Comment on amt-2022-101

Satellite aerosol optical depth (AOD) is widely used to estimate particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5) mass concentrations. Polar orbiting satellite retrieval 1–2 times each day is frequently affected by cloud, snow cover or misclassification of heavy pollution. Novel methods are therefore required to improve AOD sampling. Sunphotometer provides much more AODs than satellite at a fixed point. Furthermore, much of the aerosol pollution is regional. Both factors indicate that sunphotometer has great potential for PM2.5 concentration estimation. The spatial representativeness of the Aerosol Robotic Network (AERONET) AOD at Beijing site is investigated by linear regression analysis of 13-year daily paired AODs at each grid from Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and Beijing AERONET. The result suggests a good correlation for the whole Beijing Administrative region, with regional mean correlation coefficient exceeding 0.73. Pixel AODs are then estimated from AERONET AOD using linear equations, which are verified to have the same accuracy as that of MODIS AOD. Either AOD from MODIS retrieval or estimation from AERONET AOD in the absence of MODIS pixel AOD is finally used to predict PM2.5 concentration. Daily AOD sampling in average is enhanced by 59% in winter when MODIS AODs are very limited. More importantly, synergy of AERONET and MODIS AOD is able to improve the estimation of regional mean PM2.5 concentrations, which indicates this method would play a significant role in monitoring regional aerosol pollution.

Collocated data of the moderate resolution imaging spectroradiometer (MO-DIS) Collection 6.1 aerosol optical depths (AOD) at 3 km × 3 km north of 59.9°N over ocean were assessed at 550 nm by aerosol robotic network (AERONET) data from coastal sites and marine aerosol network (MAN) data from vessels during June to October 2006 to 2018. Typically, MODIS AOD was higher at low and lower at high values than the AERONET AOD. Discrepancies were largest for sites where the Earth’s surface around the site is very heterogeneous (Canadian Archipelago, coast of Greenland). Due to the higher likelihood for sea-ice, MAN and MODIS AOD differed stronger west of Greenland and over the Beaufort Sea than at location in the Greenland and Norwegian Seas and Atlantic. MODIS AOD well captured the inter-seasonal variability found in the AERONET AOD data (R = 0.933). At all sites, MO-DIS and AERONET AOD agreement improved as time progressed in the shipping season, hinting at errors in sea-ice vs. open water classification. Overall 75.3% of the MODIS AOD data fell within the limits of the error envelops of the AERONET/MAN AOD data with MAN ranging between 87.5% and 100%. Changes in both MODIS and AERONET mean AOD between two periods of same length (2006-2011, 2013-2018) were explainable by changes in emissions for all sites.

Accurate retrieval of satellite‐derived aerosol optical depth (AOD) is critical for air quality forecasting, especially when AOD is assimilated into models. However, if retrieval errors in the satellite‐derived AOD are not corrected or characterized, they can lead to false analysis increments and ultimately degrade the quality of data assimilation (DA). The issue also arises in the Geostationary Ocean Color Imager (GOCI)‐II AOD product, which remains susceptible to cloud contamination. To address the issue, this study developed a cloud screening framework for GOCI‐II AOD that combines two processes: (a) cloud removal using cloud type information from Himawari‐8/9 Advanced Himawari Imager (AHI) and (b) a statistical cloud filtering based on AOD retrieval characteristics. Comparisons with Aerosol Robotic Network (AERONET) AOD demonstrated that the cloud screening framework effectively mitigated overestimation of GOCI‐II AODs compared to AERONET AODs. Consequently, the cloud‐screened GOCI‐II AOD showed a better agreement with AERONET AOD, improving Pearson's correlation coefficient from 0.77 to 0.82 and decreasing root‐mean‐square error from 0.175 to 0.110. Despite these improvements, the normalized biases of the cloud‐screened GOCI‐II AOD against AERONET AOD still exhibited diurnal variations, with GOCI‐II AODs being overestimated in coastal regions and underestimated in inland sites, under low‐AOD conditions. Nonetheless, the enhanced consistency of the cloud‐screened GOCI‐II AOD with AERONET AOD and its more Gaussian‐like error distribution supports the utility of the cloud screening framework in improving the quality of GOCI‐II AOD for DA systems.

The lack of aerosol information over the cryosphere introduces large uncertainties to our understanding of phenomenon, known as the Arctic Amplification (AA) and its feedback mechanisms. The aerosol optical depth (AOD) describes the optical characteristics of aerosol loading. This paper describes a novel algorithm, which retrieves AOD above snow-covered regions from the measurements of the up-welling radiation at the top of atmosphere, observed by the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) instruments. The algorithm optimizes the generic eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) approach for longer wavelengths over the cryosphere. The algorithm utilizes the characteristics of solar bidirectional distribution properties of snow and aerosol at wavelength 3.7 μm to derive above-snow-AOD. Since the impact of fine-mode aerosol on 3.7 μm is ignorable, the retrieved AOD in this manuscript represents mainly coarse-mode dominated part. A novel method to extract the solar reflection part at 3.7 μm is presented and used in the surface parameterization. Two aerosol types (sea salt-dominated and dust-dominated) are used and the best-fit type is derived by an iterative procedure, using a Look-Up-Table (LUT) approach. Sensitivity studies of the impact on the retrieved AOD using XBAER algorithm, which investigate the impacts of aerosol type, snow surface emissivity and potential cloud contamination under typical AATSR observation conditions, are presented. The sensitivity studies show that the surface parametrization and aerosol typing are suitable for the retrieval of above-snow-AOD over the Arctic snow-covered region. AOD observations retrieved in this study from AATSR (2002–2012) observation collocated with those from the Aerosol Robotic Network (AERONET) sites over Greenland show good agreement. 72.1% of the match-ups fall into the expected error envelope of (±0.15AOD ± 0.025). The AATSR derived above-snow-AOD at 0.55 μm research product has also been compared with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) aerosol product, the Mineral Aerosols Profiling from Infrared Radiances (MAPIR) derived Infrared Atmospheric Sounding Interferometer (IASI) AOD research product, and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) AOD simulations over Greenland on April 2011. The comparison reveals that all datasets show similar patterns for the AOD above Greenland. The AOD is smaller in central Greenland and larger over the coastline regions. The XBAER derived above-snow-AOD has improved coverage, as compared to that of the existing AATSR aerosol product. The transition between above-snow-AOD and AOD derived over surrounding ocean surfaces does not indicate any systematic errors. Two aerosol transport events have been well-captured by the XBAER derived above-snow-AOD research product. The new algorithm is also applied to the SLSTR onboard Sentinel-3 demonstrating new SLSTR above-snow-AOD data products, and its value for research in the changing AOD during the period of Arctic Amplification.

Land Surface Temperature (LST) is one of the key parameters in the physics of land-surface processes on regional and global scales, combining the results of all surface-atmosphere interactions and energy fluxes between the surface and the atmosphere. With the advent of the European Space Agency (ESA) Sentinel 3 (S3) satellite, accurate LST retrieval methodologies are being developed by exploiting the synergy between the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR). In this paper we explain the implementation in the Basic ENVISAT Toolbox for (A)ATSR and MERIS (BEAM) and the use of one LST algorithm developed in the framework of the Synergistic Use of The Sentinel Missions For Estimating And Monitoring Land Surface Temperature (SEN4LST) project. The LST algorithm is based on the split-window technique with an explicit dependence on the surface emissivity. Performance of the methodology is assessed by using MEdium Resolution Imaging Spectrometer/Advanced Along-Track Scanning Radiometer (MERIS/AATSR) pairs, instruments with similar characteristics than OLCI/ SLSTR, respectively. The LST retrievals were properly validated against in situ data measured along one year (2011) in three test sites, and inter-compared to the standard AATSR level-2 product with satisfactory results. The algorithm is implemented in BEAM using as a basis the MERIS/AATSR Synergy Toolbox. Specific details about the processor validation can be found in the validation report of the SEN4LST project.

Abstract. Originally developed for the moderate resolution imaging spectroradiometer (MODIS) in polar, sun-synchronous low earth orbit (LEO), the Dark Target (DT) aerosol retrieval algorithm relies on the assumption of a surface reflectance parameterization (SRP) over land surfaces. Specifically for vegetated and dark-soiled surfaces, values of surface reflectance in blue and red visible-wavelength bands are assumed to be nearly linearly related to each other and to the value in a shortwave infrared (SWIR) wavelength band. This SRP also includes dependencies on scattering angle and a normalized difference vegetation index computed from two SWIR bands (NDVISWIR). As the DT retrieval algorithm is being ported to new sensors to continue and expand the aerosol data record, we assess whether the MODIS-assumed SRP can be used for these sensors. Here, we specifically assess SRP for the Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite (GOES)-16/East (ABIE). First, we find that using MODIS-based SRP leads to higher biases and artificial diurnal signatures in aerosol optical depth (AOD) retrievals from ABIE. The primary reason appears to be that the geostationary orbit (GEO) encounters an entirely different set of observation geometry than does LEO, primarily with regard to solar angles coupled with fixed-view angles. Therefore, we have developed a new SRP for GEO that draws the angular shape of the surface bidirectional reflectance. We also introduce modifications to the parameterization of both red–SWIR and blue–red spectral relationships to include additional information. The revised red–SWIR SRP includes the solar zenith angle, NDVISWIR, and land-type percentage from an ancillary database. The blue–red SRP adds dependencies on the scattering angle and NDVISWIR. The new SRPs improve the AOD retrieval of ABIE in terms of overall less bias and mitigation of the overestimation around local noon. The average bias of the DT AOD compared to the Aerosol Robotic Network (AERONET) AOD shows a reduction from 0.08 to 0.03, while the bias of local solar noon decreases from 0.12 to 0.03. The agreement between the DT and AERONET AOD is established through a regression slope of 1.06 and a y intercept of 0.01 with a correlation coefficient of 0.74. By using the new SRP, the percentage of data falling within the expected error range (±0.05 % + 15 %) is notably increased from 54 % to 78 %.

The influence and contribution of fine mode particles to aerosol optical properties during haze events at the foothills of Himalaya-Karakorum region

Abstract. This work aims at investigating the effect of NO2 absorption on aerosol-optical-depth (AOD) measurements and Ångström exponent (AE) retrievals of sun photometers by the synergistic use of accurate NO2 characterization for optical-depth estimation from co-located ground-based measurements. The analysis was performed for ∼ 7 years (2017–2023) at several sites worldwide for the AOD measurements and AE retrievals by Aerosol Robotic Network (AERONET) sun photometers which use OMI (Ozone Monitoring Instrument) climatology for NO2 representation. The differences in AOD and AE retrievals by NO2 absorption are accounted for using high-frequency columnar NO2 measurements by a co-located Pandora spectroradiometer belonging to the Pandonia Global Network (PGN). NO2 absorption affects the AOD measurements in UV-Vis (visible) range, and we found that the AOD bias is the most affected at 380 nm by NO2 differences, followed by 440, 340, and 500 nm, respectively. AERONET AOD was found to be overestimated in half of the cases, while also underestimated in other cases as an impact of the NO2 difference from “real” (PGN NO2) values. Overestimations or underestimations are relatively low. About one-third of these stations showed a mean difference in NO2 and AOD (at 380 and 440 nm) above 0.5 × 10−4 mol m−2 and 0.002, respectively, which can be considered a systematic contribution to the uncertainties in the AOD measurements that are reported to be of the order of 0.01. However, under extreme NO2 loading scenarios (i.e. 10 % highest differences) at highly urbanized/industrialized locations, even higher AOD differences were observed that were at the limit of or higher than the reported 0.01 uncertainty in the AOD measurement. PGN NO2-based sensitivity analysis of AOD difference suggested that for PGN NO2 varying between 2 × 10−4 and 8 × 10−4 mol m−2, the median AOD differences were found to rise above 0.01 (even above 0.02) with the increase in NO2 threshold (i.e. the lower limit from 2 × 10−4 to 8 × 10−4 mol m−2). The AOD-derivative product, AE, was also affected by the NO2 correction (discrepancies between the AERONET OMI climatological representation of NO2 values and the real PGN NO2 measurements) on the spectral AOD. Normalized frequency distribution of AE (at 440–870 and 340–440 nm wavelength pair) was found to be narrower for a broader AOD distribution for some stations, and vice versa for other stations, and a higher relative error at the shorter wavelength (among the wavelength pairs used for AE estimation) led to a shift in the peak of the AE difference distribution towards a higher positive value, while a higher relative error at a lower wavelength shifted the AE difference distribution to a negative value for the AOD overestimation case, and vice versa for the AOD underestimation case. For rural locations, the mean NO2 differences were found to be mostly below 0.50 × 10−4 mol m−2, with the corresponding AOD differences being below 0.002, and in extreme NO2 loading scenarios, it went above this value and reached above 1.00 × 10−4 mol m−2 for some stations, leading to higher AOD differences but below 0.005. Finally, AOD and AE trends were calculated based on the original AERONET AOD (based on AERONET OMI climatological NO2), and its comparison with the mean differences in the AERONET and PGN NO2-corrected AOD was indicative of how NO2 correction could potentially affect realistic AOD trends.

This paper presents the validation results of Aerosol Optical Depth (AOD) retrieved from the Spinning Enhanced Visible Infrared Radiometer (SEVIRI) data using the near-real-time algorithm further developed in the frame of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project. The SEVIRI AOD was compared against multiple data sources: six stations of the Aerosol Robotic Network (AERONET) in Romania and Poland, three stations of the Aerosol Research Network in Poland (Poland–AOD) and Moderate Resolution Imaging Spectroradiometer (MODIS) data overlapping Romania, Czech Republic and Poland. The correlation values between a four-month dataset (June–September 2014) from SEVIRI and the closest temporally available data for both ground-based and satellite products were identified. The comparison of the SEVIRI AOD with the AERONET AOD observations generally shows a good correlation (r = 0.48–0.83). The mean bias is 0.10–0.14 and the root mean square error RMSE is between 0.11 and 0.15 for all six stations cases. For the comparison with Poland–AOD correlation values are 0.55 to 0.71. The mean bias is 0.04–0.13 and RMSE is between 0.10 and 0.14. As for the intercomparison to MODIS AOD, correlations values were generally lower (r = 0.33–0.39). Biases of −0.06 to 0.24 and RMSE of 0.04 to 0.28 were in good agreement with the ground–stations retrievals. The validation of SEVIRI AOD with AERONET results in the best correlations followed by the Poland–AOD network and MODIS retrievals. The average uncertainty estimates are evaluated resulting in most of the AOD values falling above the expected error range. A revised uncertainty estimate is proposed by including the observed bias form the AERONET validation efforts.

In this study, several georeferencing methods such as Polynomial, Thin Plate Spline (TPS) and geolocation transformations are applied on satellite images over Vietnam areas from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard Terra/Aqua satellites and Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the Suomi National Polar-orbiting Partnership (NPP) satellite. In comparison with global administrative areas data, the georeferenced images of TPS function have a better fitting than Polynomial function. In addition, the experimental results highlighted the effect of georeferencing methods on satellite aerosol optical depth (AOD) data in which TPS function produce a higher quality than Polynomial function on both MODIS Terra/Aqua and VIIRS AOD images. Validation results indicated strong correlation between AERONET AOD and TPS georeferenced images (R2 = 0.739, 0.537 and 0.596 for MODIS Terra, MODIS Aqua and VIIRS AOD, respectively) meanwhile these correlations of Polynomial are lower (R2 = 0.596, 0.443 and 0.475 for MODIS Terra, MODIS Aqua and VIIRS AOD, respectively). The correlations between MODIS AOD and VIIRS AOD images are also affected by georeferencing methods. The average of these correlations on TPS georeferenced images is 0.72 meanwhile mean correlation of Polynomial is 0.51. This study focused on the assessment of georeferencing methods and highlighted the effect of these methods on the quality and correlation of satellite AOD images.

<p>The performance of JAXA Himawari-8 Advanced Himawari Imager (AHI) aerosol optical depth (AOD) products over China is evaluated with ground-based AErosol RObotic NETwork (AERONET) and Sun-Sky Radiometer Observation Network (CARSNET) observations as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products. Considering the quality and quantity of valid data, the study was limited to AOD products from AHI with a Quality Assurance Flag (QA_Flag) of “good” and “very good.” The spatial distribution of the AHI AOD product is similar to that of the MODIS AOD product. The AOD correlation between AHI and MODIS is better in the morning than in the afternoon after March, however, using MODIS AOD as a reference resulted in underestimation in the morning and overestimation in the afternoon. The bias is also larger in spring and autumn than in summer and winter. Validation with sun-photometer observations indicates good correlation between AHI AOD and ground-based observations with correlation coefficients larger than 0.75 (N>1000) when barren and sparsely vegetated surfaces are excluded. At 02:30 UTC, 53% of the collocated AHI AOD observations fall in the expected error (EE) range and at 5:30 UTC, 59.3% fall above the EE. The AHI AOD overestimation was apparent at the Northern China stations in April and after October, whereas the underestimation was apparent in southern China throughout the year. The temporal variations of AHI and AERONET AOD also show that the overestimation occurred in the afternoon and underestimation occurred in the morning.</p><p>The assumption that the solar geometries were nearly identical and the surface reflectance unchanged for a month causes the surface reflectance underestimation and leads to the AOD overestimation for barren surfaces in autumn and winter. Because background aerosols were neglected, the surface reflectance was overestimated, leading to AOD underestimation in vegetated surfaces.</p><p>Overall, the JAXA AOD provides a reliable and high temporal resolution aerosol product for environmental and climate research and the aerosol retrieval algorithm requires improvement.</p>

The Ocean and Land Color Instrument (OLCI) onboard the Copernicus Sentinel-3A satellite is a medium-resolution and multi-spectral push-broom imager acquiring radiance in 21 spectral bands covering from the visible to the far near-infrared. These measurements are primary dedicated to land & ocean color applications, but actually include also reliable information for atmospheric aerosol and surface brightness characterization. In the framework of the EUMETSAT funded study to support the Copernicus Program, we describe the retrieval of aerosol and surface properties from OLCI single-viewing multi-spectral Top-Of-Atmosphere (TOA) radiances based on the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm. The high potential of the OLCI/GRASP configuration stems from the attempt to retrieve both aerosol load and surface reflectance simultaneously using a globally consistent high-level approach. For example, both over land and ocean surfaces OLCI/GRASP uses 9 spectral channels (albeit with different weights), strictly the same prescribed aerosol models and globally the same a priori constraints (though with some differences for observations over land and ocean). Due to the lack of angular multi-viewing information, the directional properties of underlying surface are strongly constrained in the retrieval: over ocean the Fresnel reflection together with foam/whitecap albedo are exclusively computed using a priori wind speed; over land, the Bidirectional Reflectance Distribution Function (BRDF) is slightly adjusted from a priori values of climatological Ross-Li volumetric and geometric terms. Meanwhile, the isotropic reflectance is retrieved globally under mild spectral smoothness constraints. It should be noticed that OLCI/GRASP configuration employs innovative multi-pixel concept (Dubovik et al., 2011) that enhance retrieval by simultaneously inverting large group of pixels. The concept allows for benefiting from knowledge about natural variability of the retrieved parameters.The obtained OLCI/GRASP products were validated with the Aerosol Robotic Network (AERONET) and Maritime Aerosol Network (MAN) and intercompared with the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol and surface products. The overall performance is quite comparable to the community-referenced MODIS. Over ocean the OLCI/GRASP results are encouraging with 67% of the AOD (550 nm) satisfying the Global Climate Observing System (GCOS) requirement using AERONET coastal sites and 74% using MAN deep ocean measurements, and an AOD (550 nm) bias 0.01 with AERONET and nearly zero bias with MAN. Over land, 48% of OLCI/GRASP AOD (550 nm) satisfy the GCOS requirement and a bias within ±0.01 for total and AOD < 0.2. Key challenges are identified and discussed: adequate screening of cloud contaminations, retrieval of aerosol over bright surfaces and in the regions containing complex mixtures of aerosol.

Abstract. Several sun photometer networks worldwide include instruments for aerosol optical depth (AOD) observations, such as Global Atmospheric Atmosphere Watch-Precision Filter Radiometer (GAW-PFR) and Aerosol Robotic Network (AERONET). AERONET provides additional aerosol properties such as the detailed volume size distribution and the single scattering albedo through inversion modelling of sky radiance measurements. However, the data availability for such properties is limited due to the limited number of daily almucantar sky radiance scans and cloudiness. AOD is measured significantly more frequently as there can be one measurement even every minute. Also, the AOD measurements are affected only by clouds being too close or covering the solar disk. The Generalized Retrieval of Atmosphere and Surface Properties (GRASP) is a flexible inversion model to retrieve aerosol properties from various observations. One of its capabilities is the retrieval of the volume concentration, the volume median radius and geometric standard deviation for each aerosol size distribution mode and the separation of AOD to each mode using only spectral AOD as an input parameter (known as the GRASP-AOD application). Such properties are important for various applications, as the size of aerosols affects their interaction with solar radiation, clouds and radiative forcing modelling. Particle size also shows significant differences depending on the aerosol type such as dust or biomass burning. In this study, we selected four common stations of GAW-PFR and AERONET, used GRASP to retrieve the bimodal size distribution parameters from AOD measured by GAW-PFR instruments (PFRs) and validated the results for different conditions using AERONET data as reference. One of those sites includes a multi-year parallel timeseries from two different BTS spectroradiometers that combined can provide direct spectral irradiance (and as a result AOD) in the 300–2150 nm range. Using this dataset, we were able to investigate the effect and potential benefits of the increased spectral range on GRASP-AOD retrievals. This is mostly focused on the retrieval of the coarse mode volume median radius, which is particularly challenging with the filter radiometers measuring up to 862 or 1020 nm. We also assessed the performance for certain dust and biomass burning cases. Our results showed good agreement between PFR AOD-based and AERONET sky radiance inversions for AOD modal separation and volume concentrations. Significant improvement of the PFR-AERONET intercomparison was also possible for the fine mode volume and effective radius when restricting the datasets to AOD at 500 nm &gt; 0.1 and Ångström Exponent (AE) &gt; 1. Also, the results showed consistency with previous study regarding the validation of such retrievals using AERONET AOD. Focusing on conditions with high proportion of dust particles, we found consistent results with the general cases Using AOD with a larger spectral range (from BTS spectroradiometer), we found that the wavelength selection may affect the results and that using longer wavelengths can increase the sensitivity of coarse mode volume median radius to AOD and improve the correlation of the GRASP BTS AOD-based and AERONET datasets. However, the available data were limited, so it is not clear under what conditions the inclusion of such wavelengths will result in more accurate retrievals or to what extent. Finally, we were able to reproduce with GRASP the aerosol size characteristics of unusual biomass burning cases from the Canadian wildfires during 2023, but the results showed systematically increased fine mode radius and concentration compared to the AERONET output.

Global estimates of surface albedo from Sentinel-3 OLCI and SLSTR data for Copernicus Climate Change Service: Algorithm and preliminary validation

<strong class="journal-contentHeaderColor">Abstract.</strong> Satellite-based aerosol optical depth (AOD) has gained popularity as a powerful data source for calibrating aerosol models and correcting model errors through data assimilation. However, simulated airborne particle mass concentrations are not directly comparable to satellite-based AODs. For this, an AOD operator needs to be developed that can convert the simulated mass concentrations into model AODs. The AOD operator is most sensitive to the input of the particle size and chemical composition of aerosols. Furthermore, assumptions regarding particle size vary significantly amongst model AOD operators. More importantly, satellite retrieval algorithms rely on different size assumptions. Consequently, the differences between the simulations and observations do not always reflect the actual difference in aerosol amount. In this study, the sensitivity of the AOD operator to aerosol properties has been explored. We conclude that, to avoid inconsistencies between the AOD operator and retrieved properties, a common understanding of the particle size is required. Accordingly, we designed a hybrid assimilation methodology (<i>hybrid</i> AOD assimilation) that includes two sequentially conducted procedures. First, aerosol size in the model operator has been brought closer to the assumption of the satellite retrieval algorithm via assimilation of à ngström exponents. This ensures that the model AOD operator is more consistent with the AOD retrieval. The second step in the methodology concerns optimization of aerosol mass concentrations through direct assimilation of AOD (<i>standard</i> AOD assimilation). The hybrid assimilation method is tested over the European domain using Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue products. The corrections made to the model aerosol size information are validated through a comparison with the ground-based Aerosol Robotic Network (AERONET) optical product. The increments in surface aerosol mass concentration that occur due to either the standard AOD assimilation analysis or the hybrid AOD assimilation analysis are evaluated against independent ground PM<span class="inline-formula">_2.5</span> observations. The standard analysis always results in relatively accurate posterior AOD distributions; however, the corrections are hardly transferred into better aerosol mass concentrations due to the uncertainty in the AOD operator. In contrast, the model AOD and mass concentration states are considerably more accurate when using the hybrid methodology.