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The current study investigates the diurnal cycle of dust and dust mixture loading across the global tropics, subtropics, and mid-latitudes by analyzing aerosol extinction and typing profiles observed by the Cloud-Aerosol Transport System (CATS) lidar aboard the International Space Station. According to the comparison with ground-based and other satellite observations, CATS aerosol and dust and dust mixture loading observations exhibit reasonable quality but significant day–night inconsistency. To account for this day–night inconsistency in CATS data quality, the diurnal variability in dust and dust mixture characteristics is currently examined separately for daytime and nighttime periods. Based on an analysis of variance (ANOVA) analytical framework, pronounced diurnal variations in dust and dust mixture loading are generally uncovered during daytime periods and over terrestrial areas. The current study identifies statistically significant diurnal variability in dust and dust mixture loading over key dust sources, including the Bodélé Depression, the West African El Djouf, Rub' al-Khali desert, and western and southern North America, confirming the previous observation-based findings regarding the diurnal cycle of dust emission and underlying meteorological processes in these regions. Significant seasonal and diurnal variability in dust and dust mixture is identified over the Iraqi and Thar deserts. The identified significant diurnal cycles in dust mixture loading over the vegetated regions in the Amazon and tropical southern Africa are hypothesized to be driven by enhanced dust emission due to wildfires.

Primary productivity (PP) has been recently investigated using remote sensing-based models over quite limited geographical areas of the Red Sea. This work sheds light on how phytoplankton and primary production would react to the effects of global warming in the extreme environment of the Red Sea and, hence, illuminates how similar regions may behave in the context of climate variability. study focuses on using satellite observations to conduct an intercomparison of three net primary production (NPP) models—the vertically generalized production model (VGPM), the Eppley-VGPM, and the carbon-based production model (CbPM)—produced over the Red Sea domain for the 1998–2018 time period. A detailed investigation is conducted using multilinear regression analysis, multivariate visualization, and moving averages correlative analysis to uncover the models’ responses to various climate factors. Here, we use the models’ eight-day composite and monthly averages compared with satellite-based variables, including chlorophyll-a (Chla), mixed layer depth (MLD), and sea-surface temperature (SST). Seasonal anomalies of NPP are analyzed against different climate indices, namely, the North Pacific Gyre Oscillation (NPGO), the multivariate ENSO Index (MEI), the Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO), and the Dipole Mode Index (DMI). In our study, only the CbPM showed significant correlations with NPGO, MEI, and PDO, with disagreements relative to the other two NPP models. This can be attributed to the models’ connection to oceanographic and atmospheric parameters, as well as the trends in the southern Red Sea, thus calling for further validation efforts.

Exposure to outdoor fine particulate matter (PM2.5) is a leading risk factor for mortality. We develop global estimates of annual PM2.5 concentrations and trends for 1998-2018 using advances in satellite observations, chemical transport modeling, and ground-based monitoring. Aerosol optical depths (AODs) from advanced satellite products including finer resolution, increased global coverage, and improved long-term stability are combined and related to surface PM2.5 concentrations using geophysical relationships between surface PM2.5 and AOD simulated by the GEOS-Chem chemical transport model with updated algorithms. The resultant annual mean geophysical PM2.5 estimates are highly consistent with globally distributed ground monitors (R2 = 0.81; slope = 0.90). Geographically weighted regression is applied to the geophysical PM2.5 estimates to predict and account for the residual bias with PM2.5 monitors, yielding even higher cross validated agreement (R2 = 0.90-0.92; slope = 0.90-0.97) with ground monitors and improved agreement compared to all earlier global estimates. The consistent long-term satellite AOD and simulation enable trend assessment over a 21 year period, identifying significant trends for eastern North America (-0.28 ± 0.03 μg/m3/yr), Europe (-0.15 ± 0.03 μg/m3/yr), India (1.13 ± 0.15 μg/m3/yr), and globally (0.04 ± 0.02 μg/m3/yr). The positive trend (2.44 ± 0.44 μg/m3/yr) for India over 2005-2013 and the negative trend (-3.37 ± 0.38 μg/m3/yr) for China over 2011-2018 are remarkable, with implications for the health of billions of people.

Thermal infrared spectra can provide unique information on atmospheric ammonia enhancements, including those produced by agricultural, industrial, and biomass burning sources. In this work, we develop and test a quantitative ammonia retrieval algorithm using data from the airborne hyperspectral thermal emission spectrometer (HyTES) instrument over diverse targets, including persistent ammonia sources, such as cattle feedlots and a power plant, as well as a small, smoldering wildfire. Although no formal validation was possible due to lack of collocated observations, the HyTES ammonia values over feedlots in the California San Joaquin Valley are shown to be consistent within the estimated retrieval uncertainty of $\sim$ 20%–50% with in situ ammonia values collected over the same facilities during the 2013 DISCOVER-AQ field campaign. HyTES also observed meter-scale thermal hot spots associated with a small wildfire in Northern Arizona, which was determined to coincide with the emission of free ammonia, presumed to originate from the fire. This very localized emission source, not visible from satellite sensors, could be resolved in HyTES imagery. The ammonia enhancement immediately downwind from the smoldering fire, before the free ammonia reacted to form aerosols, was found to be distinguishable from the background with values three to ten times smaller than ammonia enhancements from the power plant and cattle feedlots, respectively. High-resolution detection and quantification of ammonia over nonpersistent sources like wildfires from HyTES shows that it is a powerful tool that can be used to improve regional scale fire emission inventories by accounting for the presence of temporally short ammonia processes.

Exposure to fine particulate matter (PM2.5) air pollution has been shown in numerous studies to be associated with detrimental health effects. However, the ability to conduct epidemiological assessments can be limited due to challenges in generating reliable PM2.5 estimates, particularly in parts of the world such as the Middle East where measurements are scarce and extreme meteorological events such as sandstorms are frequent. In order to supplement exposure modeling efforts under such conditions, satellite-retrieved aerosol optical depth (AOD) has proven to be useful due to its global coverage. By using AODs from the Multiangle Implementation of Atmospheric Correction (MAIAC) of the MODerate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) combined with meteorological and assimilated aerosol information from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), we constructed machine learning models to predict PM2.5 in the area surrounding the Persian Gulf, including Kuwait, Bahrain, and the United Arab Emirates (U.A.E). Our models showed regional differences in predictive performance, with better results in the U.A.E. (median test R2 = 0.66) than Kuwait (median test R2 = 0.51). Variable importance also differed by region, where satellite-retrieved AOD variables were more important for predicting PM2.5 in Kuwait than in the U.A.E. Divergent trends in the temporal and spatial autocorrelations of PM2.5 and AOD in the two regions offered possible explanations for differences in predictive performance and variable importance. In a test of model transferability, we found that models trained in one region and applied to another did not predict PM2.5 well, even if the transferred model had better performance. Overall the results of our study suggest that models developed over large geographic areas could generate PM2.5 estimates with greater uncertainty than could be obtained by taking a regional modeling approach. Furthermore, development of methods to better incorporate spatial and temporal autocorrelations in machine learning models warrants further examination.

Given that persons with a prior history of respiratory diseases tend to demonstrate more severe illness from COVID-19 and, hence, are at higher risk of serious symptoms, ambient air quality data from NASA's satellite observations might provide a critical insight into which geographical areas may exhibit higher numbers of hospitalizations due to COVID-19, how the expected severity of COVID-19 and associated survival rates may vary across space in the future, and most importantly how given this information, health professionals can distribute vaccines in a more efficient, timely, and fair manner. Despite the utmost urgency of this problem, there yet exists no systematic analysis on linkages among COVID-19 clinical severity, air quality, and other atmospheric conditions, beyond relatively simplistic regression-based models. The goal of this project is to glean a deeper insight into sophisticated spatio-temporal dependencies among air quality, atmospheric conditions, and COVID-19 clinical severity using the machinery of Geometric Deep Learning (GDL), while providing quantitative uncertainty estimates. Our results based on the GDL model on a county level in three US states, California, Pennsylvania and Texas, indicate that AOD attributes to COVID-19 clinical severity in 39, 30, and 132 counties out of 58, 67, and 254 total counties, respectively. In turn, relative humidity is another important factor for understanding dynamics of clinical course and mortality risks due COVID-19, but predictive utility of temperature is noticeably lower. Our findings do not only contribute to understanding of latent factors behind COVID-19 progression but open new perspectives for innovative use of NASA's datasets for biosurveillance and social good.

In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global coverage and granularity in order to test our models’ capabilities to represent structure at finer and broader scales, using many different kinds of instrumentation, that can be fused when applicable. In all cases tested, our models show a strong ability to segment the objects within input scenes, use multiple datasets fused together where appropriate to improve results, and, at times, outperform the pre-existing datasets. The success here will allow this methodology to be used within use concrete cases and become the basis for future dynamic object tracking across datasets from various remote sensing instruments.

The number of different modalities for remote sensors continues to grow, bringing with it an increase in the volume and complexity of the data being collected. Although these datasets individually provide valuable information, in aggregate they provide additional opportunities to discover meaningful patterns on a large scale. However, the ability to combine and analyze disparate datasets is challenged by the potentially vast parameter space that results from aggregation. Each dataset in itself requires instrument-specific and dataset-specific knowledge. If the intention is to use multiple, diverse datasets, one needs an understanding of how to translate and combine these parameters in an efficient and effective manner. While there are established techniques for combining datasets from specific domains or platforms, there is no generic, automated method that can address the problem in general. Here, we discuss the application of deep learning to track objects across different image-like data-modalities, given data in a similar spatio-temporal range, and automatically co-register these images. Using deep belief networks combined with unsupervised learning methods, we are able to recognize and separate different objects within image-like data in a structured manner, thus making progress toward the ultimate goal of a generic tracking and fusion pipeline requiring minimal human intervention.

We introduce and evaluate an approach for the simultaneous retrieval of aerosol and surface properties from Airborne Visible/Infrared Imaging Spectrometer Classic (AVIRIS-C) data collected during wildfires. The joint National Aeronautics and Space Administration (NASA) National Oceanic and Atmospheric Administration Fire Influence on Regional to Global Environments and Air Quality field campaign took place in August 2019, and involved two aircraft and coordinated ground-based observations. The AVIRIS-C instrument acquired data from onboard NASA's high altitude ER-2 research aircraft, coincident in space and time with aerosol observations obtained from the Aerosol Robotic Network (AERONET) DRAGON mobile platform in the smoke plume downwind of the Williams Flats Fire in northern Washington in August 2019. Observations in this smoke plume were used to assess the capacity of optimal-estimation based retrievals to simultaneously estimate aerosol optical depth (AOD) and surface reflectance from Visible Shortwave Infrared (VSWIR) imaging spectroscopy. Radiative transfer modeling of the sensitivities in spectral information collected over smoke reveal the potential capacity of high spectral resolution retrievals to distinguish between sulfate and smoke aerosol models, as well as sensitivity to the aerosol size distribution. Comparison with ground-based AERONET observations demonstrates that AVIRIS-C retrievals of AOD compare favorably with direct sun AOD measurements. Our analyses suggest that spectral information collected from the full VSWIR spectral interval, not just the shortest wavelengths, enables accurate retrievals. We use this approach to continuously map both aerosols and surface reflectance at high spatial resolution across heterogeneous terrain, even under relatively high AOD conditions associated with wildfire smoke.

Atmospheric aerosols are an important element of Earth’s climate system, and have significant impacts on the environment and on human health. Global aerosol modeling has been increasingly used for operational forecasting and as support to decision making. For example, aerosol analyses and forecasts are routinely used to provide air quality information and alerts in both civilian and military applications. The growing demand for operational aerosol forecasting calls for additional observational data that can be assimilated into models to improve model accuracy and predictive skill. These factors have motivated the development, testing, and release of a new near real-time (NRT) level 2 (L2) aerosol product from the Multi-angle Imaging SpectroRadiometer (MISR) instrument on NASA’s Terra platform. The NRT product capitalizes on the unique attributes of the MISR aerosol retrieval approach and product contents, such as reliable aerosol optical depth as well as aerosol microphysical information. Several modifications are described that allow for rapid product generation within a three-hour window following acquisition of the satellite observations. Implications for the product quality and consistency are discussed as compared to the current operational L2 MISR aerosol product. Several ways of implementing additional use-specific retrieval screenings are also highlighted.

Visible-light-absorbing carbonaceous aerosols within the boundary layer affect the radiance and polarization states of the radiation at the top of the atmosphere. Remote sensing from suborbital and satellite-based platforms utilizes these radiance and polarization signals to retrieve the key properties of these aerosols. Recent retrieval algorithms have shown a progressive trend toward including multi-angular and multi-spectral polarimetric measurements to produce better retrieval accuracy in comparison to those using measurements based on a single viewing angle. Here, we perform a theoretical investigation of the top of atmosphere (TOA) radiance-related reflectance factor (bidirectional reflectance factor (BRF)) and the two types of polarimetry-related factors (polarized bidirectional reflectance factor (pBRF) and the degree of linear polarization (DoLP)) for different types of atmospheric light-absorbing carbonaceous aerosols as a function of particle size distribution. We selected three polarimetric bands corresponding to those utilized by NASA Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) -- near-UV (470 nm), visible (660 nm), and near-infrared (865 nm) -- for our simulations which were performed over ocean surface using the successive order of scattering (SOS) algorithm coupled to a Lorenz-Mie aerosol optics model. The analysis of particle phase matrix elements indicates a close relationship between the angular dependencies of DoLP and associated phase matrix components at the shortest polarimetric band (470 nm). Using Jacobian analysis, we find that the radiance- and polarimetry-related reflectance factors of weakly light-absorbing aerosols, such as brown carbon, are more sensitive to changes in particle size and imaginary refractive index in comparison with those of black carbon, which is strongly light-absorbing., Comment: Includes main text and supplementary material

The current study investigates the diurnal cycle of dust loading across the global tropics, sub-tropics, and mid-latitudes by analyzing aerosol extinction and typing profiles observed by the Cloud–Aerosol Transport System (CATS) lidar aboard the International Space Station. According to the comparison with ground-based and other satellite observations, CATS aerosol and dust loading observations exhibits reasonable quality but significant day–night inconsistency. To account for this day–night inconsistency in CATS data quality, the diurnal variability in dust characteristics are currently examined separately for daytime and nighttime periods. Based on an analysis of variance analytical framework, pronounced diurnal variations in dust loading are generally uncovered during daytime periods and over terrestrial areas. The current study identifies statistically significant diurnal variability in dust loading over key dust sources, including the Bodélé Depression, the West African El Djouf, Rub-al Khali Desert, and western and southern North America, confirming the previous observation-based findings regarding the diurnal cycle of dust emission and underlying meteorological processes in these regions. Significant seasonal dust diurnal variability is identified over the Iraqi and Thar deserts. The identified significant diurnal cycles in dust loading over the rainforests in Amazon and tropical southern Africa are hypothesized to be driven by enhanced dust emission due to wildfires.

Many air pollution health effects studies rely on exposure estimates of particulate matter (PM) concentrations derived from remote sensing observations of aerosol optical depth (AOD). Simple but robust calibration models between AOD and PM are therefore important for generating reliable PM exposures. We conduct an in-depth examination of the spatial and temporal characteristics of the AOD-PM2.5 relationship by leveraging data from the Distributed Regional Aerosol Gridded Observation Networks (DRAGON) field campaign where eight NASA Aerosol Robotic Network (AERONET) sites were co-located with EPA Air Quality System (AQS) monitoring sites in California’s Central Valley from November 2012 to April 2013. With this spatiotemporally rich data we found that linear calibration models (R2 = 0.35, RMSE = 10.38 μg/m3) were significantly improved when spatial (R2 = 0.45, RMSE = 9.54 μg/m3), temporal (R2 = 0.62, RMSE = 8.30 μg/m3), and spatiotemporal (R2 = 0.65, RMSE = 7.58 μg/m3) functions were included. As a use-case we applied the best spatiotemporal model to convert space-borne MultiAngle Imaging Spectroradiometer (MISR) AOD observations to predict PM2.5 over the region (R2 = 0.60, RMSE = 8.42 μg/m3). Our results imply that simple AERONET AOD-PM2.5 calibrations are robust and can be reliably applied to space-borne AOD observations, resulting in PM2.5 prediction surfaces for use in downstream applications.

Satellite instruments provide a vantage point for studying aerosol loading consistently over different regions of the world. However, the typical lifetime of a single satellite platform is on the order of 5–15 years; thus, for climate studies, the use of multiple satellite sensors should be considered. Discrepancies exist between aerosol optical depth (AOD) products due to differences in their information content, spatial and temporal sampling, calibration, cloud masking, and algorithmic assumptions. Users of satellite-based AOD time-series are confronted with the challenge of choosing an appropriate dataset for the intended application. In this study, 16 monthly AOD products obtained from different satellite sensors and with different algorithms were inter-compared and evaluated against Aerosol Robotic Network (AERONET) monthly AOD. Global and regional analyses indicate that products tend to agree qualitatively on the annual, seasonal and monthly timescales but may be offset in magnitude. Several approaches were then investigated to merge the AOD records from different satellites and create an optimised AOD dataset. With few exceptions, all merging approaches lead to similar results, indicating the robustness and stability of the merged AOD products. We introduce a gridded monthly AOD merged product for the period 1995–2017. We show that the quality of the merged product is as least as good as that of individual products. Optimal agreement of the AOD merged product with AERONET further demonstrates the advantage of merging multiple products. This merged dataset provides a long-term perspective on AOD changes over different regions of the world, and users are encouraged to use this dataset.

Detailed characterization of the aerosol content of wildfire smoke plumes is typically performed through in situ aircraft observations, which have limited temporal and spatial coverage. Extending such observations to regional or global scales requires new remote sensing approaches, such as retrievals that make use of spectropolarimetric, multiangle imaging. In this work measurements made during the Imaging Polarimetric Assessment and Characterization of Tropospheric Particulate Matter (ImPACT‐PM) field campaign in a smoke plume near the town of Lebec in Southern California by the Navy Center for Interdisciplinary Remotely Piloted Aircraft Studies Twin Otter aircraft on 8 July 2016 are used in conjunction with near‐coincident measurements from the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) on the National Aeronautics and Space Administration ER‐2 high‐altitude research aircraft to assess the sensitivity of spectropolarimetric measurements to the black carbon content of the plume. Tracking visible features in the smoke through the sequence of AirMSPI observations allowed the height of the plume to be estimated through geometric techniques. Then, by constraining the fractional amounts of the aerosol constituents with the in situ data, radiative closure was obtained through simulations performed with a polarimetric radiative transfer code, demonstrating the ability to constrain the black carbon mass fraction to approximately 5%, given the uncertainties in the AirMSPI measurements and the assumption of external mixing of aerosol components. The AirMSPI retrieval, made using a limited set of observations from the 470 nm polarimetric spectral band alone, was also generally consistent with operational retrievals of aerosol optical depth and surface reflectance made by the Multi‐Angle Implementation of Atmospheric Correction algorithm at 1 km resolution.

This study focuses on the role that African dust over the Atlantic had on the persistence of the tropical system that eventually became Hurricane Sandy in October 2012. On October 8, a Saharan dust event in the Mauritania region of West Africa transported significant amounts of mineral dust into the troposphere and along the path of an easterly wave created by a break in the Intertropical Convergence Zone (ITCZ). The Terra/Aqua-MODIS satellite observations clearly define the spatial distribution of the coarse/fine aerosols, while the CALIPSO observations of the total attenuated backscatter at 532 nm provide a detailed view of the vertical structure and aerosol types in the dust-laden layer. European Centre for Medium-Range Weather Forecasts and Modern-Era Retrospective Analysis for Research and Applications, Version 2 reanalysis data show the distribution of aerosols along the path of the pre-Sandy wave as well as a second wave that formed north of the ITCZ under different condition. The second wave, which started in an area of relatively larger aerosol optical depth (AOD), moved into an area with abnormally low convective available potential energy and AOD, subsequently dying out, while the wave that became Sandy had light aerosol loading (AOD between 0.15–0.5) along a majority of its path. The evidence suggests that aerosols played a nontrivial role in the maintenance of this system until it moved into an environment favorable for cyclogenesis.

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.

Research efforts to better characterize the differential toxicity of PM2.5 (particles with aerodynamic diameters less than or equal to 2.5 μm) speciation are often hindered by the sparse or non-existent coverage of ground monitors. The Multi-angle Imaging SpectroRadiometer (MISR) aboard NASA’s Terra satellite is one of few satellite aerosol sensors providing information of aerosol shape, size and extinction globally for a long and continuous period that can be used to estimate PM2.5 speciation concentrations since year 2000. Currently, MISR only provides a 17.6 km product for its entire mission with global coverage every 9 days, a bit too coarse for air pollution health effects research and to capture local spatial variability of PM2.5 speciation. In this study, generalized additive models (GAMs) were developed using MISR prototype 4.4 km-resolution aerosol data with meteorological variables and geographical indicators, to predict ground-level concentrations of PM2.5 sulfate, nitrate, organic carbon (OC) and elemental carbon (EC) in Southern California between 2001 and 2015 at the daily level. The GAMs are able to explain 66%, 62%, 55% and 58% of the daily variability in PM2.5 sulfate, nitrate, OC and EC concentrations during the whole study period, respectively. Predicted concentrations capture large regional patterns as well as fine gradients of the four PM2.5 species in urban areas of Los Angeles and other counties, as well as in the Central Valley. This study is the first attempt to use MISR prototype 4.4 km-resolution AOD (aerosol optical depth) components data to predict PM2.5 sulfate, nitrate, OC and EC concentrations at the sub-regional scale. In spite of its low temporal sampling frequency, our analysis suggests that the MISR 4.4 km fractional AODs provide a promising way to capture the spatial hotspots and long-term temporal trends of PM2.5 speciation, understand the effectiveness of air quality controls, and allow our estimated PM2.5 speciation data to be linked with common spatial units such as census tract or zip code in epidemiological studies. This modeling strategy needs to be validated in other regions when more MISR 4.4 km data becoming available in the future., GRAPHICAL ABSTRACT

A new method for retrieving aerosol optical depth (AOD) and its uncertainty from Multi-angle Imaging SpectroRadiometer (MISR) observations over dark water is outlined. MISR's aerosol retrieval algorithm calculates cost functions between observed and pre-simulated radiances for a range of AODs (from 0.0 to 3.0) and a prescribed set of aerosol mixtures. The previous version 22 (V22) operational algorithm considered only the AOD that minimized the cost function for each aerosol mixture and then used a combination of these values to compute the final, “best estimate” AOD and associated uncertainty. The new approach considers the entire range of cost functions associated with each aerosol mixture. The uncertainty of the reported AOD depends on a combination of (a) the absolute values of the cost functions for each aerosol mixture, (b) the widths of the cost function distributions as a function of AOD, and (c) the spread of the cost function distributions among the ensemble of mixtures. A key benefit of the new approach is that, unlike the V22 algorithm, it does not rely on empirical thresholds imposed on the cost function to determine the success or failure of a particular mixture. Furthermore, a new aerosol retrieval confidence index (ARCI) is established that can be used to screen high-AOD retrieval blunders caused by cloud contamination or other factors. Requiring ARCI ≥0.15 as a condition for retrieval success is supported through statistical analysis and outperforms the thresholds used in the V22 algorithm. The described changes to the MISR dark water algorithm will become operational in the new MISR aerosol product (V23), planned for release in 2017.

The present study summarizes two decades (2000–2019) of climatology and trends in aerosol loading and optical properties using a high spatial resolution data obtained from NASA's MODIS MAIAC and MISR aerosol products supplemented by moderate resolution aerosol data from OMI sensor over South Asia (SA). MISR AOD showed good agreement against AERONET AOD with 68.68% of the retrievals falling within the expected error and high Pearson's correlation coefficient (R = 0.83). The 20 years geometric mean of MAIAC and MISR AOD revealed higher loading of aerosols over the Indo-Gangetic Plain (IGP) and Eastern coast of India by 30% to 44% compared to the mean AOD over the entire SA. The highest mean AOD under cloud-free conditions was noted during monsoon season, followed by pre-monsoon, post-monsoon, and winter. The high contribution of coarse-mode AOD (cAOD) mainly from natural aerosol emission and small-mode AOD (sAOD) from local anthropogenic emissions are the main driver to high AOD in monsoon and pre-monsoon seasons. Besides, the presence of high humidity during the monsoon season favors the hygroscopic growth of the particles and leads to higher AOD values over SA. The high spatial resolutions of MODIS/MAIAC and MISR aerosol products enabled the identification of previously unobserved aerosol hotspots over Bihar, West Bengal, and the eastern Indian coastal state of Odisha, which is mainly dominated by small aerosol particles. The contributions of smaller aerosol particles to the total aerosol loading were found to be higher during post-monsoon and winter over most states in India, Nepal, and Bangladesh. In contrast, the contribution of coarser particles was higher over Pakistan during pre-monsoon and monsoon seasons. Smaller particles were predominantly retrieved over the Indian states dominated by mining industries, including Jharkhand and Odisha. A typical dominance of absorbing carbonaceous aerosols was also noted over the northwestern region of IGP during post-monsoon, which otherwise was mainly affected by mixed dust aerosols and carbonaceous aerosols in pre-monsoon and monsoon seasons. A statistically significant positive temporal trend in AOD was observed for the whole study period, over most of the SA region, which was influenced by the increase in small particles over India and Bangladesh. Urban/industrial weakly absorbing aerosols were found to be the main contributor to a similarly positive trend over Central India and East coast Indian states. Overall, recent advancements in high spatial resolution satellite-based aerosol optical properties showed good potential to identify the aerosol hotspots and constrain aerosol types across a highly polluted SA region.