MODIS Aerosol Optical Thickness Research Articles

How to Measure the Amount of Aerosol Optical Thickness in the Atmosphere in a Simple Way: A Calitoo Handheld Sun-Photometer Measurement

To have useful information about the effects of atmospheric particles on visibility degradation, extinction of solar radiation, climate changes, and tropospheric corrections in remote sensing, aerosol optical thickness (AOT) plays a crucial role. So, the measurement of AOT is an essential task for atmospheric physicists. In this regard, this article presents a systematic way in a simple form using a Calitoo handheld sun-photometer (SPM) to study atmospheric AOT. Zanjan city is selected for a period of six months as an example due to existing reference data for comparison. Hence, we use a schedule distributed in seven times, including 8:00, 9:30, 10:45, solar noon, 13:45, 15:30, and 17:00 in local time. The used wavelengths for recording are 465, 540, and 619 nm. Our data analysis shows that the mean value for AOT at those wavelengths are 0.25, 0.21, and 0.20, respectively. Also, the mean Ångström exponent (AE) extracted from the AOTs at 465, and 619 nm is 0.80. Accordingly, AE versus AOT plot demonstrates that most of the particles in Zanjan’s atmosphere are classified as urban industrial (∼64%) and dust plus mixed (∼36%) particles. In addition, to have a metric, a comparison between the AOT extracted from simultaneous measurements of Calitoo handheld and Cimel automatic SPMs is employed that indicates a strong correlation (r = 0.99) between them. Furthermore, OMI, Aqua MODIS, and Terra MODIS AOT products are compared with the Calitoo SPM AOT data. The results reveal that there is a better relationship between MODIS and Calitoo data than OMI. This study leads us to conclude that it can open new insights by simple instruments to study the atmospheres that are unknown to scientists due to the limitations of geographical stations.

Discrimination of Biomass-Burning Smoke From Clouds Over the Ocean Using MODIS Measurements

Smokes from biomass burning can contribute substantial amounts of hazardous substances and carbon to the atmosphere. These substances can be transported seaward and deposited on the ocean surface. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) images are used to map the relative smoke concentration over the ocean between November 8 and 11, 2018 from the recent California fires, with the ultimate goal of developing a generally applicable approach to map smokes over oceans. Because both biomass-burning smokes and clouds can produce strong backscattering signals, two key differences are used to separate them: 1) water-vapor absorption in certain wavelengths only occurs in clouds and 2) cumulus and cirrus clouds occur at different altitudes, therefore, bearing different thermal signatures. Based on these observations, a decision-tree method is developed to separate smokes from clouds. First, MODIS top-of-atmosphere (TOA) reflectance at 936 nm is used to detect both clouds and smokes over oceans. Then, brightness temperature derived from the 9730-nm band is used to separate cirrus from others. Finally, a water absorption depth (WAD) index is used to distinguish cumulus clouds from smokes, whose relative concentration in each image pixel is estimated from the MODIS TOA reflectance at 859 nm. Such derived smoke distribution and concentration are validated using concurrent Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO) data, which provide the fine mode aerosol optical thickness (AOT) of smokes. Test of the approach over the recent Australia fires shows promising results, suggesting that the approach might be implemented by operational agencies to monitor and quantify smokes from biomass burning on a routine basis.

Superior PM2.5 Estimation by Integrating Aerosol Fine Mode Data from the Himawari-8 Satellite in Deep and Classical Machine Learning Models

Artificial intelligence is widely applied to estimate ground-level fine particulate matter (PM2.5) from satellite data by constructing the relationship between the aerosol optical thickness (AOT) and the surface PM2.5 concentration. However, aerosol size properties, such as the fine mode fraction (FMF), are rarely considered in satellite-based PM2.5 modeling, especially in machine learning models. This study investigated the linear and non-linear relationships between fine mode AOT (fAOT) and PM2.5 over five AERONET stations in China (Beijing, Baotou, Taihu, Xianghe, and Xuzhou) using AERONET fAOT and 5-year (2015–2019) ground-level PM2.5 data. Results showed that the fAOT separated by the FMF (fAOT = AOT × FMF) had significant linear and non-linear relationships with surface PM2.5. Then, the Himawari-8 V3.0 and V2.1 FMF and AOT (FMF&AOT-PM2.5) data were tested as input to a deep learning model and four classical machine learning models. The results showed that FMF&AOT-PM2.5 performed better than AOT (AOT-PM2.5) in modelling PM2.5 estimations. The FMF was then applied in satellite-based PM2.5 retrieval over China during 2020, and FMF&AOT-PM2.5 was found to have a better agreement with ground-level PM2.5 than AOT-PM2.5 on dust and haze days. The better linear correlation between PM2.5 and fAOT on both haze and dust days (dust days: R = 0.82; haze days: R = 0.56) compared to AOT (dust days: R = 0.72; haze days: R = 0.52) partly contributed to the superior accuracy of FMF&AOT-PM2.5. This study demonstrates the importance of including the FMF to improve PM2.5 estimations and emphasizes the need for a more accurate FMF product that enables superior PM2.5 retrieval.

Climatology of Fine and Coarse Mode Aerosol Optical Thickness Over East and South Asia Derived From POLDER/PARASOL Satellite

AbstractThe climatological characteristics of total aerosol optical thickness (AOT) together with fine and coarse mode AOT (fAOT and cAOT) and fine mode fraction (FMF = fAOT/AOT) over East and South Asia are presented here for the period March 2005 to October 2013. The characteristics are retrieved from POLDER/PARASOL measurements using recently developed aerosol component module of GRASP algorithm. The satellite retrievals were validated and presented a good agreement with the AERONET products (e.g., R of ~0.8–0.9 for AOT, fAOT, Ångström exponent, and FMF). The results show a seasonal cycle and strong relation to the emission sources. For example, FMF has a peak of nearly 1.0 in March in the Indo‐China Peninsula region, reaching a minimum of about 0.4 in April in Northwestern China region. These patterns can be explained by dominance of anthropogenic pollution or desert dust emissions. Maps of fine, coarse, and total AOT, as well as of FMF for the East and South Asia region, are presented and analyzed in scope of seasonal and interannual variabilities.

Regression analysis of MODIS aerosol optical thickness and air quality index in Xiamen City

Regression analysis of MODIS aerosol optical thickness and air quality index in Xiamen City

Estimation and Validation of Collection 6 Moderate Resolution Imaging Spectroradiometer Aerosol Products for East Asia

The operational aeroscthm, a substantially improved version of the collection 5 (C5) algorithm, uses an enhanced aerosol optical thickness (AOT) retrieval process consisting of new surface reflection and aerosol models. This study reports on the estimation and validation of the two latest versions, the C5 and C6 MODIS aerosol products over the East Asian region covering 20°N to 56°N and 80°E to 150°E. This study also presents a comparative validation of the two versions(C5 and C6) of algorithms with different methods (Dark Target(DT) and Deep Blue (DB) retrieval methods) from the Terra and Aqua platforms to make use of the Aerosol Robotic Network(AERONET) sites for the years 2000–2016. Over the study region, the spatially averaged annual mean AOT retrieved from C6 AOT is about 0.035(5%) less than the C5 counterparts. The linear correlations between MODIS and AERONET AOT are R = 0.89 (slope = 0.86) for C5 and R = 0.95 (slope = 1.00) for C6. Moreover, the magnitude of the mean error in C6 AOT-the difference between MODIS AOT and AERONET AOT-is 40% less than that in C5 AOT.

Satellite-based PM2.5 estimation using fine-mode aerosol optical thickness over China

Accurate estimation of ground-level PM2.5 from satellite-derived aerosol optical thickness (AOT) presents various difficulties. This is because the association between AOT and surface PM2.5 can be affected by many factors, such as the contribution of fine mode AOT (FM-AOT) and the weather conditions. In this study, we compared the total AOT and FM-AOT for surface PM2.5 estimation using ground-based measurements collected in Xingtai, China from May to June 2016. The correlation between PM2.5 and FM-AOT was higher (r = 0.74) than that between PM2.5 and total AOT (r = 0.49). Based on FM-AOT, we developed a ground-level PM2.5 retrieval method that incorporated a Simplified Aerosol Retrieval Algorithm (SARA) AOT, look-up table–spectral deconvolution algorithm (LUT-SDA) fine mode fraction (FMF), and the PM2.5 remote sensing method. Due to the strong diurnal variations displayed by the particle density of PM2.5, we proposed a pseudo-density for PM2.5 retrieval based on real-time visibility data. We applied the proposed method to determine retrieval surface PM2.5 concentrations over Beijing from December 2013 to June 2015 on cloud-free days. Compared with Aerosol Robotic Network (AERONET) data, the LUT-SDA FMF was more easily available than the Moderate Resolution Imaging Spectroradiometer (MODIS) FMF. The derived PM2.5 results were compared with the ground-based monitoring values (30 stations), yielding an R2 of 0.64 and root mean square error (RMSE) = 18.9 μg/m3 (N = 921). This validation demonstrated that the developed method performed well and produced reliable results.

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data.

Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi’an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5.

Evaluation of Polarized Remote Sensing of Aerosol Optical Thickness Retrieval over China

The monitoring capability of a polarized instrument (POLDER) under high aerosol loading conditions over China is investigated. The aerosol optical thickness (AOT), which infers the aerosol burden, is used to measure the satellite monitoring capabilities. AOT products retrieved from POLDER on low aerosol loading days, and products from a radiometric instrument (MODIS) on high and low aerosol loading days, are presented for comparison. Our study reveals that for high aerosol days, the monitoring capability of the polarized instrument is lower than that of the traditional instrument. The accuracy of matched POLDER fine-AOTs is lower than that of MODIS-matched AOTs. On low aerosol loading days, the performance of the polarized instrument is good when monitoring the aerosol optical thickness. Further analysis reveals that for the high aerosol loading days, the mean relative errors of matched POLDER fine AOTs and MODIS AOTs with respect to AERONET measurements are 44% and 16%, respectively. For the low aerosol loading days, the mean relative errors of POLDER and MODIS measurements with respect to AERONET measurements are 41% and 40%, respectively. During high aerosol days, POLDER-retrieved fine-AOTs reveal a poor accuracy with only 14% of matches falling within the error range, which is nearly one fourth of the MODIS regression results (51.59%). For the low aerosol loading days, the POLDER regression results are good. Approximately 62% of the POLDER measurements fall within the expected error range ±(0.05 + 15%) compared with the AERONET observed values.

Estimation of potential source regions of PM2.5 in Beijing using backward trajectories

Possible source regions of the PM2.5 mass concentrations in Beijing, China have been identified using the Potential Source Contribution Function (PSCF) analysis based on the backward trajectory modeling. Five–day backward trajectories for the arrival of air masses to the ground observation station at Chaoyang Olympic Sports Center were calculated, using the Hybrid Single–Particle Lagrangian Integrated Trajectory (HYSPLIT) model in 2013. The PSCF results demonstrated that regional sources in different seasons could be one of the crucial contributors to PM2.5 mass concentrations in Beijing, especially in the winter season. Compared PSCF results with the MODIS fine–mode aerosol optical thickness (AOTfine), the regions showing high AOTfine especially the Southern Hebei, Northern Henan and Southwest Shandong are the significant potential PM2.5 contributors to Beijing. Environmental authorities may use the derived models and results for pinpointing the source areas, for improving air quality in Beijing City.

Spatiotemporal Spectral Variations of AOT in India’s EEZ over Arabian Sea: Validation of OCM-II

We report the results of sun-photometric measurements of Aerosol Optical Thickness (AOT) in India’s Exclusive Economic Zone (EEZ) over the Arabian Sea along with synchronous Ocean Color Monitor (OCM-II) derived AOT estimates during December 12, 2009–January 10, 2010. Relatively higher values of Angstrom exponent (α) around 1.2 near coast and 0.2–0.8 in the India’s EEZ, observed during the cruise period, indicate the presence of smaller particles near the coast due to anthropogenic activities; and larger particles in the India’s EEZ due to advection of pollutants from Indian subcontinent via long-range transport. Results related to α and its derivative reveal four different aerosol types (urban-industrial, desert-dust, clean-marine, and mixed-type) with varying fraction during the study period. Surface radiative forcing due to aerosols is found to be 20 W/m2 over India’s EEZ. OCM-derived AOTs showed good corroboration with in situ measurements with a correlation coefficient of about 0.95. A reasonably good correlation was also observed between AOT and wind speed (R = 0.6); AOT and relative humidity (R = 0.58). The concurrent MODIS AOT data also agree well with those observed by the OCEANSAT (OCM-II) satellite during the campaign period.

Impact of aerosol on air temperature in Kuwait

This work uses MODIS aerosol optical thickness (AOT) data observed over Kuwait during the 7-year interval 2000–2007. The values of AOT and the Ångström wavelength exponent ( α) show a clear annual cycle. These data are categorized into two catalogues in terms of the values of the AOT of the 870 nm channel ( τ 870). One catalogue (71 days) includes days with high values of AOT ( τ 870 ≥ 0.75). The most probable “modal” value of α for these days is 0.52. The other catalogue (1162 days) consists of the background days with a modal value ~ 1.1 for the exponent α. This analysis is extended to include water vapor content (WVC), surface wind speed (V), visibility (Vis) and the diurnal temperature range (DTR). Chree's method of superposed-epoch analysis is applied to these parameters in order to compare the variation in the daily averages during days with high AOT values with respect to background days. The high values of AOT during the 71 days are positively correlated with aerosol size, near-surface winds and poor visibility. This concludes that the aerosol particles during these days were mostly dust. The mean daily value of the DTR (Δ T) and visibility reduced significantly during these days. This reduction on DTR is a direct result of increasing the atmospheric opacity due to the presence of dust.

Monitoring of urban air pollution from MODIS aerosol data: effect of meteorological parameters

Remote sensors designed specifically for studying the atmosphere have been widely used to derive timely information on air pollution at various scales. Whether the satellite-generated aerosol optical thickness (AOT) data can be used to monitor air pollution, however, is subject to the effect of a number of meteorological parameters. This study analyses the influence of four meteorological parameters (air pressure, air temperature, relative humidity, and wind velocity) on estimating particulate matter (PM) from MODIS AOT data for the city of Nanjing, China during 2004–2006. After the PM data were correlated with the AOT data that had been divided into four chronological seasons, a minimum correlation coefficient of 0.47 was found for the winter season, but a much stronger correlation (r > 0.80) existed in summer and autumn. Similar analyses were carried out after all observations were clustered into four groups based on their meteorological similarity using the K-Means analysis. Grouping caused more observations to be useable in the monitoring of air pollution than season-based analysis. Of the four groups, three had a correlation coefficient higher than 0.60. Grouping-based analysis enables the pollution level to be determined more accurately from MODIS AOT data at a higher temperature and relative humidity, but a lower air pressure and wind velocity. The accuracy of monitoring air pollution is inversely related to the pollution level. Thus, remote sensing monitoring of air pollution has its limits.

Temporal trend in anthropogenic sulfur aerosol transport from central and eastern Europe to Israel

Decrease of sulfur emissions in central and eastern Europe over the past 3 decades is well documented and linked to changes in economic activity, use of different fuels, addition of pollution controls, etc. These changes result in a decreasing trend of sulfate aerosol and aerosol forcing over the source region, but also at a receptor site located in southern Israel, thousands of kilometers downwind of the original source. A combination of several independent observations, namely, satellite and ground‐based remote sensing, in situ aerosol sampling, and backward trajectory analysis, was implemented to show significant downward trends in fine particle aerosol optical thickness (AOT), in general, and sulfur aerosol, in particular, between 1995 and 2007. For the study years, MODIS‐Terra observations over central and eastern Europe show 38% reduction of fine AOT. At the reception site in southern Israel, 43% reduction of fine AOT was observed by a ground‐based sun/sky photometer and 25% reduction of sampled fine aerosols was obtained. During the corresponding observation periods, the coarse mode AOT has remained constant. The majority of the backward trajectories, where meaningful sulfur events were observed at the receptor site, are originated from eastern and central Europe. The aerosol radiative effect at top of the atmosphere has become less negative during the past decade, decreasing by 30% in Europe and 67% in Israel. The decrease in negative cloud‐free aerosol forcing is consistent with observations of “global brightening” reported since 1990.

Retrieving global aerosol sources from satellites using inverse modeling

Abstract. Understanding aerosol effects on global climate requires knowing the global distribution of tropospheric aerosols. By accounting for aerosol sources, transports, and removal processes, chemical transport models simulate the global aerosol distribution using archived meteorological fields. We develop an algorithm for retrieving global aerosol sources from satellite observations of aerosol distribution by inverting the GOCART aerosol transport model. The inversion is based on a generalized, multi-term least-squares-type fitting, allowing flexible selection and refinement of a priori algorithm constraints. For example, limitations can be placed on retrieved quantity partial derivatives, to constrain global aerosol emission space and time variability in the results. Similarities and differences between commonly used inverse modeling and remote sensing techniques are analyzed. To retain the high space and time resolution of long-period, global observational records, the algorithm is expressed using adjoint operators. Successful global aerosol emission retrievals at 2°×2.5 resolution were obtained by inverting GOCART aerosol transport model output, assuming constant emissions over the diurnal cycle, and neglecting aerosol compositional differences. In addition, fine and coarse mode aerosol emission sources were inverted separately from MODIS fine and coarse mode aerosol optical thickness data, respectively. These assumptions are justified, based on observational coverage and accuracy limitations, producing valuable aerosol source locations and emission strengths. From two weeks of daily MODIS observations during August 2000, the global placement of fine mode aerosol sources agreed with available independent knowledge, even though the inverse method did not use any a priori information about aerosol sources, and was initialized with a "zero aerosol emission" assumption. Retrieving coarse mode aerosol emissions was less successful, mainly because MODIS aerosol data over highly reflecting desert dust sources is lacking. The broader implications of applying our approach are also discussed.

MODIS AOT Based Inverse Modeling for Asian Dust

Inverse modeling of Asian dust was performed using MODIS coarse-mode aerosol optical thickness (AOT) and a regional adjoint dust model. The a posteriori AOT shows better agreement with the MODIS AOT and OMI Aerosol index (AI). Independent validation with the NIES Lidar network shows that the inversion leads to a significant improvement and reduces root mean squares of differences by 29-32% at Seoul, Matsue, and Toyama. The inversion results show considerably greater dust emissions over the Gobi Desert and Mongolia. Especially, optimized emissions are estimated as 8.1 Tg (352% of the a priori) for 29 March and 19.5 Tg (325%) for 30 March. Comparison with inversion results using NIES Lidar (Yumimoto et al. 2008) shows consistency with our a posteriori dust emissions which underscores the feasibility of dust inversion with satellite optical observations, and encourages combined inversion that assimilates the Lidar extinction coefficient (vertical profiles) and MODIS AOT (horizontal distribution) concurrently. However, the a posteriori results might reflect effects of air pollutants from highly industrialized regions, which might contaminate the assimilated results. Further quality control of the input for the data assimilation is necessary in future studies.

Mesoscale modeling of Central American smoke transport to the United States: 2. Smoke radiative impact on regional surface energy budget and boundary layer evolution

During 20 April to 21 May 2003, large amounts of smoke aerosols from Central American Biomass Burning (CABB) fires were transported to southeastern United States. Using a coupled aerosol, radiation, and meteorology model built upon the heritage of the Regional Atmospheric Modeling System (RAMS) with new capabilities called the Assimilation and Radiation Online Modeling of Aerosols (AROMA), this paper, the second of a two‐part series, investigates smoke radiative impact on the regional surface energy budget, temperature and relevant boundary layer processes. Comparisons with limited ground‐based observations and MODIS aerosol optical thickness (AOT) showed that model consistently simulated the smoke AOT and smoke radiative impacts on the 2 m air temperature (2mT) and downward shortwave irradiance (DSWI). Over 30 days the 24‐hour mean smoke AOT was 0.18 (at 0.55 μm) near the smoke source region (Yucatan Peninsula and southern Mexico), and 0.09 in downwind region (e.g., southern Texas), both showing a diurnal variation of 24%. Maximum AOT occurred during late afternoon and minimum during early morning in smoke source region. The smoke radiative effects were dominant mostly during the daytime and resulted in the decrease of DSWI, sensible heat and latent heat by 22.5 Wm−2, 6.2 Wm−2, and 6.2 Wm−2, respectively, near the source region, in contrast to 15.8 Wm−2, 4.7 Wm−2, and 7.9 Wm−2, respectively, in downwind regions. Both maximum and minimum 2mT were decreased, and the overall diurnal temperature range (DTR) was reduced by 0.31°C and 0.26°C in the smoke source and downwind regions, respectively. The smoke absorption of solar radiation increased the lapse rate by 0.1–0.5 K/day in the planetary boundary layer (PBL), thus warming the air over the ocean surface. However, over the land surface where the coupling between the lower PBL and the cooler land surface is strong, such warming only occurred in the upper PBL and is amendable to the diurnal variation of smoke emission. The simulation numerically verifies the smoke self‐trapping feedback mechanism proposed by Robock (1988), where the increase of the atmospheric stability in the PBL caused by the smoke radiative effects further traps more smoke aerosols in the lower PBL. Such feedbacks, when coupled with favorable synoptic systems, may have important implications for air quality modeling and hydrological processes.

Separation of fine and coarse aerosol modes in MFRSR data sets

A new MFRSR data analysis algorithm is presented. Our earlier algorithm assumed a monomodal aerosol size distribution, while the new algorithm allows us to partition the aerosol optical thickness into fine and coarse aerosol modes. In addition, we retrieve the fine mode effective radius and Angstrom exponent. A bimodal gamma distribution is used to describe the aerosol particle size distribution. The algorithm has been tested using a multi‐year data set from the local MFRSR network at the DOE Atmospheric Radiation Measurement (ARM) Program site in Southern Great Plains (SGP). Our retrieved aerosol optical thicknesses (total, fine, and coarse) are compared with the corresponding AERONET almucantar retrieval results derived from a CIMEL sunphotometer co‐located with the MFRSR at the SGP Central Facility. A constrained variant of the algorithm (zero NO2 column values) has been used to define the range of physically justified values of the fine mode effective radius, and for comparison with AERONET particle size retrievals. We use the multiple MFRSR measurements obtained during the year 2000 at the SGP Extended Facilities to examine geographical and seasonal variability of aerosol properties. A correspondence has been found between the geographical variation in the fine mode particle size and aerosol composition (nitrates versus sulfates) as measured by National Atmospheric Deposition Program. We similarly find good correspondence between our retrieved aerosol sizes and the PM2.5 to PM10 ratios obtained from EPA AirData monitoring. Examination of the data from the SGP Central Facility obtained for the period 1993–1997 reveals a decreasing trend in coarse mode aerosol optical thickness during the 1993–1995 period, consistent with the decay of the stratospheric aerosol following the 1991 eruption of Mt. Pinatubo. In contrast, fine mode optical thickness exhibits only seasonal variability with summer maxima during this period.