Assessment of Aerosol Optical Depth Under Background and Polluted Conditions Using AERONET and VIIRS Datasets

We investigated aerosol optical depth (AOD) under background and polluted conditions using Aerosol Robotic Network (AERONET) and Visible Infrared Imaging Radiometer Suite (VIIRS) observations. The AOD data were separated into background, high, and median AOD (BAOD, HAOD, and MAOD, respectively) based on the cumulative AOD distribution at each point and then their spatiotemporal variations were analyzed. Persistent pollutant emissions from industrial activity in South Asia (SUA) and Northeast Asia (NEA) produced the highest BAOD values. Gridded-BAODs obtained from VIIRS Deep Blue AOD products showed widespread high-level BAOD over the oceans associated with transport from dust and biomass burning events. The temporal variations in BAOD and HAOD were generally consistent with that of MAOD, but differences were found in seasonal variation as well as in long-term trends in some regions. Southeast Asia (SEA) and South America/South Africa (SAM/SAF) showed similar HAOD levels owing to biomass burning, but BAODs were higher in SEA than in SAM/SAF. In NEA, BAOD was lowest during the summer rainy season, as opposed to the peaks in MAOD and HAOD. Long-term trends of the AODs show clear regional characteristics. The AODs have decreasing trends in NEA, Europe/Mediterranean basin, and Northeast America but increasing trends in SUA, North Africa, and the Middle East. The trend of HAOD in Northwest America and Australia was opposite to that of BAOD. The spatiotemporal patterns of the HAOD and BAOD provide detailed information on changes in aerosol loading compared to using only MAOD

AERONET-Based Nonspherical Dust Optical Models and Effects on the VIIRS Deep Blue/SOAR Over-Water Aerosol Product

Aerosol Robotic Network (AERONET)-based nonspherical dust optical models are developed and applied to the Satellite Ocean Aerosol Retrieval (SOAR) algorithm as part of the Version 1 Visible Infrared Imaging Radiometer Suite (VIIRS) NASA 'Deep Blue' aerosol data product suite. The optical models are created using Version 2 AERONET inversion data at six distinct sites influenced frequently by dust aerosols from different source regions. The same spheroid shape distribution as used in the AERONET inversion algorithm is assumed to account for the nonspherical characteristics of mineral dust, which ensures the consistency between the bulk scattering properties of the developed optical models with the AERONET-retrieved microphysical and optical properties. For the Version 1 SOAR aerosol product, the dust optical models representative for Capo Verde site are used, considering the strong influence of Saharan dust over the global ocean in terms of amount and spatial coverage. Comparisons of the VIIRS-retrieved aerosol optical properties against AERONET direct-Sun observations at three island coastal sites suggest that the use of nonspherical dust optical models significantly improves the retrievals of aerosol optical depth (AOD) and Angstrom exponent by mitigating the well-known artifact of scattering angle dependence of the variables observed when incorrectly assuming spherical dust. The resulting removal of these artifacts results in a more natural spatial pattern of AOD along the transport path of Saharan dust to the Atlantic Ocean; i.e., AOD decreases with increasing distance transported, whereas the spherical assumption leads to a strong wave pattern due to the spurious scattering angle dependence of AOD

Air Quality over China

The strong economic growth in China in recent decades, together with meteorological factors, has resulted in serious air pollution problems, in particular over large industrialized areas with high population density. To reduce the concentrations of pollutants, air pollution control policies have been successfully implemented, resulting in the gradual decrease of air pollution in China during the last decade, as evidenced from both satellite and ground-based measurements. The aims of the Dragon 4 project “Air quality over China” were the determination of trends in the concentrations of aerosols and trace gases, quantification of emissions using a top-down approach and gain a better understanding of the sources, transport and underlying processes contributing to air pollution. This was achieved through (a) satellite observations of trace gases and aerosols to study the temporal and spatial variability of air pollutants; (b) derivation of trace gas emissions from satellite observations to study sources of air pollution and improve air quality modeling; and (c) study effects of haze on air quality. In these studies, the satellite observations are complemented with ground-based observations and modeling

Merging regional and global aerosol optical depth records from major available satellite products

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

Working with the enemy? Social work education and men who use intimate partner violence

This article examines service user involvement in social work education. It discusses the challenges and ethical considerations of involving populations who may previously have been excluded from user involvement initiatives, raising questions about the benefits and challenges of their involvement. The article then provides discussion of an approach to service user involvement in social work education with one of these populations, men who use violence in their intimate relationships, and concludes by considering the implications of their involvement for the social work academy

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

Assimilating spaceborne lidar dust extinction can improve dust forecasts

Atmospheric mineral dust has a rich tri-dimensional spatial and temporal structure that is poorly constrained in forecasts and analyses when only column-integrated aerosol optical depth (AOD) is assimilated. At present, this is the case of most operational global aerosol assimilation products. Aerosol vertical distributions obtained from spaceborne lidars can be assimilated in aerosol models, but questions about the extent of their benefit upon analyses and forecasts along with their consistency with AOD assimilation remain unresolved. Our study thoroughly explores the added value of assimilating spaceborne vertical dust profiles, with and without the joint assimilation of dust optical depth (DOD). We also discuss the consistency in the assimilation of both sources of information and analyse the role of the smaller footprint of the spaceborne lidar profiles in the results. To that end, we have performed data assimilation experiments using dedicated dust observations for a period of 2 months over northern Africa, the Middle East, and Europe. We assimilate DOD derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-Orbiting Partnership (SUOMI-NPP) Deep Blue and for the first time Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP)-based LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) pure-dust extinction coefficient profiles on an aerosol model. The evaluation is performed against independent ground-based DOD derived from AErosol RObotic NETwork (AERONET) Sun photometers and ground-based lidar dust extinction profiles from the Cyprus Clouds Aerosol and Rain Experiment (CyCARE) and PREparatory: does dust TriboElectrification affect our ClimaTe (Pre-TECT) field campaigns. Jointly assimilating LIVAS and Deep Blue data reduces the root mean square error (RMSE) in the DOD by 39 % and in the dust extinction coefficient by 65 % compared to a control simulation that excludes assimilation. We show that the assimilation of dust extinction coefficient profiles provides a strong added value to the analyses and forecasts. When only Deep Blue data are assimilated, the RMSE in the DOD is reduced further, by 42 %. However, when only LIVAS data are assimilated, the RMSE in the dust extinction coefficient decreases by 72 %, the largest improvement across experiments. We also show that the assimilation of dust extinction profiles yields better skill scores than the assimilation of DOD under an equivalent sensor footprint. Our results demonstrate the strong potential of future lidar space missions to improve desert dust forecasts, particularly if they foresee a depolarization lidar channel to allow discrimination of desert dust from other aerosol types.This work received funding from the European Union’s Horizon 2020 research and innovation programme (Marie Skłodowska-Curie (grant no. 754433)), the European Research Council (FRAGMENT (grant no. 773051)), and the AXA Research Fund. We were also supported by the Ministerio de Ciencia, Innovación y Universidades (MICINN), as part of the BROWNING project RTI2018-099894-B-I00 and NUTRIENT project CGL2017-88911-R, along with PRACE and RES for awarding access to Marenostrum4 based in Spain at the Barcelona Supercomputing Center through the eFRAGMENT2 and AECT2020-1-0007 projects. Martina Klose received funding from the European Union’s Horizon 2020 research and innovation programme (Marie Skłodowska-Curie (grant no. 789630)). Martina Klose was also supported by the Helmholtz Association’s Initiative and Networking Fund (grant no. VH-NG-1533). Vassilis Amiridis and Eleni Marinou were supported by ERC Consolidator Grant 2016 D-TECT: “Does dust TriboElectrification affect our ClimaTe?” (grant no. 725698). Eleni Marinou was supported by a DLR VO-R young investigator group and the Deutscher Akademischer Austauschdienst (grant no. 57370121). Emmanouil Proestakis was supported by the project PANhellenic infrastructure for Atmospheric Composition and climatE change (grant no. MIS5021516), which is implemented under the Action Reinforcement of the Research and Innovation Infrastructure, funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (grant no. NSRF2014–2020) and co-financed by Greece and the European Union (European Regional Development Fund). This research was supported by the German–Israeli Foundation for Scientific Research and Development (GIF, grant no. I1262-401.10/2014), the European Union’s Framework Programme for Research and Innovation, Horizon 2020 (ACTRIS-2, grant no. 654109), and the former European Commission Seventh Framework Programme FP7/2007–2013 (ACTRIS (grant no. 262254) and BACCHUS (grant no. 603445)).Peer ReviewedObjectius de Desenvolupament Sostenible::13 - Acció per al ClimaObjectius de Desenvolupament Sostenible::13 - Acció per al Clima::13.3 - Millorar l’educació, la conscienciació i la capacitat humana i institucional en relació amb la mitigació del canvi climàtic, l’adaptació a aquest, la reducció dels efectes i l’alerta primerencaPostprint (published version