaly over East Africa

Trend analysis of atmospheric aerosols enhances confidence in the evaluation of both direct and indirect effects of aerosols on regional climate change. To comprehensively achieve this over East Africa, it’s important to understand aerosols temporal characteristics over well selected sites namely Nairobi (1°S, 36°E), Mbita (0°S, 34°E), Mau Forest (0.0°S - 0.6°S; 35.1°E - 35.7°E), Malindi (2°S, 40°E), Mount Kilimanjaro (3°S, 37°E) and Kampala (0°N, 32.1°E). In this context, trend analysis (annual (in Aerosol Optical Depth (AOD) at 550 nm and &#197ngstr&#246m Exponent Anomaly (&#197EA) at 470 - 660 nm) and seasonal (AOD)) from Moderate Resolution Imaging Spectroradiometer (MODIS) were performed following the weighted least squares (WLS) fitting method for the period 2000 to 2013. The MODIS AOD annual trends were ground-truthed by AErosol RObotic NETwork (AERONET) data. Tropical Rainfall Measurement Mission (TRMM) was utilized to derive rainfall rates (RR) in order to assess its influence on the observed aerosol temporal characteristics. The derived annual AOD trends utilizing MODIS and AERONET data were consistent with each other. However, monthly AOD and RR were found to be negatively correlated over Nairobi, Mbita, Mau forest complex and Malindi. There was no clear relationship between the two trends over Kampala and Mount Kilimanjaro, which may imply the role of aerosols in cloud modulation and hence RR received. Seasonality is evident between AOD and &#197EA annual trends as these quantities were observed to be modulated by RR. AOD was observed to decrease over East Africa except Nairobi during the study period as a result of RR during the study period. Unlike the other study sites, Nairobi shows positive trends in AOD that may be attributed to increasing populace and fossil fuel, vehicular-industrial emission and biomass and refuse burning during the study period. Negative trends over the rest of the study sites were associated to rain washout. The AOD and &#197EA derived annual trends were found to meet the statistical significance of 95% confidence level over each study site.

Geostatistical Methods for Filling Gaps in Level-3 Monthly-Mean Aerosol Optical Depth Data from Multi-Angle Imaging SpectroRadiometer

ABSTRACTThe Aerosol Optical Depth (AOD) retrieved from satellite remote sensing measurements such as from MISR and MODIS, both onboard the Terra platform, are widely used for studying regional and global patterns of aerosol loading. Aerosol products from these sensors are also used for analyzing feedbacks and relationship between aerosols and climatic variables including clouds, precipitation, and radiation fluxes. Several statistical techniques leading to the understanding of such relationships, including empirical orthogonal function and temporal trend extraction methods, require spatially complete AOD data records. Inherent to remote sensing of aerosols, cloud cover significantly affects aerosol retrievals and results in missing data across the AOD products. This paper demonstrates widely-used geostatistical techniques, such as Co-Kriging (CK) and Regression Kriging (RK), for spatially-filling missing data in the MISR AOD product for the period 2001–2013. Among the unique characteristics of this data-filling algorithm is that it utilizes additional AOD information obtained from MODIS. The mean accuracy of the predicted MISR AOD using CK method is estimated to be 0.05, globally. The gap-filled MISR AOD data are also compared with 131 ground-based Aerosol Robotic Network (AERONET) stations, located around the world. It is found that Root Mean Squared Error of the gap-filled AOD dataset and the original MISR AOD product with respect to AERONET data are 0.143. The gap-filled AOD dataset can be used in applications where the presence of missing values is undesirable such as for global/regional aerosol variability and trend analysis.

Aerosol data assimilation in the chemical transport model MOCAGE during the TRAQA/ChArMEx campaign: aerosol optical depth

Abstract. In this study, we describe the development of the aerosol optical depth (AOD) assimilation module in the chemistry transport model (CTM) MOCAGE (Modèle de Chimie Atmosphérique à Grande Echelle). Our goal is to assimilate the spatially averaged 2-D column AOD data from the National Aeronautics and Space Administration (NASA) Moderate-resolution Imaging Spectroradiometer (MODIS) instrument, and to estimate improvements in a 3-D CTM assimilation run compared to a direct model run. Our assimilation system uses 3-D-FGAT (first guess at appropriate time) as an assimilation method and the total 3-D aerosol concentration as a control variable. In order to have an extensive validation dataset, we carried out our experiment in the northern summer of 2012 when the pre-ChArMEx (CHemistry and AeRosol MEditerranean EXperiment) field campaign TRAQA (TRAnsport à longue distance et Qualité de l'Air dans le bassin méditerranéen) took place in the western Mediterranean basin. The assimilated model run is evaluated independently against a range of aerosol properties (2-D and 3-D) measured by in situ instruments (the TRAQA size-resolved balloon and aircraft measurements), the satellite Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument and ground-based instruments from the Aerosol Robotic Network (AERONET) network. The evaluation demonstrates that the AOD assimilation greatly improves aerosol representation in the model. For example, the comparison of the direct and the assimilated model run with AERONET data shows that the assimilation increased the correlation (from 0.74 to 0.88), and reduced the bias (from 0.050 to 0.006) and the root mean square error in the AOD (from 0.12 to 0.07). When compared to the 3-D concentration data obtained by the in situ aircraft and balloon measurements, the assimilation consistently improves the model output. The best results as expected occur when the shape of the vertical profile is correctly simulated by the direct model. We also examine how the assimilation can influence the modelled aerosol vertical distribution. The results show that a 2-D continuous AOD assimilation can improve the 3-D vertical profile, as a result of differential horizontal transport of aerosols in the model.

Reducing multisensor satellite monthly mean aerosol optical depth uncertainty: 1. Objective assessment of current AERONET locations.

Various space-based sensors have been designed and corresponding algorithms developed to retrieve aerosol optical depth (AOD), the very basic aerosol optical property, yet considerable disagreement still exists across these different satellite data sets. Surface-based observations aim to provide ground truth for validating satellite data; hence, their deployment locations should preferably contain as much spatial information as possible, i.e., high spatial representativeness. Using a novel Ensemble Kalman Filter (EnKF)-based approach, we objectively evaluate the spatial representativeness of current Aerosol Robotic Network (AERONET) sites. Multisensor monthly mean AOD data sets from Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, Sea-viewing Wide Field-of-view Sensor, Ozone Monitoring Instrument, and Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar are combined into a 605-member ensemble, and AERONET data are considered as the observations to be assimilated into this ensemble using the EnKF. The assessment is made by comparing the analysis error variance (that has been constrained by ground-based measurements), with the background error variance (based on satellite data alone). Results show that the total uncertainty is reduced by ~27% on average and could reach above 50% over certain places. The uncertainty reduction pattern also has distinct seasonal patterns, corresponding to the spatial distribution of seasonally varying aerosol types, such as dust in the spring for Northern Hemisphere and biomass burning in the fall for Southern Hemisphere. Dust and biomass burning sites have the highest spatial representativeness, rural and oceanic sites can also represent moderate spatial information, whereas the representativeness of urban sites is relatively localized. A spatial score ranging from 1 to 3 is assigned to each AERONET site based on the uncertainty reduction, indicating its representativeness level.

Analysis of aerosol absorption properties and transport over North Africa and the Middle East using AERONET data

Abstract. In this paper particle categorization and absorption properties were discussed to understand transport mechanisms at different geographic locations and possible radiative impacts on climate. The long-term Aerosol Robotic Network (AERONET) data set (1999–2015) is used to estimate aerosol optical depth (AOD), single scattering albedo (SSA), and the absorption Ångström exponent (αabs) at eight locations in North Africa and the Middle East. Average variation in SSA is calculated at four wavelengths (440, 675, 870, and 1020 nm), and the relationship between aerosol absorption and physical properties is used to infer dominant aerosol types at different locations. It was found that seasonality and geographic location play a major role in identifying dominant aerosol types at each location. Analyzing aerosol characteristics among different sites using AERONET Version 2, Level 2.0 data retrievals and the Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT) backward trajectories shows possible aerosol particle transport among different locations indicating the importance of understanding transport mechanisms in identifying aerosol sources.

Mixing weight determination for retrieving optical properties of polluted dust with MODIS and AERONET data

In this study, an approach in determining effective mixing weight of soot aggregates from dust–soot aerosols is proposed to improve the accuracy of retrieving properties of polluted dusts by means of satellite remote sensing. Based on a pre-computed database containing several variables (such as wavelength, refractive index, soot mixing weight, surface reflectivity, observation geometries and aerosol optical depth (AOD)), the fan-shaped look-up tables can be drawn out accordingly for determining the mixing weights, AOD and single scattering albedo (SSA) of polluted dusts simultaneously with auxiliary regional dust properties and surface reflectivity. To validate the performance of the approach in this study, 6 cases study of polluted dusts (dust–soot aerosols) in Lower Egypt and Israel were examined with the ground-based measurements through AErosol RObotic NETwork (AERONET). The results show that the mean absolute differences could be reduced from 32.95% to 6.56% in AOD and from 2.67% to 0.83% in SSA retrievals for MODIS aerosol products when referenced to AERONET measurements, demonstrating the soundness of the proposed approach under different levels of dust loading, mixing weight and surface reflectivity. Furthermore, the developed algorithm is capable of providing the spatial distribution of the mixing weights and removing the requirement to assume that the dust plume properties are uniform. The case study further shows the spatially variant dust–soot mixing weight would improve the retrieval accuracy in AODmixture and SSAmixture about 10.0% and 1.4% respectively.

Potential of polarization lidar to provide profiles of CCN- and INP-relevant aerosol parameters

Abstract. We investigate the potential of polarization lidar to provide vertical profiles of aerosol parameters from which cloud condensation nucleus (CCN) and ice nucleating particle (INP) number concentrations can be estimated. We show that height profiles of particle number concentrations n50, dry considering dry aerosol particles with radius > 50 nm (reservoir of CCN in the case of marine and continental non-desert aerosols), n100, dry (particles with dry radius > 100 nm, reservoir of desert dust CCN), and of n250, dry (particles with dry radius > 250 nm, reservoir of favorable INP), as well as profiles of the particle surface area concentration sdry (used in INP parameterizations) can be retrieved from lidar-derived aerosol extinction coefficients σ with relative uncertainties of a factor of 1.5–2 in the case of n50, dry and n100, dry and of about 25–50 % in the case of n250, dry and sdry. Of key importance is the potential of polarization lidar to distinguish and separate the optical properties of desert aerosols from non-desert aerosol such as continental and marine particles. We investigate the relationship between σ, measured at ambient atmospheric conditions, and n50, dry for marine and continental aerosols, n100, dry for desert dust particles, and n250, dry and sdry for three aerosol types (desert, non-desert continental, marine) and for the main lidar wavelengths of 355, 532, and 1064 nm. Our study is based on multiyear Aerosol Robotic Network (AERONET) photometer observations of aerosol optical thickness and column-integrated particle size distribution at Leipzig, Germany, and Limassol, Cyprus, which cover all realistic aerosol mixtures. We further include AERONET data from field campaigns in Morocco, Cabo Verde, and Barbados, which provide pure dust and pure marine aerosol scenarios. By means of a simple CCN parameterization (with n50, dry or n100, dry as input) and available INP parameterization schemes (with n250, dry and sdry as input) we finally compute profiles of the CCN-relevant particle number concentration nCCN and the INP number concentration nINP. We apply the method to a lidar observation of a heavy dust outbreak crossing Cyprus and a case dominated by continental aerosol pollution.

Seasonal Variations of Aerosol Optical Properties and Identification of Different Aerosol Types Based on AERONET Data over Sub-Sahara West-Africa

The uncertainty in the quantification of aerosol properties such as concentration, size, and composition, spatially and temporally makes regional studies important. Therefore, this study presents seasonal variations of aerosol optical properties over Ilorin (8&#17632'N, 4&#17634'E), Nigeria. Long-term (1998-2013) records of aerosol optical depth (AOD) and angstrom exponent α, from ground-based Aerosol Robotic Network (AERONET) are used to study the seasonal variability, characteristics and types of aerosol. The study showed that seasonal variations (Harmattan and Summer) result in different aerosol concentration, characteristics, and types. The magnitude and sensitivity of AOD to wavelength are found low in Summer with significant increase during Saharan dust transport season (Harmattan). The average mean AODs are 0.73 ± 0.50, 0.97 ± 0.52 and 0.46 ± 0.29 with corresponding mean angstrom of 0.66 ± 0.36, 0.68 ± 0.34, and 0.64 ± 0.37 for the entire period, Harmattan and Summer seasons. High frequency of occurrence of angstrom exponent below 1 (78% and 81%) which were observed during Harmattan and Summer indicates that the particles are generally coarse in mode. The results revealed that for both Harmattan and Summer seasons, the dominant aerosol was dust (DA) with frequency of occurrence of 82% and 79%. However, mixed aerosol (MIXA) (14.4%) is the second dominant case during Harmattan while in Summer maritime aerosol (MA) (9.1%) associated with transport due to southwesterly trade wind is the second dominant aerosol. This conclusion is supported by size distribution data for the study site which showed that large volume of aerosol particle size are enclosed in largely coarse mode range in all seasons. A 7-day back trajectory seasonal frequency plot sourced from the Hysplit Single Particles Lagrangian Integrated Trajectory model (Hysplit_4 model) shows that dust are transported from the Sahara during north-easterly trade wind flow while the observed marine aerosols are conveyed by the southwesterly trade wind influences to the study site.

A new method of satellite-based haze aerosol monitoring over the North China Plain and a comparison with MODIS Collection 6 aerosol products

With worldwide urbanization, hazy weather has been increasingly frequent, especially in the North China Plain. However, haze aerosol monitoring remains a challenge. In this paper, MODerate resolution Imaging Spectroradiometer (MODIS) measurements were used to develop an enhanced haze aerosol retrieval algorithm (EHARA). This method can work not only on hazy days but also on normal weather days. Based on 12-year (2002–2014) Aerosol Robotic Network (AERONET) aerosol property data, empirical single scattering albedo (SSA) and asymmetry factor (AF) values were chosen to assist haze aerosol retrieval. For validation, EHARA aerosol optical thickness (AOT) values, along with MODIS Collection 6 (C6) dark-pixel and deep blue aerosol products, were compared with AERONET data. The results show that the EHARA can achieve greater AOT spatial coverage under hazy conditions with a high accuracy (73% within error range) and work a higher resolution (1-km). Additionally, this paper presents a comprehensive discussion of the differences between and limitations of the EHARA and the MODIS C6 DT land algorithms.

On the Importance of Aerosol Composition for Estimating Incoming Solar Radiation: Focus on the Western African Stations of Dakar and Niamey during the Dry Season

The article investigates the impact of aerosol composition on the estimation of the downwelling surface shortwave flux (DSSF). This initiative forms part of the efforts to improve the DSSF distributed by the Land Surface Analysis Satellite Application Facility (LSA-SAF). This operational product assumes invariant aerosol properties under clear sky conditions, which can be inaccurate for some regions of the world. This is the case of West Africa, where aerosol activity is not only highly variable due to frequent dust storms but also rich because of the coexistence of different aerosol species. This study was carried out over the West African stations of Dakar and Niamey, which represent different aerosol scenarios. Several dates during the dry season of 2006 were selected and classified into four different day types according to aerosol activity: standard, clean, mixture and dusty days. The diurnal evolution of DSSF and its direct and diffuse components were estimated for the selected dates by the current LSA-SAF algorithm and two other approaches using aerosol measurements from the Aerosol Robotic Network (AERONET) as input. The first alternative approach took the diurnal evolution of the total aerosol optical depth (AOD) into account, assuming a default desert aerosol type. Experiments with this method showed a significant improvement in the estimated DSSF compared to the current LSA-SAF algorithm. For example, root mean square error (RMSE) improved from 170 W/m2 to 50 W/m2 for dusty days in Dakar and from 73 W/m2 to 21 W/m2 for mixture days in Niamey. This improvement resulted from the consideration of a time-varying AOD, which accounted for the rapidly changing aerosol load for these two day types. The second alternative approach tested included consideration of the diurnal variation of the aerosol load and composition. Again, this was done using AERONET data on the fine and coarse aerosol modes, which may be associated with different sized dust particles, sea salt, or soot from biomass burning (depending on the date). This enhanced consideration of the aerosol composition greatly improved the estimation of the diffuse component of the DSSF, further reducing the RMSE during mixture days from 50 W/m2 to less than 10 W/m2. This improvement mainly came from consideration of the right scattering properties of the aerosol particles, which may be significantly different for each aerosol type.

Validation of Long-Term Global Aerosol Climatology Project Optical Thickness Retrievals Using AERONET and MODIS Data

A comprehensive set of monthly mean aerosol optical thickness (AOT) data from coastal and island AErosol RObotic NETwork (AERONET) stations is used to evaluate Global Aerosol Climatology Project (GACP) retrievals for the period 1995–2009 during which contemporaneous GACP and AERONET data were available. To put the GACP performance in broader perspective, we also compare AERONET and MODerate resolution Imaging Spectroradiometer (MODIS) Aqua level-2 data for 2003–2009 using the same methodology. We find that a large mismatch in geographic coverage exists between the satellite and ground-based datasets, with very limited AERONET coverage of open-ocean areas. This is especially true of GACP because of the smaller number of AERONET stations at the early stages of the network development. Monthly mean AOTs from the two over-the-ocean satellite datasets are well-correlated with the ground-based values, the correlation coefficients being 0.81–0.85 for GACP and 0.74–0.79 for MODIS. Regression analyses demonstrate that the GACP mean AOTs are approximately 17%–27% lower than the AERONET values on average, while the MODIS mean AOTs are 5%–25% higher. The regression coefficients are highly dependent on the weighting assumptions (e.g., on the measure of aerosol variability) as well as on the set of AERONET stations used for comparison. Comparison of over-the-land and over-the-ocean MODIS monthly mean AOTs in the vicinity of coastal AERONET stations reveals a significant bias. This may indicate that aerosol amounts in coastal locations can differ significantly from those in adjacent open-ocean areas. Furthermore, the color of coastal waters and peculiarities of coastline meteorological conditions may introduce biases in the GACP AOT retrievals. We conclude that the GACP and MODIS over-the-ocean retrieval algorithms show similar ranges of discrepancy when compared to available coastal and island AERONET stations. The factors mentioned above may limit the performance of the validation procedure and cause us to caution against a direct extrapolation of the presented validation results to the entirety of the GACP dataset.

AERONET data–based determination of aerosol types

Aerosols are among the most interesting topics investigated by researchers because of their complicated characteristics and poor quantification. Moreover, significant uncertainties are associated with changes in the Earth's radiation budget. Previous studies have shown numerous difficulties and challenges in quantifying aerosol influences. In addition, the heterogeneity from aerosol loading and properties, including spatial, temporal, size, and composition features, presents a challenge. In this study, we investigated aerosol characteristics over two regions with different environmental conditions and aerosol sources. The study sites are Penang and Kuching in Malaysia, where a ground–based AErosol RObotic NETwork (AERONET) sun–photometer was deployed. The types of aerosol, such as biomass burning, urban/industrial, marine, and dust aerosols, for both study sites were identified by analyzing aerosol optical depth and angstrom exponent. Seasonal monsoon variation results in different aerosol optical properties, characteristics, and types of aerosols that are dominant in Penang and Kuching. Seasonal monsoon flow trend patterns from a seven–day back–trajectory frequency plotted by the Hybrid Single–Particle Lagrangian Integrated Trajectory model illustrated the distinct origins of trans–boundary aerosol sources. Finally, we improved our findings in Malaysian sites using the AERONET data from Singapore and Indonesia. Similarities in the optical properties of aerosols and the distribution types (referred to as homogeneous aerosol) were observed in the Penang–Singapore and the Kuching–Pontianak sites. The dominant aerosol distribution types were completely different for locations in the western (Penang–Singapore) and eastern (Kuching–Pontianak) parts of the South China Sea. This is a result of spatial and temporal heterogeneity. The spatial and temporal heterogeneities for the western and eastern portions of South China Sea provide information on the natural or anthropogenic processes that take place.

Intercomparison of MODIS, MISR, OMI, and CALIPSO aerosol optical depth retrievals for four locations on the Indo-Gangetic plains and validation against AERONET data

This study provides an intercomparison of aerosol optical depth (AOD) retrievals from satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), Ozone Monitoring Instrument (OMI), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) instrumentation over Karachi, Lahore, Jaipur, and Kanpur between 2007 and 2013, with validation against AOD observations from the ground-based Aerosol Robotic Network (AERONET). Both MODIS Deep Blue (MODISDB) and MODIS Standard (MODISSTD) products were compared with the AERONET products.The MODISSTD–AERONET comparisons revealed a high degree of correlation for the four investigated sites at Karachi, Lahore, Jaipur, and Kanpur, the MODISDB–AERONET comparisons revealed even better correlations, and the MISR–AERONET comparisons also indicated strong correlations, as did the OMI–AERONET comparisons, while the CALIPSO–AERONET comparisons revealed only poor correlations due to the limited number of data points available.We also computed figures for root mean square error (RMSE), mean absolute error (MAE) and root mean bias (RMB). Using AERONET data to validate MODISSTD, MODISDB, MISR, OMI, and CALIPSO data revealed that MODISSTD data was more accurate over vegetated locations than over un-vegetated locations, while MISR data was more accurate over areas close to the ocean than over other areas. The MISR instrument performed better than the other instruments over Karachi and Kanpur, while the MODISSTD AOD retrievals were better than those from the other instruments over Lahore and Jaipur. We also computed the expected error bounds (EEBs) for both MODIS retrievals and found that MODISSTD consistently outperformed MODISDB in all of the investigated areas. High AOD values were observed by the MODISSTD, MODISDB, MISR, and OMI instruments during the summer months (April–August); these ranged from 0.32 to 0.78, possibly due to human activity and biomass burning. In contrast, high AOD values were observed by the CALIPSO instrument between September and December, due to high concentrations of smoke and soot aerosols. The variable monthly AOD figures obtained with different sensors indicate overestimation by MODISSTD, MODISDB, OMI, and CALIPSO instruments over Karachi, Lahore, Jaipur and Kanpur, relative to the AERONET data, but underestimation by the MISR instrument.

Improving satellite-retrieved aerosol microphysical properties using GOCART data

Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) aboard the NASA Earth Observing System's Terra satellite can provide more reliable aerosol optical depth (AOD) and better constraints on particle size (Ångström exponent, or ANG), sphericity, and single-scattering albedo (SSA) than many other satellite instruments. However, many aerosol mixtures pass the algorithm acceptance criteria, yielding a poor constraint, when the particle-type information in the MISR radiances is low, typically at low AOD. We investigate adding value to the MISR aerosol product under these conditions by filtering the list of MISR-retrieved mixtures based on agreement between the mixture ANG and absorbing AOD (AAOD) values, and simulated aerosol properties from the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model. MISR–GOCART ANG difference and AAOD ratio thresholds for applying GOCART constraints were determined based on coincident AOD, ANG, and AAOD measurements from the AErosol RObotic NETwork (AERONET). The results were validated by comparing the adjusted MISR aerosol optical properties over the contiguous USA between 2006 and 2009 with additional AERONET data. The correlation coefficient (r) between the adjusted MISR ANG derived from this study and AERONET improves to 0.45, compared to 0.29 for the MISR Version 22 standard product. The ratio of the adjusted MISR AAOD to AERONET increases to 0.74, compared to 0.5 for the MISR operational retrieval. These improvements occur primarily when AOD < 0.2 for ANG and AOD < 0.5 for AAOD. Spatial and temporal differences among the aerosol optical properties of MISR V22, GOCART, and the adjusted MISR are traced to (1) GOCART underestimation of AOD and ANG in polluted regions; (2) aerosol mixtures lacking in the MISR Version 22 algorithm climatology; (3) low MISR sensitivity to particle type under some conditions; and (4) parameters and thresholds used in our method.

Using single‐scattering albedo spectral curvature to characterize East Asian aerosol mixtures

AbstractSpectral dependence of aerosol single‐scattering albedo (SSA) has been used to infer aerosol composition. In particular, aerosol mixtures dominated by dust absorption will have monotonically increasing SSA with wavelength while that dominated by black carbon absorption has monotonically decreasing SSA spectra. However, by analyzing SSA measured at four wavelengths, 440, 675, 870, and 1020 nm from the Aerosol Robotic Network data set, we find that the SSA spectra over East Asia are frequently peaked at 675 nm. In these cases, we suggest that SSA spectral curvature, defined as the negative of the second derivative of SSA as a function of wavelength, can provide additional information on the composition of these aerosol mixtures. Aerosol SSA spectral curvatures for East Asia during fall and winter are considerably larger than those found in places primarily dominated by biomass burning or dust aerosols. SSA curvature is found to increase as the SSA magnitude decreases. The curvature increases with coarse mode fraction (CMF) to a CMF value of about 0.4, then slightly decreases or remains constant at larger CMF. Mie calculations further verify that the strongest SSA curvature occurs at ~40% dust fraction, with 10% scattering aerosol fraction. The nonmonotonic SSA spectral dependence is likely associated with enhanced absorption in the shortwave by dust, absorption by black carbon at longer wavelengths, and also the flattened absorption optical depth spectral dependence due to the increased particle size.

Estimation of PM<sub>10</sub> concentrations over Seoul using multiple empirical models with AERONET and MODIS data collected during the DRAGON-Asia campaign

Abstract. The performance of various empirical linear models to estimate the concentrations of surface-level particulate matter with a diameter less than 10 μm (PM10) was evaluated using Aerosol Robotic Network (AERONET) sun photometer and Moderate-Resolution Imaging Spectroradiometer (MODIS) data collected in Seoul during the Distributed Regional Aerosol Gridded Observation Network (DRAGON)-Asia campaign from March to May 2012. An observed relationship between the PM10 concentration and the aerosol optical depth (AOD) was accounted for by several parameters in the empirical models, including boundary layer height (BLH), relative humidity (RH), and effective radius of the aerosol size distribution (Reff), which was used here for the first time in empirical modeling. Among various empirical models, the model which incorporates both BLH and Reff showed the highest correlation, which indicates the strong influence of BLH and Reff on the PM10 estimations. Meanwhile, the effect of RH on the relationship between AOD and PM10 appeared to be negligible during the campaign period (spring), when RH is generally low in northeast Asia. A large spatial dependency of the empirical model performance was found by categorizing the locations of the collected data into three different site types, which varied in terms of the distances between instruments and source locations. When both AERONET and MODIS data sets were used in the PM10 estimation, the highest correlations between measured and estimated values (R = 0.76 and 0.76 using AERONET and MODIS data, respectively) were found for the residential area (RA) site type, while the poorest correlations (R = 0.61 and 0.68 using AERONET and MODIS data, respectively) were found for the near-source (NS) site type. Significant seasonal variations of empirical model performances for PM10 estimation were found using the data collected at Yonsei University (one of the DRAGON campaign sites) over a period of 17 months including the DRAGON campaign period. The best correlation between measured and estimated PM10 concentrations (R = 0.81) was found in winter, due to the presence of a stagnant air mass and low BLH conditions, which may have resulted in relatively homogeneous aerosol properties within the BLH. On the other hand, the poorest correlation between measured and estimated PM10 concentrations (R = 0.54) was found in spring, due to the influence of the long-range transport of dust to both within and above the BLH.

AERONET data investigation of the aerosol mixtures over Iasi area, One-year time scale overview

In order to analyze the troposphere dynamics under particular conditions in North-East region of Romania, various types of aerosols chemical compositions have been studied using complementary techniques. Thus, the seasonal trends of aerosols and its external influences have been studied using aerosol optical properties retrieved from Aerosol Robotic Network (AERONET). Complementary studies were taken into account by using several meteorological factors, computational models and meteorological data. Moreover, this paper presents optical properties analysis of different types of aerosols and the seasonal variability of them in one year of measurements. The major categories of aerosol types are evidenced, such as urban/industrial aerosol, biomass burning and mineral dust.

Intercomparison of aerosol single-scattering albedo derived from AERONET surface radiometers and LARGE in situ aircraft profiles during the 2011 DRAGON-MD and DISCOVER-AQ experiments

Single-scattering albedo (SSA) retrievals obtained with CIMEL Sun-sky radiometers from the Aerosol Robotic Network (AERONET) aerosol monitoring network were used to make comparisons with simultaneous in situ sampling from aircraft profiles carried out by the NASA Langley Aerosol Group Experiment (LARGE) team in the summer of 2011 during the coincident DRAGON-MD (Distributed Regional Aerosol Gridded Observational Network-Maryland) and DISCOVER-AQ (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality) experiments. The single-scattering albedos (interpolated to 550 nm) derived from AERONET measurements for aerosol optical depth (AOD) at 440 nm ≥ 0.4 (mean SSA: 0.979) were on average 0.011 lower than the values derived from the LARGE profile measurements (mean SSA: 0.99). The maximum difference observed was 0.023 with all the observed differences within the combined uncertainty for the stated SSA accuracy (0.03 for AERONET; 0.02 for LARGE). Single-scattering albedo averages were also analyzed for lower aerosol loading conditions (AOD ≥ 0.2) and a dependence on aerosol optical depth was noted with significantly lower single-scattering albedos observed for lower AOD in both AERONET and LARGE data sets. Various explanations for the SSA trend were explored based on other retrieval products including volume median radius and imaginary refractive index as well as column water vapor measurements. Additionally, these SSA trends with AOD were evaluated for one of the DRAGON-MD study sites, Goddard Space Flight Center, and two other Mid-Atlantic AERONET sites over the long-term record dating to 1999.

Sensitivity of aerosol retrieval to geometrical configuration of ground-based sun/sky radiometer observations

Abstract. A sensitivity study of aerosol retrievals to the geometrical configuration of the ground-based sky radiometer observations is carried out through inversion tests. Specifically, this study is focused on principal plane and almucantar observations, since these geometries are employed in AERONET (AErosol RObotic NETwork). The following effects have been analyzed with simulated data for both geometries: sensitivity of the retrieval to variability of the observed scattering angle range, uncertainties in the assumptions of the aerosol vertical distribution, surface reflectance, possible instrument pointing errors, and the effects of the finite field of view. The synthetic observations of radiometer in the tests were calculated using a previous climatology data set of retrieved aerosol properties over three AERONET sites: Mongu (Zambia) for biomass burning aerosol, Goddard Space Flight Center (GSFC; Maryland, USA) for urban aerosol and Solar Village (Saudi Arabia) for desert dust aerosol. The results show that almucantar retrievals, in general, are more reliable than principal plane retrievals in presence of the analyzed error sources. This fact partially can be explained by practical advantages of the almucantar geometry: the symmetry between its left and right branches that helps to eliminate some observational uncertainties and the constant value of optical mass during the measurements, that make almucantar observations nearly independent of the vertical variability of aerosol. Nevertheless, almucantar retrievals present instabilities at high sun elevations due to the reduction of the scattering angle range coverage, resulting in decrease of information content. It is in such conditions that principal plane retrievals show a better stability, as shown by the simulation analysis of the three different aerosol models. The last part of the study is devoted to the identification of possible differences between the aerosol retrieval results obtained from real AERONET data using both geometries. In particular, we have compared AERONET retrievals at the same sites used in the simulation analysis: Mongu (biomass burning), GSFC (urban) and Solar Village (desert dust). Overall, this analysis shows robust consistency between the retrievals from simultaneous observations in principle plane and almucantar All identified differences are within the uncertainties estimated for the AERONET operational aerosol retrieval. The differences in the size distribution are generally under 10% for radii between 0.1 μm and 5 μm, and outside this size range, the differences can be as large as 50%. For the absorption parameters, i.e., single scattering albedo and the imaginary part of the refractive index, the differences are typically under 0.01 and 0.003, respectively. The real part of the refractive index showed a difference of 0.01 for biomass burning and urban aerosol, and a difference of around 0.03 for desert dust. Finally, it should be noted that the whole data set includes only 200 pairs, which have been taken under very stable atmospheric conditions; therefore, in a general case, differences between principal plane (PPL) and almucantar (ALM) are expected to be higher. Though the observed differences between ALM and PPL are rather small, it should be noted that this analysis has been conducted using a limited set of 200 observation pairs selected under stable atmospheric conditions.