AERONET Aerosol Optical Depth Research Articles

Consistency of Aerosol Optical Properties between MODIS Satellite Retrievals and AERONET over a 14-Year Period in Central–East Europe

Aerosols influence Earth’s climate by interacting with radiation and clouds. Remote sensing techniques aim to enhance our understanding of aerosol forcing using ground-based and satellite retrievals. Despite technological advancements, challenges persist in reducing uncertainties in satellite remote sensing. Our study examines retrieval biases in MODIS sensors on Terra and Aqua satellites compared to AERONET ground-based measurements. We assess their performance and the correlation with the AERONET aerosol optical depth (AOD) using 14 years of data (2010–2023) from 29 AERONET stations across 10 Central–East European countries. The results indicate discrepancies between MODIS Terra and Aqua retrievals: Terra overestimates the AOD at 16 AERONET stations, while Aqua underestimates the AOD at 21 stations. The examination of temporal biases in the AOD using the calculated estimated error (ER) between AERONET and MODIS retrievals reveals a notable seasonality in coincident retrievals. Both sensors show higher positive AOD biases against AERONET in spring and summer compared to fall and winter, with few ER values for Aqua indicating poor agreement with AERONET. Seasonal variations in correlation strength were noted, with significant improvements from winter to summer (from R2 of 0.58 in winter to R2 of 0.76 in summer for MODIS Terra and from R2 of 0.53 in winter to R2 of 0.74 in summer for MODIS Aqua). Over the fourteen-year period, monthly mean aerosol AOD trends indicate a decrease of −0.00027 from AERONET retrievals and negative monthly mean trends of the AOD from collocated MODIS Terra and Aqua retrievals of −0.00023 and −0.00025, respectively. An aerosol classification analysis showed that mixed aerosols comprised over 30% of the total aerosol composition, while polluted aerosols accounted for more than 22%, and continental aerosols contributed between 22% and 24%. The remaining 20% consists of biomass-burning, dust, and marine aerosols. Based on the aerosol classification method, we computed the bias between the AERONET AE and MODIS AE, which showed higher AE values for AERONET retrievals for a mixture of aerosols and biomass burning, while for marine aerosols, the MODIS AE was larger and for dust the results were inconclusive.

Parameterizing spectral surface reflectance relationships for the Dark Target aerosol algorithm applied to a geostationary imager

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 %.

Comparative analysis of CAMS aerosol optical depth data and AERONET observations in the Eastern Mediterranean over 19 years

Aerosol optical depth (AOD) is an essential metric for evaluating the atmospheric aerosol load and its impacts on climate, air quality, and public health. In this study, the AOD data from the Copernicus Atmosphere Monitoring Service (CAMS) were validated against ground-based measurements from the Aerosol Robotic Network (AERONET) throughout the Eastern Mediterranean, a region characterized by diverse aerosol types and sources. A comparative analysis was performed on 3-hourly CAMS AOD values at 550 nm against observations from 20 AERONET stations across Cyprus, Greece, Israel, Egypt, and Turkey from 2003 to 2021. The CAMS AOD data exhibited a good overall agreement with AERONET AOD data, demonstrated by a Pearson correlation coefficient of 0.77, a mean absolute error (MAE) of 0.08, and a root mean square error (RMSE) of 0.11. Nonetheless, spatial and temporal variations were observed in the CAMS AOD data performance, with site-specific correlation coefficients ranging from 0.57 to 0.85, the lowest correlations occurring in Egypt and the highest in Greece. An underestimation of CAMS AOD was noted at inland sites with high AOD levels, while a better agreement was observed at coastal sites with lower AOD levels. The diurnal variation analysis indicated improved CAMS reanalysis performance during the afternoon and evening hours. Seasonally, CAMS reanalysis showed better agreement with AERONET AODs in spring and autumn, with lower correlation coefficients noted in summer and winter. This study marks the first comprehensive validation of CAMS AOD performance in the Eastern Mediterranean, offering significant enhancements for regional air quality and climate modeling, and underscores the essential role of consistent validation in refining aerosol estimations within this complex and dynamic geographic setting.

Optical and physical characteristics of aerosols over Asia: AERONET, MERRA-2 and CAMS

A comprehensive study on the regional and spatial distributions of aerosol columnar optical and physical characteristics utilizing high-quality Aerosol Robotic Network (AERONET) datasets over Asia (Central, South, South-East, and East Asia), along with spatiotemporal collocated validation of two highly-spatially resolved models (Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS)) simulated aerosol optical depth (AOD) and Ångström exponent (AE) on annual and seasonal scales, is conducted for the first time. AOD is the highest over South Asia in each season, followed by South-East, East, and Central Asia. Annual regional average AOD at 0.50 μm over South Asia is 0.61. Combined influence of both fine mode anthropogenic aerosol emissions from fossil fuel combustion and biomass burning, and coarse mode dust aerosols from seasonal transport lead to higher AOD and total volume concentration (TVC), and exhibit significant spatiotemporal variations. Coarse volume fraction (CVF) and effective radius (Reff) are higher over Central Asia due to the dominance of coarse dust aerosols. East and South-East Asia are dominated by fine mode aerosols and result in higher fine volume concentration (FVC), AE, fine mode fraction (FMF), and lower Reff. Compared to the other regions of the globe (except Africa), AOD is higher over Asia, with higher spatiotemporal variabilities in AOD, AE, FMF, and TVC. Over Asia, the agreement between AERONET and CAMS AOD is better than that of AERONET and MERRA-2 AOD. For high AOD conditions, underestimation in model AODs is higher, and lower fraction of model AODs satisfy the Global Climate Observing System (GCOS) requirement over all regions, and these are more pronounced over Asia. Biases in model AODs are higher over Asia compared to the other regions of the globe, and lower over North America, Europe, and Australia. Higher bias in model AEs compared to AODs over all the regions shows substantial challenges in simulating spectral AOD and appropriate contributions of fine and coarse mode aerosols. These findings over a global aerosol hotspot region, Asia, along with other regions of the globe are crucial for accurate simulation and fine-tuning of aerosol characteristics by regional and global models, and for reducing uncertainties in the assessment of radiative and climate impact of aerosols.

Satellite-based aerosol optical depth estimates over the continental U.S. during the 2020 wildfire season: Roles of smoke and land cover

Wildfires produce smoke that can affect an area >1000 times the burn extent, with far-reaching human health, ecologic, and economic impacts. Accurately estimating aerosol load within smoke plumes is therefore crucial for understanding and mitigating these impacts. We evaluated the effectiveness of the latest Collection 6.1 MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in estimating aerosol optical depth (AOD) across the U.S. during the historic 2020 wildfire season. We compared satellite-based MAIAC AOD to ground-based AERONET AOD measurements during no-, light-, medium-, and heavy-smoke conditions identified using the Hazard Mapping System Fire and Smoke Product. This smoke product consists of maximum extent smoke polygons digitized by analysts using visible band imagery and classified according to smoke density. We also examined the strength of the correlations between satellite- and ground-based AOD for major land cover types under various smoke density levels. MAIAC performed well in estimating AOD during smoke-affected conditions. Correlations between MAIAC and AERONET AOD were strong for medium- (r = 0.91) and heavy-smoke (r = 0.90) density, and MAIAC estimates of AOD showed little bias relative to ground-based AERONET measurements (normalized mean bias = 3 % for medium, 5 % for heavy smoke). During two high AOD, heavy smoke episodes, MAIAC underestimated ground-based AERONET AOD under mixed aerosol (i.e., smoke and dust; median bias = −0.08) and overestimated AOD under smoke-dominated (median bias = 0.02) aerosol. MAIAC most overestimated ground-based AERONET AOD over barren land (mean NMB = 48 %). Our findings indicate that MODIS MAIAC can provide robust estimates of AOD as smoke density increases in coming years. Increased frequency of mixed aerosol and expansion of developed land could affect the performance of the MAIAC algorithm in the future, however, with implications for evaluating wildfire-associated health and welfare effects and air quality standards.

Assessment and characteristics of S-NPP VIIRS Deep Blue and Dark Target aerosol properties under clean, polluted and fire scenarios over the Amazon

The present study carries out the systematic performance evaluation of aerosol optical depth (AOD) products retrieved using Visible Infrared Imaging Radiometer Suite (VIIRS) Deep Blue (DB) and Dark Target (DT) onboard Suomi National Polar-orbiting Partnership (S-NPP) satellite over the Amazon Basin. Characteristics and uncertainty were evaluated under distinct air pollution scenarios such as a clean background in the wet season, polluted conditions in the dry season with biomass burning emissions and peak burning season with higher fire activity. VIIRS retrievals were also analyzed under aerosol loading, particle size and surface vegetation coverage against the Aerosol Robotic Network (AERONET) measurements at 9 sites in 2012–2022. VIIRS DB showed good accuracy, with 78% of AOD matchups falling within the expected error, 84% in the wet season, and 71% in the dry and burning seasons. In contrast, VIIRS DT indicated poor accuracy (64%) and trends overestimate the AOD in all air pollution scenarios. Both algorithms were sensitive to AERONET sites with high elevation and dark vegetated coverage characteristics. VIIRS DB and DT systematically overestimated AERONET AOD as increased aerosol loading. DB trends underestimate aerosol under background conditions and overestimate with coarse and fine particle predominance. Additionally, both algorithms indicated poor accuracy under forest type with a large positive bias. VIIRS DB demonstrated the highest accuracy in the presence of aerosol loading in sites characterized by mixed land cover type. This was observed in both coarse and fine mode scenarios for grassland. VIIRS DT demonstrated satisfactory accuracy under background conditions and dominance of coarse particles within grassland land cover type. For mixed land cover, satisfactory accuracy was found under intermediate aerosol loading conditions. DB algorithm showed greater uncertainty associated with the coarse particle aerosol for the full period and all polluted scenarios. Overall, VIIRS DT accuracy was more sensitive to varying air pollution scenarios.

Preliminary Retrieval and Validation of Aerosol Optical Depths from FY-4B Advanced Geostationary Radiation Imager Images

Fengyun-4B (FY-4B) is the latest Chinese next-generation geostationary meteorological satellite. The Advanced Geostationary Radiation Imager (AGRI) aboard FY-4B is equipped with 15 spectral bands, from visible to infrared, suitable for aerosol optical depth (AOD) retrieval. In this study, an overland AOD retrieval algorithm was developed for the FY-4B AGRI. Considering the large directional variation in the FY-4B AGRI reflectances, a bidirectional reflectance distribution function (BRDF) database was built, through which to estimate land surface reflectance/albedo. Seasonal aerosol models, based on four geographical regions in China, were developed between 2016 and 2022 using AERONET aerosol products, to improve their applicability to regional distribution differences and seasonal variations in aerosol types. AGRI AODs were retrieved using this new method over China from September 2022 to August 2023 and validated against ground-based measurements. The AGRI, Advanced Himawari Imager (AHI), and Moderate-Resolution Imaging Spectroradiometer (MODIS) official land aerosol products were also evaluated for comparison purposes. The results showed that the AGRI AOD retrievals were highly consistent with the AERONET AOD measurements, with a correlation coefficient (R) of 0.88, root mean square error (RMSE) of 0.14, and proportion that met an expected error (EE) of 65.04%. Intercomparisons between the AGRI AOD and other operational AOD products showed that the AGRI AOD retrievals achieved better performance results than the AGRI, AHI, and MODIS official AOD products. Moreover, the AGRI AOD retrievals showed high spatial integrity and stable performance at different times and regions, as well as under different aerosol loadings and characteristics. These results demonstrate the robustness of the new aerosol retrieval method and the potential of FY-4B AGRI measurements for the monitoring of aerosols with high accuracy and temporal resolutions.

Latitudinal and temporal distribution of aerosols and precipitable water vapor in the tropical Andes from AERONET, sounding, and MERRA-2 data

The aerosol and precipitable water vapor (PW) distribution over the tropical Andes region is characterized using Aerosol Robotic Network (AERONET) observations at stations in Medellin (Colombia), Quito (Ecuador), Huancayo (Peru), and La Paz (Bolivia). AERONET aerosol optical depth (AOD) is interpreted using PM2.5 data when available. Columnar water vapor derived from ozone soundings at Quito is used to compare against AERONET PW. MERRA-2 data are used to complement analyses. Urban pollution and biomass burning smoke (BBS) dominate the regional aerosol composition. AOD and PM2.5 yearly cycles for coincident measurements correlate linearly at Medellin and Quito. The Andes cordillera’s orientation and elevation funnel or block BBS transport into valleys or highlands during the two fire seasons that systematically impact South America. The February–March season north of Colombia and the Colombian-Venezuelan border directly impacts Medellin. Possibly, the March aerosol signal over Quito has a long-range transport component. At Huancayo and La Paz, AOD increases in September due to the influence of BBS in the Amazon. AERONET PW and sounding data correlate linearly but a dry bias with respect to soundings was identified in AERONET. PW and rainfall progressively decrease from north to south due to increasing altitude. This regional diagnosis is an underlying basis to evaluate future changes in aerosol and PW given prevailing conditions of rapidly changing atmospheric composition.

Estimation of high-resolution aerosol optical depth (AOD) from Landsat and Sentinel images using SEMARA model over selected locations in South Asia

The study investigates a high-resolution retrieval of AOD over complex surfaces and various aerosol regimes of South Asian countries such as China, Bangladesh and Nepal from Landsat 7/8/9 and Sentinel 2 images. To estimate AOD from these satellite images, unlike the conventional radiative transfer-based look-up-table (LUT) approach, this study uses SEMARA coupled model which combines a simplified and robust surface reflection estimation (SREM) model and a simplified aerosol retrieval algorithm (SARA). The SEMARA model uses top-of-atmospheric reflectance (TOA) and land surface reflectance (LSR), solar and sensors viewing geometries, including the in-situ AOD data from the Aerosol Robotic Network (AERONET) station to simulate AOD at 30 m and 10 m spatial resolutions. The results are compared with AERONET and MODIS-based AOD for the validation sites in the South Asian countries. A high degree of agreement with AERONET AOD is obtained over Beijing in China (R2 > 0.9) for all sensors, while it varies for the validation AERONET stations over Nepal and Bangladesh (R2 = 0.40–0.86). The accuracy metrices suggest a low uncertainty over China Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for Beijing: 0.07 and 0.13), while it remains higher for stations over Nepal and Bangladesh (MAE and RMSE: 0.33, and 0.41; 0.15–0.27, respectively). >85% of the modelled observations fall within expected error (EE) for China, while 20–75% of observations fall within EE for Nepal and Bangladesh. We have also obtained a proximity effect for the accuracy of the estimated AOD from the SEMARA model. The findings, in general, suggest that the complexity in surface cover and aerosol type produce a higher offset with respect to AERONET observations over Bangladesh and Nepal, and therefore further validation studies are needed for other parts of the globe to measure the consistency of the model.

Aerosol absorption using in situ filter-based photometers and ground-based sun photometry in the Po Valley urban atmosphere

Abstract. Light-absorbing aerosols (LAAs) are short-lived climate forcers with a significant impact on Earth's radiative balance. LAAs include dust aerosols, black carbon (BC) and organic light-absorbing carbonaceous aerosol (collectively termed brown carbon, BrC), which have also been proven to be highly toxic. In this study, aerosol absorption at five wavelengths (ranging from ultraviolet to infrared) was monitored continuously using filter-based photometers during two winter seasons in 2020 and 2021 in the city of Modena (southern central Po Valley, northern Italy), at two regulatory air quality monitoring sites, along with other pollutants (coarse particulate matter, PM10; fine particulate matter, PM2.5; O3; NO; NO2; and C6H6) and the vehicular traffic rate. The aerosol optical depth (AOD) and other column aerosol optical properties were concurrently monitored at four wavelengths by an AErosol RObotic NETwork (AERONET) sun photometer under urban background conditions within Modena. In situ absorption levels were apportioned to both sources (fossil fuel and biomass burning) and species (BC and BrC), while columnar absorption was apportioned to BC, BrC and mineral dust. The combined analysis of the atmospheric aerosol and gas measurements and of the meteorological conditions (in situ and from the ERA5 reanalysis) identified the location of potential urban sources of BC and BrC, most likely related to traffic and biomass burning. In situ data show different diurnal/weekly patterns for BrC from biomass burning and BC from traffic, with minor differences between the background and the urban traffic conditions. AERONET version 3 absorption aerosol optical depth (AAOD) retrievals at four wavelengths allowed the estimation of the absorptive direct radiative effect due to LAAs over the same period under the reasonable assumption that the AOD signal is concentrated within the mixing layer. AERONET retrievals showed a modest correlation of columnar absorption with planetary boundary layer (PBL)-scaled in situ observations, although the correlation improved significantly during a desert dust transport event that affected both in situ aerosol and columnar absorption, particularly in the blue spectrum range. A low correlation occurred between the contribution of BrC to aerosol absorption for the in situ and the columnar observations, with the BrC contribution being generally larger for in situ observations. Finally, evidence of a highly layered atmosphere during the study period, featuring significant spatial mixing and modest vertical mixing, was shown by ERA5-based atmospheric temperature profiles and by the large correlation of concurrent AERONET AOD retrievals in Modena and in Ispra (on the northwestern side of the Po Valley, ca. 225 km from Modena).

Diurnal time representation of MODIS, VIIRS, MISR, and AHI over Asia and Oceania

Retrievals of satellite aerosol optical depth (AOD) products are used for studies of air pollution, energy balance, and climate change. However, few studies have evaluated the diurnal time representation (DTR) of satellite AOD products. In this study, we proposed a statistically valid threshold method to investigate the DTR of Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Multi-angle Imaging Spectroradiometer (MISR), and Advanced Himawari Imager (AHI) AOD products by comparing their retrievals to the daily AERONET AOD values and explored the ability of AHI and AERONET to capture daily aerosol variability at 54 sites in Asia and Oceania from July 2015 to December 2019. For polar-orbiting satellites, based on collocations with AERONET measurements, MISR-derived AOD showed high consistency with daily average AERONET AOD, with a correlation coefficient (R) of 0.94 and 80.85% matchups falling into the envelopes of expected error (EE, ± (0.05 + 0.15 × AODAERONET)). Compared with single MODIS products, a combination of satellite data from the Terra and Aqua MODIS dark target (DT) algorithm might obtain a dataset with better DTR (R = 0.94 and %Within EE = 72.10). The satellite-retrieved AOD did a good job to represent the daily mean AERONET AOD, because AERONET AOD averaged within a ± 30 min time window of different satellite overpasses closely mirrored the daily mean AERONET AOD. Furthermore, the seasonal average diurnal variations (SADV) of AERONET AOD were changing insignificant, except for the urban coverage area in the Southern Hemisphere and equatorial climate. For geostationary satellite, the DTR of AHI was relatively poorer in comparison to the other sensors (R = 0.84, %Within EE = 58.00), and AHI AOD was seriously underestimated in South Asia and overestimated in Oceania. Besides, the SADVs of AHI AOD presented several patterns summarized as wave type, unimodal type, stable fluctuation type, and other types, which meant that AHI AOD retrievals did not accurately capture the diurnal variability of aerosols. This study provides a reference for the aerosol community to select satellite AOD products with appropriate DTR.

Improving the accuracy of AOD by using multi-sensors data over the Red Sea and the Persian Gulf

Aerosol Optical Depth (AOD) retrieved using Cloud-Aerosol LIDAR with Orthogonal Polarization (CALIOP) and Moderate Resolution Imaging Spectroradiometer (MODIS) plays a crucial role in aerosol sensing. However, the performance of these products varies across different aerosol concentrations. This research assessed the performance of CALIOP and MODIS AOD products over a three-year period (January 2017 to June 2019) under various aerosol concentrations in the Red Sea and the Persian Gulf. The products were compared with ship-based AERONET (Aerosol Robotic Network) measurements conducted in the study area. The findings reveal that MODIS AOD products exhibit greater reliability during clear days, whereas CALIOP AOD products are more accurate in regions characterized by high aerosol concentrations. However, CALIOP’s limited product resolution prevents it from providing adequate spatial-temporal coverage under heavy aerosol concentrations. To enhance the accuracy of MODIS AOD, this paper proposes a methodology based on various machine learning (ML) algorithms, including Random Forest (RF), Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVR), and Bayesian network. The study explores the performance of MODIS and CALIOP AOD products during moderate and pollution days in the Persian Gulf and Red Sea regions.The ML models are ranked based on their accuracy, with the order being XGBoost > MLP >SVRrbf > RF > Bayesian >SVRlinear for the annual and two semi-annual networks. In other words, the Bayesian method demonstrates higher efficiency (R2 = 0.97, 0.91, and 0.90) in handling seasonal subsets. The proposed model’s outcomes are validated using ship-based AERONET AOD data. By comparing the estimated AOD values with the ship-based AERONET AOD values and analyzing the overall correlation results, it is evident that machine learning techniques effectively provide accurate AOD estimates for moderate and pollution days.

Investigating the applicability of a simple iterative approach for aerosol optical depth (AOD) retrieval over diverse land surface types from Landsat 8 and Sentinel 2 using visible and near-infrared (VNIR) spectral bands

Previous attempts at aerosol optical depth (AOD) retrieval from top-of-atmosphere satellite signals using a simplified approach have been limited in their assessment over selective surface types, calibration sites and given the low surface reflectance components at lower wavelengths, have mostly relied, fully or partially, on near-UV or blue spectral bands for AOD retrieval. The present study attempts to investigate the applicability of a simple iterative approach for retrieval of AOD over diverse land surface types using any of the visible and near-infrared (VNIR) spectral bands. The results have been validated for 18 AERONET sites from 7 countries in South Asia and we found a strong correspondence between retrieved AOD and the AERONET AOD with good correlation (r > 0.80), both at 550 nm as well as at individual spectral bands. For image-based comparison, the results have been compared with those from MODIS products. While the retrievals show strong spatial similarity with MODIS, in absolute terms, they are mostly underestimated. Nevertheless, the coincident retrievals from both sensors do appear to be very similar, with intercomparison also showing good correlation. Overall, the iterative approach for AOD retrieval performs reasonably well for different surface types and can be easily adopted for most sensors, especially those which do not have deep blue or blue spectral bands.

Detection and analysis of Lhù'ààn Mân' (Kluane Lake) dust plumes using passive and active ground-based remote sensing supported by physical surface measurements

Abstract. There is growing recognition that high-latitude dust (HLD), originating from local drainage-basin flows, is the dominant source for certain important phenomena such as particle deposition on snow/ice. The analysis of such local plumes (including a better exploitation of remote sensing data) has been targeted as a key aerosol issue by the HLD community. The sub-Arctic Lhù'ààn Mân' (Kluane Lake) region in the Canadian Yukon is subject to regular drainage-basin, wind-induced dust plumes. This dust emission site is one of many current and potential proglacial dust sources in the Canadian north. In situ ground-based measurements are, due to constraints in accessing these types of regions, rare. Ground- and satellite-based remote sensing accordingly play an important role in helping to characterize local dust sources in the Arctic and sub-Arctic. We compared ground-based passive and active remote sensing springtime (May 2019) retrievals with microphysical surface-based measurements in the Lhù'ààn Mân' region in order to better understand the potential for ground- and satellite-based remote sensing of HLD plumes. This included correlation analyses between ground-based coarse mode (CM) aerosol optical depth (AOD) retrievals from AERONET AOD spectra, CM AODs derived from co-located Doppler lidar profiles, and OPS (optical particle sizer) surface measurements of CM particle-volume concentration (vc(0)). An automated dust classification scheme was developed to objectively identify local dust events. The classification process helped distinguish lidar-derived CM AODs which covaried with vdust(0) (during recognized dust events) and those that varied at the same columnar scale as AERONET-derived CM AOD (and thus could be remotely sensed). False positive cloud events for dust-induced, high-frequency variations in lidar-derived CM AODs in cloudless atmospheres indicated that the AERONET cloud-screening process was rejecting CM dust AODs. The persistence of a positive lidar ratio bias in comparing the CIMEL/lidar-derived value with a prescribed value obtained from OPS-derived particle sizes coupled with dust-speciation-derived refractive indices led to the suggestion that the prescribed value could be increased to optically derived values of 20 sr by the presence of optically significant dust particles at an effective radius of 11–12 µm. Bimodal CM PSDs (see Appendix B for a glossary) from full-fledged AERONET inversions (the combination of AOD spectra and almucantar radiances) also showed CM peaks at ∼ 1.3 and 5–6.6 µm radius: this, we argue, was associated with springtime Asian dust and Lhù'ààn Mân' dust, respectively. Correlations between the CIMEL-derived fine mode (FM) AOD and FM OPS-derived particle-volume concentrations suggest that remote sensing techniques can be employed to monitor FM dust (which is arguably a better indicator of the long-distance transport of HLD).

Spatial distribution of aerosols burden and evaluation of changes in aerosol optical depth using multi-approach observations in tropical region

Understanding of Aerosol optical depth (AOD) parameter is important for air quality assessment. This study aims to evaluate and validate AOD measurements from combine datasets to improve air quality for a period 2005–2020 using Aerosol Robotic Network (AERONET) at Ilorin site (8.320° N, 4.340° E) in Nigeria. AOD outputs from Community Atmosphere Model Version 6 with chemistry (CAM6-chem) at 1° horizontal resolution and Modern-Era Retrospective analysis for Research and Applications (MERRA-2) are investigated in addition to validation of two satellites AOD retrievals: Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR). Result of spatial distribution of AOD shows high values > 1 in the North and Western Sahara compared to Central Africa. Desert dust shows largest contribution in the North and Western Africa that is up to 2 magnitude larger than other aerosol types. Primary organic matter (POM) and secondary organic aerosols (SOAs) both presents high burdens with later been dominant at around 10° band, and black carbon (BC) largest burden (2.6 × 10 − 5 kgm − 2) is seen in the model from oil and gas exploration site in Nigeria. Inter-comparison of MERRA/MISR/MODIS and AERONET AOD using linear correlation of the seasonal dependence demonstrated high correlation (r = 0.864 − 0.973) subjected to Root Mean Square Error (RMSE = 0.069 − 0.211), suggesting good agreement between the datasets. When compared to seasonal mean maximum AERONET AOD value of 0.978 MERRA is ∼5%, MISR ∼28% and MODIS ∼29% lower with stronger correlations observed in the wet and pre-harmattan seasons. Similarly, MODEL AOD at 550 nm and dust burden were found to be ∼34% and ∼67% lower in context to AERONET AOD annual mean value of 0.627. Positive relationships that indicate an upward slope exist between all the computed datasets with moderate value of AERONET/CAM-chem spearman partial correlation, and MERRA/MODIS and MODIS/MISR showing strong and significant relationship with p-value less than 0.05. Low variance is observed with all measurements except in MERRA.

An algorithm for retrieving aerosol optical depth from Landsat 8 Operational Land Imager in Vietnam

High-resolution aerosol products offer advantages over lower spatial-resolution products for monitoring pollution in urban areas and industrial zones. Therefore, in this study we develop a Landsat 8 algorithm to retrieve aerosol optical depth (AOD) based on the MODIS Dark Target (DT) algorithm. This algorithm uses the direct surface reflectance of Landsat 8 to build a surface reflectance relationship between the visible (BLUE, RED) and short-wave infrared bands (SWIR2). AOD data from AERONET stations in the study area and AOD data from MODIS 3 km were used for validation of the estimated Landsat 8 AOD. The estimated AOD was highly correlated at most AERONET AOD, with =0.98, (RMSE = 0.3), except Bac Lieu, but only had a moderate correlation with MODIS AOD which was attributed to the difference in data retrieval time and associated changes in climate conditions. This is the first high-resolution AOD retrieval in Vietnam, leading to more practical applications for air quality monitoring.

Variation of Aerosol Optical Properties over Cluj-Napoca, Romania, Based on 10 Years of AERONET Data and MODIS MAIAC AOD Product

Aerosols play an important role in Earth’s climate system, and thus long-time ground- based measurements of aerosol optical properties are useful in understanding this role. Ten years of quality-assured measurements between 2010 and 2020 are used to investigate the aerosol climatology in the Cluj-Napoca area, in North-Western Romania. In this study, we analyze the aerosol optical depth (AOD), single scattering albedo (SSA) and angstrom exponent obtained by the CIMEL sun photometer, part of the aerosol robotic network (AERONET), to extract the seasonality of aerosols in the region and investigate the aerosol climatology of the area. Higher aerosol loads are found during July and August. The angstrom exponent has the lowest values in April and May, and the highest in August. The classification of aerosols using AERONET data is performed to separate dust, biomass burning, polluted urban, marine and continental-dominant aerosol mixtures. In addition, the study presents the validation efforts of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) dataset against AERONET AOD over a 10-year period.

Performance evaluation of MODIS and VIIRS satellite AOD products over the Indian subcontinent

In the present study, the first systematic performance evaluation of aerosol optical depth (AOD) products retrieved using two satellite sensors i.e., Visible Infrared Imaging Radiometer Suite (VIIRS) and Aqua-Moderate-Resolution Imaging Spectroradiometer (MODIS) is carried out over India. We have used ground-based AOD from AERONET at 550 nm wavelength for inter-comparison with MODIS Aqua version C6.1 (C061) Deep Blue (DB) aerosol product and VIIRS/SNPP collection version 1.1 (V1.1) DB aerosol product over the time span of 7-year (2014–2020) observation periods. For validation, the average value of satellite pixels falling within the box of 50 Km x 50 Km keeping the AERONET station at the center is retrieved. The average daily data from the AERONET sun photometer (2014–2019) were obtained within ±15 min of satellite overpass time. Statistical parameters like correlation coefficient (R), RMSE, MAE, and RMB were calculated. The uncertainty of satellite AOD is evaluated using an envelope of Expected Error (EE = ±0.05 + 0.15 AOD for land). Statistical analysis shows that the MODIS AOD product outperforms VIIRS-retrieved AOD. The AOD retrieved from both sensors yields a high correlation (0.86—Jaipur, 0.79—Kanpur, 0.84—Gandhi College, and 0.74—Pune for MODIS and 0.75—Jaipur, 0.77—Kanpur, 0.49—Gandhi College, and 0.86—Pune for VIIRS) and low MAE (0.12—Jaipur, 0.20—Kanpur, 0.15—Gandhi College, and 0.09—Pune for MODIS and 0.13—Jaipur, 0.13—Kanpur, 0.26—Gandhi College, and 0.10—Pune for VIIRS). Other statistical measures such as RMSE, RMB, and P also suggest similar performance. More than 66% of the total data fall within the range of EE for both the satellite products at each station. Spatial comparison exhibits the same AOD pattern seasonally as well as annually having a minimum bias from −0.3 to +0.3 between MODIS and VIIRS. Slight underestimation and overestimation are observed in all the stations by MODIS, whereas VIIRS continuously underestimates AOD with increase in optical depth, suggesting improvements in the aerosol model and surface reflection in retrieval. Overall, the comparison of ground AERONET AOD reveals better accuracy of MODIS AOD with that of VIIRS satellite datasets over India.

High-Resolution Aerosol Optical Depth Retrieval in Urban Areas Based on Sentinel-2

In this paper, an improved aerosol optical depth (AOD ) retrieval algorithm is proposed based on Sentinel-2 and AErosol RObotic NETwork (AERONET ) data. The surface reflectance for AOD retrieval was estimated from the image that had minimal aerosol contamination in a temporal window determined by AERONET data. Validation of the Sentinel-2 AOD retrievals was conducted against four Aerosol Robotic Network (AERONET ) sites located in Beijing. The results show that the Sentinel-2 AOD retrievals are highly consistent with the AERONET AOD measurements (R = 0.942), with 85.56% falling within the expected error. The mean absolute error and the root-mean-square error are 0.0688 and 0.0882, respectively. In addition, the AOD distribution map obtained by this algorithm well reflects the fine-spatial-resolution changes in AOD distribution. These results suggest that the improved high-resolution AOD retrieval algorithm is robust and has the potential advantage of retrieving high-resolution AOD over urban areas.

JEDI‐Based Three‐Dimensional Ensemble‐Variational Data Assimilation System for Global Aerosol Forecasting at NCEP

AbstractA three‐dimensional ensemble‐variational global aerosol data assimilation system based on the Joint Effort for Data assimilation Integration (JEDI) was developed for the Global Ensemble Forecast System‐Aerosols (GEFS‐Aerosols) coupled with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model at the National Centers for Environmental Prediction. Aerosol mass mixing ratios in GEFS‐Aerosols were selected as control or analysis variables and were adjusted by assimilating 550 nm Aerosol Optical Depth (AOD) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments onboard the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite produced by the National Environmental Satellite, Data, and Information Service (NESDIS) at National Oceanic and Atmospheric Administration (NOAA). The original NOAA/NESDIS S‐NPP VIIRS Level 2.0 550 nm AOD retrievals were converted to JEDI Interface for Observation Data Access format. AOD forward operator and its tangent‐linear and adjoint models were implemented based on GOCART in JEDI Unified Forward Operator. A stochastically perturbed emission (SPE) approach was developed in the Common Community Physics Package‐based GEFS‐Aerosols to account for aerosol emission uncertainty. One‐month retrospective and three‐month near‐real‐time experiments consistently showed improved GEFS‐Aerosols analyses and forecasts from assimilating VIIRS 550 nm AOD retrievals against independent NASA Aqua and Terra Moderate Resolution Imaging Spectroradiometer AOD retrievals, Aerosol Robotic Network AOD, and independent AOD and aerosol analyses from NASA and European Centre for Medium‐Range Weather Forecasts. Through scaling and perturbing aerosol emissions, SPE enhanced ensemble error‐spread consistency and further improved AOD assimilation. The valid‐time‐shifting ensemble approach in a cost‐effective manner of populating background ensembles showed positive impacts on AOD assimilation.