Popular summary.Aerosols, tiny solid or liquid particles suspended in the atmosphere, were once only a side note in the Atmospheric Sciences. Today we realize the importance of aerosols in instigating or mitigating climate change, in modifling clouds and large-scale precipitation patterns and in affecting human health. Unlike greenhouse gases, which are well-mixed and long-lasting in the atmosphere, aerosols are temporally and spatially variable with lifetimes of a few days to a few weeks. Their transient natures make aerosols difficult to characterize and their effects on climate, hydrology and health difficult to model. Satellites provide the best means to observe the global aerosol system and narrow the uncertainties associated with aerosol characterization, but the satellite observations must be sufficiently accurate to be useful. The MODerate resolution Imaging Spectroradiometer (MODIS) aboard both NASA's Terra and Aqua satellites provides a unique tool to discern the global impact of aerosols. The products derived from MODIS data include aerosol optical thickness, which is a measure of aerosol amount, as well as products that describe the size of the aerosol particles. The MODIS aerosol retrievals are continuously evaluated against ground-truth of an existing global network of highly accurate instruments (AERONET). The results show an accuracy for the MODIS aerosol products that will sufficiently narrow the uncertainty of global aerosol characterization. Furthermore, the MODIS derivation of aerosol particle size aids in discriminating between man-made aerosol and naturally produced aerosols. This is a major step forward in narrowing the uncertainties associated with estimating the total anthropogenic effect on climate.

Abstract. The twin Moderate resolution Imaging Spectroradiometer (MODIS) sensors have been flying on Terra since 2000 and Aqua since 2002, creating an extensive data set of global Earth observations. Here, we introduce the Collection 6 (C6) algorithm to retrieve aerosol optical depth (AOD) and aerosol size parameters from MODIS-observed spectral reflectance. While not a major overhaul from the previous Collection 5 (C5) version, there are enough changes that there are significant impacts to the products and their interpretation. The C6 aerosol data set will be created from three separate retrieval algorithms that operate over different surface types. These are the two "Dark Target" (DT) algorithms for retrieving (1) over ocean (dark in visible and longer wavelengths) and (2) over vegetated/dark-soiled land (dark in the visible), plus the "Deep Blue" (DB) algorithm developed originally for retrieving (3) over desert/arid land (bright in the visible). Here, we focus on DT-ocean and DTland (#1 and #2). We have updated assumptions for central wavelengths, Rayleigh optical depths and gas (H 2 O, O 3 , CO 2 , etc.) absorption corrections, while relaxing the solar zenith angle limit (up to ≤ 84 • ) to increase poleward coverage. For DT-land, we have updated the cloud mask to allow heavy smoke retrievals, fine-tuned the assignments for aerosol type as function of season/location, corrected bugs in the Quality Assurance (QA) logic, and added diagnostic parameters such topographic altitude. For DT-ocean, improvements include a revised cloud mask for thin-cirrus detection, inclusion of wind speed dependence on the surface reflectance, updates to logic of QA Confidence flag (QAC) assignment, and additions of important diagnostic information.At the same time, we quantified how "upstream" changes to instrument calibration, land/sea masking and cloud masking will also impact the statistics of global AOD, and affect Terra and Aqua differently. For Aqua, all changes will result in reduced global AOD (by 0.02) over ocean and increased AOD (by 0.02) over land, along with changes in spatial coverage. We compared preliminary data to surface-based sun photometer data, and show that C6 should improve upon C5. C6 will include a merged DT/DB product over semi-arid land surfaces for reduced-gap coverage and better visualization, and new information about clouds in the aerosol field. Responding to the needs of the air quality community, in addition to the standard 10 km product, C6 will include a global (DT-land and DT-ocean) aerosol product at 3 km resolution.

[1] Since first light in early 2000, operational global quantitative retrievals of aerosol properties over land have been made from Moderate Resolution Imaging Spectroradiometer (MODIS) observed spectral reflectance. These products have been continuously evaluated and validated, and opportunities for improvements have been noted. We have replaced the surface reflectance assumptions, the set of aerosol model optical properties, and the aerosol lookup table (LUT). This second-generation operational algorithm performs a simultaneous inversion of two visible (0.47 and 0.66 mm) and one shortwave-IR (2.12 mm) channel, making use of the coarse aerosol information content contained in the 2.12 mm channel. Inversion of the three channels yields three nearly independent parameters, the aerosol optical depth (t) at 0.55 mm, the nondust or fine weighting (h), and the surface reflectance at 2.12 mm. Retrievals of small-magnitude negative t values (down to À0.05) are considered valid, thus balancing the statistics of t in near zero t conditions. Preliminary validation of this algorithm shows much improved retrievals of t, where the MODIS/Aerosol Robotic Network t (at 0.55 mm) regression has an equation of: y = 1.01x + 0.03, R = 0.90. Global mean t for the test bed is reduced from $0.28 to $0.21.

Abstract. NASA's MODIS sensors have been observing the Earth from polar orbit, from Terra since early 2000 and from Aqua since mid 2002. We have applied a consistent retrieval and processing algorithm to both sensors to derive the Collection 5 (C005) dark-target aerosol products over land. Here, we validate the MODIS along-orbit Level 2 products by comparing to quality assured Level 2 AERONET sunphotometer measurements at over 300 sites. From 85 463 collocations, representing mutually cloud-free conditions, we find that >66% (one standard deviation) of MODIS-retrieved aerosol optical depth (AOD) values compare to AERONETobserved values within an expected error (EE) envelope of ±(0.05 + 15%), with high correlation (R = 0.9). Thus, the MODIS AOD product is validated and quantitative. However, even though we can define EEs for MODIS-reported Angström exponent and fine AOD over land, these products do not have similar physical validity. Although validated globally, MODIS-retrieved AOD does not fall within the EE envelope everywhere. We characterize some of the residual biases that are related to specific aerosol conditions, observation geometry, and/or surface properties, and relate them to situations where particular MODIS algorithm assumptions are violated. Both Terra's and Aqua's-retrieved AOD are similarly comparable to AERONET, however, Terra's global AOD bias changes with time, overestimating (by ∼0.005) before 2004, and underestimating by similar magnitude after. This suggests how small calibration uncertainties of <2% can lead to spurious conclusions about long-term aerosol trends.

BackgroundEpidemiologic and health impact studies of fine particulate matter with diameter < 2.5 μm (PM2.5) are limited by the lack of monitoring data, especially in developing countries. Satellite observations offer valuable global information about PM2.5 concentrations.ObjectiveIn this study, we developed a technique for estimating surface PM2.5 concentrations from satellite observations.MethodsWe mapped global ground-level PM2.5 concentrations using total column aerosol optical depth (AOD) from the MODIS (Moderate Resolution Imaging Spectroradiometer) and MISR (Multiangle Imaging Spectroradiometer) satellite instruments and coincident aerosol vertical profiles from the GEOS-Chem global chemical transport model.ResultsWe determined that global estimates of long-term average (1 January 2001 to 31 December 2006) PM2.5 concentrations at approximately 10 km × 10 km resolution indicate a global population-weighted geometric mean PM2.5 concentration of 20 μg/m3. The World Health Organization Air Quality PM2.5 Interim Target-1 (35 μg/m3 annual average) is exceeded over central and eastern Asia for 38% and for 50% of the population, respectively. Annual mean PM2.5 concentrations exceed 80 μg/m3 over eastern China. Our evaluation of the satellite-derived estimate with ground-based in situ measurements indicates significant spatial agreement with North American measurements (r = 0.77; slope = 1.07; n = 1057) and with noncoincident measurements elsewhere (r = 0.83; slope = 0.86; n = 244). The 1 SD of uncertainty in the satellite-derived PM2.5 is 25%, which is inferred from the AOD retrieval and from aerosol vertical profile errors and sampling. The global population-weighted mean uncertainty is 6.7 μg/m3.ConclusionsSatellite-derived total-column AOD, when combined with a chemical transport model, provides estimates of global long-term average PM2.5 concentrations.

[1] The recently released Collection 5 Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products provide a consistent record of the Earth's aerosol system. Comparing with ground-based AERONET observations of aerosol optical depth (AOD) we find that Collection 5 MODIS aerosol products estimate AOD to within expected accuracy more than 60% of the time over ocean and more than 72% of the time over land. This is similar to previous results for ocean and better than the previous results for land. However, the new collection introduces a 0.015 offset between the Terra and Aqua global mean AOD over ocean, where none existed previously. Aqua conforms to previous values and expectations while Terra is higher than what had been expected. The cause of the offset is unknown, but changes to calibration are a possible explanation. Even though Terra's higher ocean AOD is unexpected and unexplained, we present climatological analyses of data from both sensors. We find that the multiannual global mean AOD at 550 nm over oceans is 0.13 for Aqua and 0.14 for Terra, and over land it is 0.19 in both Aqua and Terra. AOD in situations with 80% cloud fraction are twice the global mean values, although such situations occur only 2% of the time over ocean and less than 1% of the time over land. Aerosol particle size associated with these very cloudy situations does not show a drastic change over ocean, but does over land. Regionally, aerosol amounts vary from polluted areas such as east Asia and India, to the cleanest regions such as Australia and the northern continents. As AOD increases over maritime background conditions, fine mode aerosol dominates over dust over all oceans, except over the tropical Atlantic downwind of the Sahara and during some months over the Arabian Sea.

[1] As more information about global aerosol properties has become available from remotely sensed retrievals and in situ measurements, it is prudent to evaluate this new information, both on its own and in the context of satellite retrieval algorithms. Using the climatology of almucantur retrievals from global Aerosol Robotic Network (AERONET) Sun photometer sites, we perform cluster analysis to determine aerosol type as a function of location and season. We find that three spherical-derived types (describing fine-sized dominated aerosol) and one spheroid-derived types (describing coarse-sized dominated aerosol, presumably dust) generally describe the range of AERONET observed global aerosol properties. The fine-dominated types are separated mainly by their single scattering albedo (w 0 ), ranging from nonabsorbing aerosol (w 0 $ 0.95) in developed urban/industrial regions, to moderately absorbing aerosol (w 0 $ 0.90) in forest fire burning and developing industrial regions, to absorbing aerosol (w 0 $ 0.85) in regions of savanna/grassland burning. We identify the dominant aerosol type at each site, and extrapolate to create seasonal 1°Â 1°maps of expected aerosol types. Each aerosol type is bilognormal, with dynamic (function of optical depth) size parameters (radius, standard deviation, volume distribution) and complex refractive index. Not only are these parameters interesting in their own right, they can also be applied to aerosol retrieval algorithms, such as to aerosol retrieval over land from Moderate Resolution Imaging Spectroradiometer. Independent direct-Sun AERONET observations of spectral aerosol optical depth (t) are consistent the spectral dependence of the models, indicating that our derived aerosol models are relevant.

We estimated global fine particulate matter (PM2.5) concentrations using information from satellite-, simulation- and monitor-based sources by applying a Geographically Weighted Regression (GWR) to global geophysically based satellite-derived PM2.5 estimates. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations for 1998-2014. The GWR predictors included simulated aerosol composition and land use information. The resultant PM2.5 estimates were highly consistent (R(2) = 0.81) with out-of-sample cross-validated PM2.5 concentrations from monitors. The global population-weighted annual average PM2.5 concentrations were 3-fold higher than the 10 μg/m(3) WHO guideline, driven by exposures in Asian and African regions. Estimates in regions with high contributions from mineral dust were associated with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM2.5 data sources can yield valuable improvements to PM2.5 characterization on a global scale.