Multi-angle Imaging SpectroRadiometer Aerosol Optical Depth Research Articles

Aerosol characteristics in CMIP6 models' global simulations and their evaluation with the satellite measurements

AbstractGlobal and regional trends of the Aerosol Optical Depth (AOD) from Coupled Model Intercomparison Project (CMIP) Phase 6 simulations for the study period 1971–2014 were compared against the satellite retrievals and the intermodel variations were analysed. The AOD from multimodel mean (MMM) of eight general circulation models (GCMs) has been evaluated against the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi‐angle Imaging Spectro Radiometer (MISR) AOD for the 2001–2014 period. Angstrom exponents (AE and its first derivative) that represent the size distribution of aerosols are estimated globally from the perturbed initial condition ensemble of MRI‐ESM2‐0 and MPI‐ESM‐1‐2‐HAM models to report the aerosol variations through their size distribution. We found that the global AOD obtained from the MMM8 showed an insignificant decreasing trend, while this trend is significantly positive over the northern tropical region. The MMM8 has overestimated the MODIS AOD over North Africa, India, China, and Australia while this overestimation is confined to North Africa and eastern China when compared against MISR AOD. The absolute percent bias of MMM8 is 28.1% and 24.1% over the globe when compared against MODIS and MISR AOD, respectively. The spatial pattern of AE showed the dominance of fine‐ and coarse‐mode particles during the boreal/austral winter and summer seasons, respectively, that replicate the seasonality of aerosols. The AE derived from MPI‐ESM‐1‐2‐HR demonstrated better agreement with AATSR SU's (Advanced Along Track Scanning Radiometer instrument series, with the algorithm developed by Swansea University) AE (550–870 nm). On the other hand, MRI‐ESM2‐0 consistently underestimated AE across different regions and wavelength ranges, suggesting an over representation of larger aerosol particles in the model's portrayal of aerosol size distribution compared to satellite observations.

Validation, analysis, and comparison of MISR V23 aerosol optical depth products with MODIS and AERONET observations

The latest Multi-angle Imaging Spectro Radiometer (MISR) Version (V) 23 aerosol optical depth (AOD) products were released, with an improved spatial resolution of 4.4 km, providing an unprecedented opportunity for the refined regional application. To ensure the reliability of their applications and build a scientific reference for the further optimization, it is imperative to conduct a comprehensive evaluation, especially for the unique size-resolved AOD products: small-size AOD (AODS, representing the contribution of fine-mode aerosols), medium-size AOD (AODM), and large-size AOD (AODL), and AODM+L represents the AOD part of coarse-mode aerosols. AErosol RObotic NETwork (AERONET) and MODerate-resolution Imaging Spectroradiometer (MODIS) Collection (C) 6.1 aerosol products from 2001 to 2020 are utilized for the validation, analysis, and inter-comparison, considering three spatial scales and four key factors. In general, MISR V23 aerosol products show a good accuracy compared with AERONET. The best performance for all AOD products appears in forest units (the highest R ~ 0.93, data percentage within Expected Error bounds, %EE > 93), related to the inactive human activity and dark underlying surface. Dependences of retrieval deviations illustrate that the performance of MISR AOD deteriorates as aerosol loading increases. Namely, with the increase of aerosols, total AOD (AODT) and AODS show increasing negative deviations, while increasing positive deviations are observed for AODM+L. This suggests that the Empirical Orthogonal Functions do not perform well in this situation, since numerous aerosol particles can obstruct the underlying reflection and reduce the surface spectral contrast. In addition, AODT and AODS often exhibit anomalous positive deviations in areas with low vegetation cover, such as deserts, revealing that MISR will overestimate aerosol content over bright surfaces and in environments dominated by coarse-mode particles. The above findings not only deepen the understanding of MISR aerosols products from multiple perspectives, but also provide useful information for the product improvement.

Influence of observation angle change on satellite retrieval of aerosol optical depth

Use of multi-angle sensors, which can perform ground observations in different view directions, provides an important technical means to acquire more comprehensive and detailed remote sensing information. This study explored the effect of angle changes on satellite-based aerosol optical depth (AOD) retrieval using 2016–2018 Multi-angle Imaging SpectroRadiometer (MISR) data in the Yangtze River Delta. The Moderate resolution Imaging Spectroradiometer (MODIS) surface reflectance determined by the MODIS V5.2 algorithm and MODIS BRDF data are used to obtain MISR surface reflectance at different angles after a spectral conversion between MODIS and MISR, and then MISR AOD with different angles were retrieved based on the MISR surface reflectance at different angles. Moreover, the errors and their formation mechanism in retrieving AOD using MISR remote sensing data at different angles were analyzed based on the Global Aerosol Robotic Network (AERONET) ground observations. We used six indicators, including correlation coefficient (R), significant level (P value), mean absolute error (MAE), mean relative error (MRE) root mean square error (RMSE) and relative mean bias (RMB), and AERONET AOD is used to verify the MISR AOD. The results show that at Taihu and Xuzhou stations, the highest R values are 0.81 (Aa AOD) and 0.75 (An AOD), and the minimum RMSE values at Taihu and Xuzhou-CUMT stations are 0.11 (Aa AOD) and 0.20 (An AOD), respectively. Incorrect surface reflectance measurement is the root cause of the angle effect in MISR AOD retrieval. With the same surface reflectance deviation, the larger the MISR observation angle, the closer the retrieved MISR AOD is to the AOD observed by AERONET. The AOD retrieval of the forward observation angle is closer to AERONET AOD than that of backward observation angle, and the retrieved error is greatest at the vertical angle (nadir). Therefore, sensors for vertical ground observation are not suitable for AOD retrieval, and sensors in non-vertical directions, especially forward to the ground, are more favorable to MISR AOD retrieval.

A surface reflectance correction model to improve the retrieval of MISR aerosol optical depth supported by MODIS data

Combined use of different satellite sensors are known to improve retrievals of aerosol optical depth (AOD). In this study, we propose a new method for retrieving Multi-angle Imaging SpectroRadiometer (MISR) AOD data supported by Moderate Resolution Imaging Spectroradiometer (MODIS) data in Jiangsu Province, China, over the period of 2016–2018 using MODIS L1B, bidirectional reflectance distribution function (BRDF), MISR 1B2T, and ground-measured AOD data. This method is based on the surface reflectance determined by the MODIS V5.2 algorithm. Through the observation angle and spectral conversion between different sensors, the MISR AOD can be obtained. The correlation coefficient (R) and root-mean-square error (RMSE) between the retrieved MISR and ground-measured AOD data varied between different seasons. The accuracy of the MISR AOD retrieval was notably improved after correcting the MISR surface reflectance. Therefore, the method proposed in this study is feasible for the retrieval of MISR AOD supported by MODIS data, and will be applicable to atmospheric environmental monitoring over large areas in the future.

Combining low-cost, surface-based aerosol monitors with size-resolved satellite data for air quality applications

Abstract. Poor air quality is the world's single largest environmental health risk, and air quality monitoring is crucial for developing informed air quality policies. Efforts to monitor air pollution in different countries are uneven, largely due to the high capital costs of reference air quality monitors (AQMs), especially for airborne particulate matter (PM). In sub-Saharan Africa, for example, few cities operate AQM systems. It is thus important to examine the potential of alternative monitoring approaches. Although PM measurements can be obtained from low-cost optical particle counters (OPCs), data quality can be an issue. This paper develops a new method using raw aerosol size distributions from multiple, surface-based low-cost OPCs to constrain the Multiangle Imaging SpectroRadiometer (MISR) component-specific, column aerosol optical depth (AOD) data, which contain some particle-size-resolved information. The combination allows us to derive surface aerosol concentrations for particles as small as ∼0.1 µm in diameter, which MISR detects but are below the OPC detection limit of ∼0.5 µm. As such, we obtain better constraints on the near-surface particulate matter (PM) concentration, especially as the smaller particles tend to dominate urban pollution. We test our method using data from five low-cost OPCs deployed in the city of Nairobi, Kenya, from 1 May 2016 to 2 March 2017. As MISR passes over Nairobi only once in about 8 d, we use the size-resolved MISR AODs to scale the more frequent Moderate Resolution Imaging Spectrometer (MODIS)-derived AODs over our sites. The size distribution derived from MISR and MODIS agrees well with that from the OPCs in the size range where the data overlap (adjusted-R2∼0.80). We then calculate surface-PM concentration from the combined data. The situation for this first demonstration of the technique had significant limitations. We thus identify factors that will reduce the uncertainty in this approach for future experiments. Within these constraints, the approach has the potential to greatly expand the range of cities that can afford to monitor long-term air quality trends and help inform public policy.

Characterization of Aerosol Type Over East Asia by 4.4 km MISR Product: First Insight and General Performance

AbstractThe lack of aerosol type information has largely hindered satellite products from further applications such as constraining model simulations and quantifying aerosol climate effects. The recent Version (V) 23 Multi‐angle Imaging Spectroradiometer (MISR) aerosol products with an enhanced spatial resolution at 4.4 km enable an unprecedented chance to explore aerosol types and associated processes in the regional scale. Here we provide a comprehensive insight into the characterization of MISR aerosol optical and microphysical properties, as well as their performance, over East Asia. Ground validation shows a remarkable improvement in the accuracy of V23 MISR aerosol optical depth (AOD) with ~80% of its retrieval bias within ±(0.05 + 20%AODAERONET). However, an underestimation of MISR AOD is still prevalent in the high‐AOD (>0.6) conditions, due to the surface‐atmosphere separation problem and insufficient absorbing aerosol mixtures being selected. MISR AOD of different size bins agrees well with AErosol RObotic NETwork (AERONET) results, demonstrating an evident advantage in discriminating natural dust from anthropogenic particles. Although MISR nonspherical and absorbing retrievals display a consistent variation with the AERONET inversions, their component AODs have a poor reliability over East Asia due to the inappropriate aerosol component models or their mixtures as in V22. In particular, the most striking problem is the sparse and discrete MISR absorbing retrievals with spatial discontinuity. Generally, the high‐resolution V23 MISR products exhibit a great potential in characterizing the regional variations of aerosol type, which can be further refined by considering the prior aerosol knowledge over East Asia.

Estimation of aerosol optical depth in relation to meteorological parameters over eastern and western routes of China Pakistan economic corridor

This study finds out seasonal and monthly variations in Aerosol Optical Depth (AOD) over eastern and western routes of China Pakistan Economic Corridor (CPEC) and the relationship between AOD and meteorological parameters (i.e., temperature, rainfall and wind speed). The Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR) data was used from the terra satellite for the period of 2000-2016. This study aims to overtake the conventional view of the purpose of using the satellite datasets. This study takes on to the concept that validated satellite data sets rather should be used for the analysis instead of just validation specifically for our study region. Hence, after comparing MODIS AOD with MISR AOD, only MISR AOD dataset is used for further analysis. The results show a decreasing trend of AOD in summer season, a positive relationship between temperature and AOD during winter and spring seasons whereas a positive relationship between wind speed and AOD in winter and spring seasons over eastern and western routes. Periodic analysis of MODIS AOD and MISR AOD depicts May-Aug as the peak period of aerosol concentration over central Pakistan. The inter-annual analysis shows the aerosol trend remained higher during summer season however rainfall shows the washout effect. Eastern route has higher standard deviation and larger values for aerosol prevalence as compared to western route. The trajectory analysis using the HYSPLIT model suggests the bias of air mass trajectory caused deviation in the aerosol trend in the year 2014.

Evaluation of the MISR fine resolution aerosol product using MODIS, MISR, and ground observations over China

Recently, NASA's Multiangle Imaging SpectroRadiometer (MISR) team released the Version 23 (V23) 4.4-km Aerosol Optical Depth (AOD) product, which has better spatial resolution than V22 at 17.6 km. However, its quality has not been validated in China. Here, V23 products for different spatiotemporal domains are obtained for validation against Aerosol Robotic NETwork (AERONET) AOD measurements for 29 sites from 2008 to 2017. Based on the national daily mean, V23 AOD yields a correlation coefficient (R) of 0.902 with AERONET; 59.45% of retrievals fall within the expected error (=EE; ±0.05 or ±0.2×AOD). A mean error (ME) of −0.0605 with 24.11% of retrievals falling below the EE indicates that MISR data are still underestimated at high AODs. The sample numbers and accuracies of spatially averaged 17.6-km and 50-km data are greatly improved relative to V22. The seasonal mean of V23 retrievals = EE in fall and winter are highest, followed by those in summer and spring; the validation results at 17.6 km are generally better than those at 50 km. By region, V23 retrievals = EE in the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) regions are 68.87%, 61.43%, and 73.13% respectively, while that of Taiwan is below 60% (55.75%). No V22 records exist in PRD, and V23 products over other regions in all seasons perform well; V22 retrievals in summer are also recommended. Compared with Terra/MODIS 3-km AOD in 2016, the V23 product has a slightly higher R value (0.925) with AERONET than MODIS (0.909). The MISR AOD bias is lower, and MODIS AOD is overestimated relative to the ground truth; both present consistent seasonality characteristics (spring > winter > summer > autumn), with the maximum in March and minimum in August. To investigate the spatiotemporal characteristics over long-term AOD, MISR V23 4.4-km AOD can be used in combination with other observation data.

Comparative analysis of MODIS, MISR, and AERONET climatology over the Middle East and North Africa

Abstract. Comparative analysis of Multi-angle Imaging SpectroRadiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) products is performed over seven AERONET stations located in the Middle East and North Africa for the period of 2000–2015. Sites are categorized into dust, biomass burning, and mixed aerosol conditions. MISR and MODIS AOD agree during high-dust seasons but MODIS tends to underestimate AOD during low-dust seasons. Over dust-dominated sites, MODIS/Terra AODs indicate a negative trend over time, while MODIS/Aqua, MISR, and AERONET depict a positive trend. A deviation between MODIS/Aqua and MODIS/Terra was observed regardless of the geographic location and data sampling. The performance of MODIS is similar over the entire region with ∼64 % of AOD within the Δτ=±0.05±0.15τAERO confidence range. MISR AOD retrievals fall within 84 % of the same confidence range for all sites examined here. Both MISR and MODIS capture aerosol climatology; however few cases were observed where one of the two sensors better captures the climatology over a certain location or AOD range than the other sensor. AERONET Level 2.0 version 3, MODIS Collection 6.1, and MISR V23 data have been used in analyzing the results presented in this study.

Evaluating MODIS and MISR aerosol optical depth retrievals over environmentally distinct sites in Pakistan

The Moderate resolution Imaging SpectroRadiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR) sensors provide aerosol observations suitable for a wide range of applications. The recently released MODIS Collection 6.1 (C061) includes several improvements compared to the previous products which are expected to minimize uncertainties in aerosol retrievals. Such collection needs to be validated and compared with previous collections form the same or other sensors before being applied for further scientific research on a regional scale. This study evaluates the performance of MODIS Collections 6 (C006) and 6.1 (C061) based on two algorithms: Dark Target (DT) and Deep Blue (DB) and the merged product (DTB) onboard the Terra (MOD04) and Aqua (MYD04) satellites, and the MISR AOD retrievals against ground-based Aerosol Robotic Network (AERONET) over two sites (Lahore and Karachi) in Pakistan during 2007–2014. Results elucidated that C061 MODIS AOD exhibited significant improvement as compared to C006, with the 10 km DT (DB) products generally overestimating (underestimating) AOD relative to AERONET AOD. The MOD04 and MYD04-DT and DB (3 and 10 km) showed comparable performance over the two sites, whereas The DTB was dominated by DT (DB) pixels over Lahore (Karachi). The MISR showed better performance over Karachi with high reflecting surface than over Lahore with dense vegetation cover. The annual cycle of AOD retrieved by the two sensors were consistent with AERONET AOD, with maximum AOD observed during summer months attributed to prevailing climatic conditions. On seasonal basis, the MODIS algorithms exhibited improved performance over Lahore except during summer where DT and DTB showed relatively low performance, attributed to modulations induced by local meteorology to the prevailing surface conditions. However, the sensors exhibited distinct performance over Karachi, where MODIS-DT (10 km) showed close correspondence with AERONET during autumn and winter, whereas MODIS-DT (3 km) exhibited the converse. The MISR performed relatively well during spring over the two stations. The study gives greater insights on the performance of MODIS and MISR and forms the basis for further research on the validation of satellite derived aerosol products over Pakistan.

Using Multi-Angle Imaging SpectroRadiometer Aerosol Mixture Properties for Air Quality Assessment in Mongolia

Ulaanbaatar (UB), the capital city of Mongolia, has extremely poor wintertime air quality with fine particulate matter concentrations frequently exceeding 500 μg/m3, over 20 times the daily maximum guideline set by the World Health Organization. Intensive use of sulfur-rich coal for heating and cooking coupled with an atmospheric inversion amplified by the mid-continental Siberian anticyclone drive these high levels of air pollution. Ground-based air quality monitoring in Mongolia is sparse, making use of satellite observations of aerosol optical depth (AOD) instrumental for characterizing air pollution in the region. We harnessed data from the Multi-angle Imaging SpectroRadiometer (MISR) Version 23 (V23) aerosol product, which provides total column AOD and component-particle optical properties for 74 different aerosol mixtures at 4.4 km spatial resolution globally. To test the performance of the V23 product over Mongolia, we compared values of MISR AOD with spatially and temporally matched AOD from the Dalanzadgad AERONET site and find good agreement (correlation r = 0.845, and root-mean-square deviation RMSD = 0.071). Over UB, exploratory principal component analysis indicates that the 74 MISR AOD mixture profiles consisted primarily of small, spherical, non-absorbing aerosols in the wintertime, and contributions from medium and large dust particles in the summertime. Comparing several machine learning methods for relating the 74 MISR mixtures to ground-level pollutants, including particulate matter with aerodynamic diameters smaller than 2.5 μm ( PM 2.5 ) and 10 μm ( PM 10 ), as well as sulfur dioxide ( SO 2 ), a proxy for sulfate particles, we find that Support Vector Machine regression consistently has the highest predictive performance with median test R 2 for PM 2.5 , PM 10 , and SO 2 equal to 0.461, 0.063, and 0.508, respectively. These results indicate that the high-dimensional MISR AOD mixture set can provide reliable predictions of air pollution and can distinguish dominant particle types in the UB region.

Improving MISR AOD Retrievals With Low-Light-Level Corrections for Veiling Light

Operational retrievals of aerosol optical depth (AOD) from Multi-angle Imaging SpectroRadiometer (MISR) data have been shown to have a high bias in pristine oceanic areas. One line of evidence involves comparison with Maritime Aerosol Network (MAN) observations, including the areas of low aerosol loading close to Antarctica. In this paper, a principal reason for the AOD overestimation is identified, which is stray light measured by the MISR cameras in dark regions of high-contrast scenes. A small fraction of the light from surrounding bright areas, such as clouds or sea ice, is redistributed to dark areas, artificially increasing their brightness. Internal reflections and light scattering from optical elements in MISR’s pushbroom cameras contribute to this veiling light effect. A simple correction model is developed that relies on the average scene brightness and an empirically determined set of veiling light coefficients for each MISR camera and wavelength. Several independent methods are employed to determine these coefficients. Three sets of coefficients are further implemented and tested in prototype MISR 4.4-km AOD retrievals. The results show dramatic improvements in retrieved AODs compared against MAN observations and the currently operational V22 MISR retrievals. For the best performing set of coefficients, the bias is reduced by 51%, from 0.039 to 0.019, the RMSE is lowered by 19%, from 0.062 to 0.050, and 84% of retrievals fall within the uncertainty envelope compared with 66% of retrievals in V22. The best performing set will be implemented operationally in the next V23 MISR AOD product release.

Development and assessment of a higher-spatial-resolution (4.4 km) MISR aerosol optical depth product using AERONET-DRAGON data

Abstract. Since early 2000, the Multi-angle Imaging SpectroRadiometer (MISR) instrument on NASA's Terra satellite has been acquiring data that have been used to produce aerosol optical depth (AOD) and particle property retrievals at 17.6 km spatial resolution. Capitalizing on the capabilities provided by multi-angle viewing, the current operational (Version 22) MISR algorithm performs well, with about 75 % of MISR AOD retrievals globally falling within 0.05 or 20 % × AOD of paired validation data from the ground-based Aerosol Robotic Network (AERONET). This paper describes the development and assessment of a prototype version of a higher-spatial-resolution 4.4 km MISR aerosol optical depth product compared against multiple AERONET Distributed Regional Aerosol Gridded Observations Network (DRAGON) deployments around the globe. In comparisons with AERONET-DRAGON AODs, the 4.4 km resolution retrievals show improved correlation (r = 0. 9595), smaller RMSE (0.0768), reduced bias (−0.0208), and a larger fraction within the expected error envelope (80.92 %) relative to the Version 22 MISR retrievals.

Satellite retrievals of dust aerosol over the Red Sea and the Persian Gulf (2005–2015)

Abstract. The inter-annual variability of the dust aerosol presence over the Red Sea and the Persian Gulf is analysed over the period 2005–2015. Particular attention is paid to the variation in loading across the Red Sea, which has previously been shown to have a strong, seasonally dependent latitudinal gradient. Over the 11 years considered, the July mean 630 nm aerosol optical depth (AOD) derived from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) varies between 0.48 and 1.45 in the southern half of the Red Sea. In the north, the equivalent variation is between 0.22 and 0.66. The temporal and spatial pattern of variability captured by SEVIRI is also seen in AOD retrievals from the MODerate Imaging Spectroradiometer (MODIS), but there is a systematic offset between the two records. Comparisons of both sets of retrievals with ship- and land-based AERONET measurements show a high degree of correlation with biases of < 0.08. However, these comparisons typically only sample relatively low aerosol loadings. When both records are stratified by AOD retrievals from the Multi-angle Imaging SpectroRadiometer (MISR), opposing behaviour is revealed at high MISR AODs ( > 1), with offsets of +0.19 for MODIS and −0.06 for SEVIRI. Similar behaviour is also seen over the Persian Gulf. Analysis of the scattering angles at which retrievals from the SEVIRI and MODIS measurements are typically performed in these regions suggests that assumptions concerning particle sphericity may be responsible for the differences seen.

Regional trends of aerosol optical depth and their impact on cloud properties over Southern India using MODIS data

Remote sensing of global aerosols has constituted a great scientific interest in a variety of applications related to global warming and climatic change. In the present study we investigate the spatial and temporal variations of aerosol optical properties and its impact on various properties of clouds over Southern India for the last ten years (2005–2014) by using Moderate Resolution Imaging Spectroradiometer (MODIS) data retrieved from the onboard Terra and Aqua satellites. The spatial distributions of annual mean lowest Aerosol Optical Depth (AOD) value is observed in Bangalore (BLR) (0.22±0.04) and the highest AOD value is noted in Visakhapatnam (VSK) (0.39±0.05). Similarly high Fine Mode Fraction (FMF) is noticed over VSK and Thiruvananthapuram (TVM), while lower values are observed in Anantapur (ATP), Hyderabad (HYD), Pune (PUNE) and BLR. From the results, a negative correlation was found between AOD and Cloud Top Temperature (CTT), Cloud Top Pressure (CTP) where as, a positive correlation was observed between AOD and Cloud Fraction (CF), Water Vapor (WV) over the selected regions. Monthly average AOD and FMF are plotted for analysis of the trends of aerosol loading in a long-term scale and both values showed statistically significant enhancing trend over all regions as derived from the MODIS measurements. Further, the annual variation of spatial correlation between MODIS and MISR (Multi - Angle Imaging Spectro Radiometer) AOD has been analyzed and the correlation coefficients are found to be higher in two of the regions VSK and PUNE (>0.8), and considerably lower for TVM (<0.7).

Recent global aerosol optical depth variations and trends — A comparative study using MODIS and MISR level 3 datasets

Abstract Aerosol distribution at global and regional scales is one of the crucial parameters for climate change assessment. The study presents global spatio-temporal variations in aerosol optical depth (AOD) retrieved by Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer (MISR) sensors at spatial resolutions of 1° × 1° and 0.5° × 0.5° respectively. Spatial correlations between MODIS and MISR AOD are reported on annual scale. The entire globe is divided into six sub-regions, i.e., North-West, North, North-East, South-West, South and South-East. The annual and seasonal mean AOD variations are obtained by analyzing Level 3 monthly datasets for the period 2001–2014. Relative changes (%) in AOD between the first and last seven years (i.e. 2001–2007 and 2008–2014 respectively) are discussed at annual and seasonal levels. AOD trends are calculated using the weighted least squares regression approach and weights are assigned to the AOD values corresponding to the expected errors. Statistically significant trends are reported at 95% confidence level. The results inferred from both the sensors are found to be in quite acceptable agreement. Increasing trends are found in the economically growing parts of Asian landmass and the surrounding oceanic regions, especially Arabian Sea and Bay of Bengal. On the other hand, decreasing trends are observed over parts of Europe, South America and North America. The study also focuses on examining the AOD trends over the ten most populous nations of the world. In terms of aerosol loadings, Bangladesh, Pakistan and Nigeria turned out to be the most polluted amongst the most populous countries. Significantly increasing trends in AOD are reported over the Indian subcontinent.

Aerosol climatology and discrimination of aerosol types retrieved from MODIS, MISR and OMI over Durban (29.88°S, 31.02°E), South Africa

The present study represents the characteristics of aerosol optical depth (AOD) retrieved from multiple satellite sensors (MODerate resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR), and Ozone Monitoring Instrument (OMI)) during 2003–2013 over an urban–coastal region, Durban (DBN; 29.88°S, 31.02°E, 46 m°asl), situated on the east coast of South Africa. An intercomparison and validation of AOD is performed against the AOD measurements from ground-based AErosol RObotic NETwork (AERONET) Sunphotometer. The results revealed that MISR-AERONET comparison indicated strong correlation compared to MODIS-AERONET comparison. Also, the comparison between MODIS and MISR AODs noticed significant positive correlation over DBN with the overestimation of latter by former. Highest AOD characterizes during the spring (September–November) followed by summer (December–February) and autumn (March–May) with the lowest AOD observed during the winter (June–August) season. The Angstrom exponent (AE470–600) indicates predominance of fine-mode aerosols during spring and summer and dominance of coarse-mode aerosols in winter. A HYbrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model is used to locate the origin of airmass transport and understand the variability of aerosol source regions. Finally, the relationship between AOD and AE has been examined to classify different aerosol types and showed seasonal heterogeneity in their contribution depending upon variability in sources. This is the first ever attempt to classify aerosols over this environment.

Estimating PM2.5 in Xi'an, China using aerosol optical depth: A comparison between the MODIS and MISR retrieval models

Satellite measurements have been widely used to estimate particulate matter (PM) on the ground, which can affect human health adversely. However, such estimation from space is susceptible to meteorological conditions and may result in large errors. In this study, we compared the aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) to predict ground-level PM2.5 concentration in Xi'an, Shaanxi province of northwestern China, using an empirical nonlinear model. Meteorological parameters from ground-based measurements and NCEP/NCAR reanalysis data were used as covariates in the model. Both MODIS and MISR AOD values were highly significant predictors of ground-level PM2.5 concentration. The MODIS and MISR models had overall comparable predictability of ground-level PM2.5 concentration and explained 67% and 72% of the daily PM2.5 concentration variation, respectively. Seasonal analysis showed that the MODIS and MISR models had overall comparable predictability of ground-level PM2.5 concentration, with the MISR model having a higher correlation coefficient (R) and thus giving a better fit in all seasons. The MISR model had high prediction accuracy in all seasons, with average R2 and absolute percentage error (APE) of 0.84 and 15.3% in all four seasons, respectively. The prediction of the MODIS model was best during winter (R2=0.83) with an APE of 19%, whereas it was relatively poor in spring (R2=0.56) with an APE of 21%. Further analysis showed that there was a significant improvement in correlation coefficient when using the nonlinear multiple regression model compared to using a simple linear regression model of AOD and PM2.5. These results are useful for assessing surface PM2.5 concentration and monitoring regional air quality.

Critical evaluation of cloud contamination in the MISR aerosol products using MODIS cloud mask products

Abstract. Using Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-based cloud screening methods, the impacts of cloud contamination on the Terra Multi-angle Imaging Spectroradiometer (MISR) aerosol optical depth (AOD) product are evaluated. Based on one year of collocated MISR and MODIS data, this study suggests that cloud contamination exists in both over-water and over-land MISR AOD data, with heavier cloud contamination occurring over the high latitude southern hemispheric oceans. On average globally, this study shows that thin cirrus cloud contamination introduces a possible ~ 0.01 high bias for the over-water MISR AOD retrievals. Over the mid- to high-latitude oceans and Southeast Asia, this number increases to 0.015–0.02. However, biases much larger than this mean value are found in individual retrievals, especially in retrievals that are near cloud edges. This study suggests that cloud-clearing methods using observations from MISR alone, which has only visible and near-infrared channels, may not be sufficient for all scenarios. Measurements from MODIS can be applied to assist cloud-clearing of the MISR aerosol retrievals. Cloud screening algorithms based on multi-sensor approaches are feasible and should be considered for current and future satellite aerosol studies.

Application of spectral analysis techniques in the intercomparison of aerosol data: Part III. Using combined PCA to compare spatiotemporal variability of MODIS, MISR, and OMI aerosol optical depth

AbstractSatellite measurements of global aerosol properties are very useful in constraining aerosol parameterization in climate models. The reliability of different data sets in representing global and regional aerosol variability becomes an essential question. In this study, we present the results of a comparison using combined principal component analysis (CPCA), applied to monthly mean, mapped (Level 3) aerosol optical depth (AOD) product from Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Ozone Monitoring Instrument (OMI). This technique effectively finds the common space‐time variability in the multiple data sets by decomposing the combined AOD field. The results suggest that all of the sensors capture the globally important aerosol regimes, including dust, biomass burning, pollution, and mixed aerosol types. Nonetheless, differences are also noted. Specifically, compared with MISR and OMI, MODIS variability is significantly higher over South America, India, and the Sahel. MODIS deep blue AOD has a lower seasonal variability in North Africa, accompanied by a decreasing trend that is not found in either MISR or OMI AOD data. The narrow swath of MISR results in an underestimation of dust variability over the Taklamakan Desert. The MISR AOD data also exhibit overall lower variability in South America and the Sahel. OMI does not capture the Russian wild fire in 2010 nor the phase shift in biomass burning over East South America compared to Central South America, likely due to cloud contamination and the OMI row anomaly. OMI also indicates a much stronger (boreal) winter peak in South Africa compared with MODIS and MISR.