Synthesis of satellite (MODIS), aircraft (ICARTT), and surface (IMPROVE, EPA-AQS, AERONET) aerosol observations over eastern North America to improve MODIS aerosol retrievals and constrain surface aerosol concentrations and sources

We use an ensemble of satellite (MODIS), aircraft, and ground-based aerosol observations during the ICARTT field campaign over eastern North America in summer 2004 to (1) examine the consistency between different aerosol measurements, (2) evaluate a new retrieval of aerosol optical depths (AODs) and inferred surface aerosol concentrations (PM2.5) from the MODIS satellite instrument, and (3) apply this collective information to improve our understanding of aerosol sources. The GEOS-Chem global chemical transport model (CTM) provides a transfer platform between the different data sets, allowing us to evaluate the consistency between different aerosol parameters observed at different times and locations. We use an improved MODIS AOD retrieval based on locally derived visible surface reflectances and aerosol properties calculated from GEOS-Chem. Use of GEOS-Chem aerosol optical properties in the MODIS retrieval not only results in an improved AOD product but also allows quantitative evaluation of model aerosol mass from the comparison of simulated and observed AODs. The aircraft measurements show narrower aerosol size distributions than those usually assumed in models, and this has important implications for AOD retrievals. Our MODIS AOD retrieval compares well to the ground-based AERONET data (R = 0.84, slope = 1.02), significantly improving on the MODIS c005 operational product. Inference of surface PM2.5 from our MODIS AOD retrieval shows good correlation to the EPA-AQS data (R = 0.78) but a high regression slope (slope = 1.48). The high slope is seen in all AOD-inferred PM2.5 concentrations (AERONET: slope = 2.04; MODIS c005: slope = 1.51) and could reflect a clear-sky bias in the AOD observations. The ensemble of MODIS, aircraft, and surface data are consistent in pointing to a model overestimate of sulfate in the mid-Atlantic and an underestimate of organic and dust aerosol in the southeastern United States. The sulfate overestimate could reflect an excessive contribution from aqueous-phase production in clouds, while the organic carbon underestimate could possibly be resolved by a new secondary pathway involving dicarbonyls

Applying the Dark Target Aerosol Algorithm with Advanced Himawari Imager Observations During the KORUS-AQ Field Campaign

For nearly 2 decades we have been quantitatively observing the Earth's aerosol system from space at one or two times of the day by applying the Dark Target family of algorithms to polar-orbiting satellite sensors, particularly MODIS and VIIRS. With the launch of the Advanced Himawari Imager (AHI) and the Advanced Baseline Imagers (ABIs) into geosynchronous orbits, we have the new ability to expand temporal coverage of the traditional aerosol optical depth (AOD) to resolve the diurnal signature of aerosol loading during daylight hours. The KoreanUnited States Air Quality (KORUS-AQ) campaign taking place in and around the Korean peninsula during MayJune 2016 initiated a special processing of full-disk AHI observations that allowed us to make a preliminary adoption of Dark Target aerosol algorithms to the wavelengths and resolutions of AHI. Here,we describe the adaptation and show retrieval results from AHI for this 2-month period. The AHI-retrieved AOD is collocated in time and space with existing AErosol RObotic NETwork stations across Asia and with collocated Terra and Aqua MODIS retrievals. The new AHI AOD product matches AERONET, and the standard MODIS product does as well, and the agreement between AHI and MODIS retrieved AOD is excellent, as can be expected by maintaining consistency in algorithm architecture and most algorithm assumptions. Furthermore, we show that the new product approximates the AERONET-observed diurnal signature. Examining the diurnal patterns of the new AHI AOD product we find specific areas over land where the diurnal signal is spatially cohesive. For example, in Bangladesh the AOD in-creases by 0.50 from morning to evening, and in northeast China the AOD decreases by 0.25. However, over open ocean the observed diurnal cycle is driven by two artifacts, one associated with solar zenith angles greater than 70t hat may be caused by a radiative transfer model that does not properly represent the spherical Earth and the other artifact associated with the fringes of the 40 degree glint angle mask. This opportunity during KORUS-AQ provides encouragement to move towards an operational Dark Target algorithm for AHI. Future work will need to re-examine masking including snow mask, reevaluate assumed aerosol models for geosynchronous geometry, address the artifacts over the ocean, and investigate size parameter retrieval from the over-ocean algorithm

Estimating Marine Aerosol Particle Volume and Number from Maritime Aerosol Network Data

As well as spectral aerosol optical depth (AOD), aerosol composition and concentration (number, volume, or mass) are of interest for a variety of applications. However, remote sensing of these quantities is more difficult than for AOD, as it is more sensitive to assumptions relating to aerosol composition. This study uses spectral AOD measured on Maritime Aerosol Network (MAN) cruises, with the additional constraint of a microphysical model for unpolluted maritime aerosol based on analysis of Aerosol Robotic Network (AERONET) inversions, to estimate these quantities over open ocean. When the MAN data are subset to those likely to be comprised of maritime aerosol, number and volume concentrations obtained are physically reasonable. Attempts to estimate surface concentration from columnar abundance, however, are shown to be limited by uncertainties in vertical distribution. Columnar AOD at 550 nm and aerosol number for unpolluted maritime cases are also compared with Moderate Resolution Imaging Spectroradiometer (MODIS) data, for both the present Collection 5.1 and forthcoming Collection 6. MODIS provides a best-fitting retrieval solution, as well as the average for several different solutions, with different aerosol microphysical models. The average solution MODIS dataset agrees more closely with MAN than the best solution dataset. Terra tends to retrieve lower aerosol number than MAN, and Aqua higher, linked with differences in the aerosol models commonly chosen. Collection 6 AOD is likely to agree more closely with MAN over open ocean than Collection 5.1. In situations where spectral AOD is measured accurately, and aerosol microphysical properties are reasonably well-constrained, estimates of aerosol number and volume using MAN or similar data would provide for a greater variety of potential comparisons with aerosol properties derived from satellite or chemistry transport model data

Continental-scale surface reflectance product from CBERS-4 MUX data: Assessment of atmospheric correction method using coincident Landsat observations

A practical atmospheric correction algorithm, called Coupled Moderate Products for Atmospheric Correction (CMPAC), was developed and implemented for the Multispectral Camera (MUX) on-board the China-Brazil Earth Resources Satellite (CBERS-4). This algorithm uses a scene-based processing and sliding window technique to derive MUX surface reflectance (SR) at continental scale. Unlike other optical sensors, MUX instrument imposes constraints for atmospheric correction due to the absence of spectral bands for aerosol estimation from imagery itself. To overcome this limitation, the proposed algorithm performs a further processing of atmospheric products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors as input parameters for radiative transfer calculations. The success of CMPAC algorithm was fully assessed and confirmed by comparison of MUX SR data with the Landsat-8 OLI Level-2 and Aerosol Robotic Network (AERONET)-derived SR products. The spectral adjustment was performed to compensate for the differences of relative spectral response between MUX and OLI sensors. The results show that MUX SR values are fairly similar to operational Landsat-8 SR products (mean difference \u3c 0.0062, expressed in reflectance). There is a slight underestimation of MUX SR compared to OLI product (except the NIR band), but the error metrics are typically low and scattered points are around the line 1:1. These results suggest the potential of combining these datasets (MUX and OLI) for quantitative studies. Further, the robust agreement of MUX and AERONET-derived SR values emphasizes the quality of moderate atmospheric products as input parameters in this application, with root-mean-square deviation lower than 0.0047. These findings confirm that (i) CMPAC is a suitable tool for estimating surface reflectance of CBERS MUX data, and (ii) ancillary products support the application of atmospheric correction by filling the gap of atmospheric information. The uncertainties of atmospheric products, negligence of the bidirectional effects, and two aerosol models were also identified as a limitation. Finally, this study presents a framework basis for atmospheric correction of CBERS-4 MUX images. The utility of CBERS data comes from its use, and this new product enables the quantitative remote sensing for land monitoring and environmental assessment at 20 m spatial resolution

Overview of the Summer 2004 Intercontinental Chemical Transport Experiment–North America (INTEX-A)

The INTEX-A field mission was conducted in the summer of 2004 (1 July to 15 August 2004) over North America (NA) and the Atlantic in cooperation with multiple national and international partners as part of a consortium called ICARTT. The main goals of INTEX-A were to (1) characterize the composition of the troposphere over NA, (2) characterize the outflow of pollution from NA and determine its chemical evolution during transatlantic transport, (3) validate satellite observations of tropospheric composition, (4) quantitatively relate atmospheric concentrations of gases and aerosols with their sources and sinks, and (5) investigate aerosol properties and their radiative effects. INTEX-A primarily relied on instrumented DC-8 and J-31 aircraft platforms to achieve its objectives. The DC-8 was equipped to measure detailed gas and aerosol composition and provided sufficient range and altitude capability to coordinate activities with distant partners and to sample the entire midlatitude troposphere. The J-31 was specifically focused on radiative effects of clouds and aerosols and operated largely in the Gulf of Maine. Satellite products along with meteorological and 3-D chemical transport model forecasts were integrated into the flight planning process. Intercomparisons were performed to quantify the accuracy of data and to create a unified data set. Satellite validation activities principally focused on Terra (MOPITT, MODIS, and MISR), Aqua (AIRS and MODIS) and Envisat (SCIAMACHY) to validate observations of CO, NO2, HCHO, H2O, and aerosol. Persistent fires in Alaska and NW Canada offered opportunities to quantify emissions from fires and study the transport and evolution of biomass burning plumes. Contrary to expectations, several pollution plumes of Asian origin, frequently mixed with stratospheric air, were sampled over NA. Quasi-Lagrangian sampling was successfully carried out to study chemical aging of plumes during transport over the Atlantic. Lightning NOx source was found to be far larger than anticipated and provided a major source of error in model simulations. The composition of the upper troposphere was significantly perturbed by influences from surface pollution and lightning. Drawdown of CO2 was characterized over NA and its atmospheric abundance related to terrestrial sources and sinks. INTEX-A observations provide a comprehensive data set to test models and evaluate major pathways of pollution transport over NA and the Atlantic. This overview provides a context within which the present and future INTEX-A/ICARTT publications can be understood.Earth and Planetary SciencesEngineering and Applied Science

Development and Implementation of a Remote- Sensing and In-situ Data Assimilating Version of CMAQ for Operational PM\u3csub\u3e2.5\u3c/sub\u3e Forecasting Part 1: MODIS Aerosol Optical Depth (AOD) Data- Assimilation Design and Testing

Air quality forecasts are now routinely used to understand when air pollution may reach unhealthy levels. For the first time, an operational air quality forecast model that includes the assimilation of remotely-sensed aerosol optical depth and ground based PM2.5 observations is being used. The assimilation enables quantifiable improvements in model forecast skill, which improves confidence in the accuracy of the officially-issued forecasts. This helps air quality stakeholders be more effective in taking mitigating actions (reducing power consumption, ridesharing, etc.) and avoiding exposures that could otherwise result in more serious air quality episodes or more deleterious health effects

Development and Implementation of a Remote- Sensing and In-situ Data Assimilating Version of CMAQ for Operational PM\u3csub\u3e2.5\u3c/sub\u3e Forecasting Part 1: MODIS Aerosol Optical Depth (AOD) Data- Assimilation Design and Testing

Air quality forecasts are now routinely used to understand when air pollution may reach unhealthy levels. For the first time, an operational air quality forecast model that includes the assimilation of remotely-sensed aerosol optical depth and ground based PM2.5 observations is being used. The assimilation enables quantifiable improvements in model forecast skill, which improves confidence in the accuracy of the officially-issued forecasts. This helps air quality stakeholders be more effective in taking mitigating actions (reducing power consumption, ridesharing, etc.) and avoiding exposures that could otherwise result in more serious air quality episodes or more deleterious health effects

A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

Cloud and Aerosol Imager (CAI) onboard the Greenhouse Gases Observing Satellite (GOSAT) is a multi-band sensor designed to observe and acquire information on clouds and aerosols. In order to retrieve aerosol optical depth (AOD) over land from the CAI sensor, a Dark Target (DT) algorithm for GOSAT CAI was developed based on the strategy of the Moderate Resolution Imaging Spectroradiometer (MODIS) DT algorithm. When retrieving AOD from satellite platforms, determining surface contributions is a major challenge. In the MODIS DT algorithm, surface signals in the visible wavelengths are estimated based on the relationships between visible channels and shortwave infrared (SWIR) near the 2.1 µm channel. However, the CAI only has a 1.6 µm band to cover the SWIR wavelengths. To resolve the difficulties in determining surface reflectance caused by the lack of 2.1 μm band data, we attempted to analyze the relationship between reflectance at 1.6 µm and at 2.1 µm. We did this using the MODIS surface reflectance product and then connecting the reflectances at 1.6 µm and the visible bands based on the empirical relationship between reflectances at 2.1 µm and the visible bands. We found that the reflectance relationship between 1.6 µm and 2.1 µm is typically dependent on the vegetation conditions, and that reflectances at 2.1 µm can be parameterized as a function of 1.6 µm reflectance and the Vegetation Index (VI). Based on our experimental results, an Aerosol Free Vegetation Index (AFRI2.1)-based regression function connecting the 1.6 µm and 2.1 µm bands was summarized. Under light aerosol loading (AOD at 0.55 µm < 0.1), the 2.1 µm reflectance derived by our method has an extremely high correlation with the true 2.1 µm reflectance (r-value = 0.928). Similar to the MODIS DT algorithms (Collection 5 and Collection 6), a CAI-applicable approach that uses AFRI2.1 and the scattering angle to account for the visible surface signals was proposed. It was then applied to the CAI sensor for AOD retrieval; the retrievals were validated by comparisons with ground-level measurements from Aerosol Robotic Network (AERONET) sites. Validations show that retrievals from the CAI have high agreement with the AERONET measurements, with an r-value of 0.922, and 69.2% of the AOD retrieved data falling within the expected error envelope of ± (0.1 + 15% AODAERONET)

The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) updates NASA's previous satellite era (1980 - onward) reanalysis system to include additional observations and improvements to the Goddard Earth Observing System, Version 5 (GEOS-5) Earth system model. As a major step towards a full Integrated Earth Systems Analysis (IESA), in addition to meteorological observations, MERRA-2 now includes assimilation of aerosol optical depth (AOD) from various ground- and space-based remote sensing platforms. Here, in the first of a pair of studies, we document the MERRA-2 aerosol assimilation, including a description of the prognostic model (GEOS-5 coupled to the GOCART aerosol module), aerosol emissions, and the quality control of ingested observations. We provide initial validation and evaluation of the analyzed AOD fields using independent observations from ground, aircraft, and shipborne instruments. We demonstrate the positive impact of the AOD assimilation on simulated aerosols by comparing MERRA-2 aerosol fields to an identical control simulation that does not include AOD assimilation. Having shown the AOD evaluation, we take a first look at aerosol-climate interactions by examining the shortwave, clear-sky aerosol direct radiative effect. In our companion paper, we evaluate and validate available MERRA-2 aerosol properties not directly impacted by the AOD assimilation (e.g. aerosol vertical distribution and absorption). Importantly, while highlighting the skill of the MERRA-2 aerosol assimilation products, both studies point out caveats that must be considered when using this new reanalysis product for future studies of aerosols and their interactions with weather and climate

Comparison of aerosol products retrieved from AERONET, MICROTOPS and MODIS over a tropical urban city, Pune, India

Aerosol Optical Depth (AOD) measurements from Aerosol Robotic NETwork (AERONET; level 2.0), Microtops - II sun-photometer and MODerate Resolution Imaging Spectroradiometer (MODIS) (Terra and Aqua; level 2, collection 5, dark target) were compared and used to characterize aerosols over Pune, India. AODs from Microtops and MODIS were compared with those measured by AERONET to evaluate the measurement quality. To the best of our knowledge, this is the first systematic comparison of MODIS aerosol products over Pune, India. The results of the analysis show that during 2008-10, 68 to 84 of the MODIS AODs fell within an expected error, as defined by the MODIS science team, and thus the retrievals from this system are validated and accepted. In addition, during pre-monsoon periods MODIS retrievals are better-matched with ground-based measurements. On the seasonal scale, MODIS retrievals corroborate well with ground-based measurements, with correlation coefficients ranging from 0.62 to 0.93. Despite an overall satellite-ground agreement, MODIS tends to under-estimate AOD during winter, and this may be due to improper assumptions of surface reflectance and the incorrect selection of aerosol types. AERONET retrieved single scattering albedo (SSA) values in winter (0.82-0.86), suggesting the dominance of absorbing aerosols, slightly increased (0.87-0.89) in pre-monsoon season, indicating more scattering type of aerosols. These values are about 8.9-1.1 lower than those of the assumed SSA values in the MODIS algorithm