[1] Aerosol mixtures composed of coarse mode desert dust combined with fine mode combustion generated aerosols (from fossil fuel and biomass burning sources) were investigated at three locations that are in and/or downwind of major global aerosol emission source regions. Multiyear monitoring data at Aerosol Robotic Network sites in Beijing (central eastern China), Kanpur (Indo-Gangetic Plain, northern India), and Ilorin (Nigeria, Sudanian zone of West Africa) were utilized to study the climatological characteristics of aerosol optical properties. Multiyear climatological averages of spectral single scattering albedo (SSA) versus fine mode fraction (FMF) of aerosol optical depth at 675 nm at all three sites exhibited relatively linear trends up to ∼50% FMF. This suggests the possibility that external linear mixing of both fine and coarse mode components (weighted by FMF) dominates the SSA variation, where the SSA of each component remains relatively constant for this range of FMF only. However, it is likely that a combination of other factors is also involved in determining the dynamics of SSA as a function of FMF, such as fine mode particles adhering to coarse mode dust. The spectral variation of the climatological averaged aerosol absorption optical depth (AAOD) was nearly linear in logarithmic coordinates over the wavelength range of 440-870 nm for both the Kanpur and Ilorin sites. However, at two sites in China (Beijing and Xianghe), a distinct nonlinearity in spectral AAOD in logarithmic space was observed, suggesting the possibility of anomalously strong absorption in coarse mode aerosols increasing the 870 nm AAOD.Citation: Eck, T. F., et al. (2010), Climatological aspects of the optical properties of fine/coarse mode aerosol mixtures,

[1] Partitioning of mineral dust, pollution, smoke, and mixtures using remote sensing techniques can help improve accuracy of satellite retrievals and assessments of the aerosol radiative impact on climate. Spectral aerosol optical depth (t) and single scattering albedo (w o ) from Aerosol Robotic Network (AERONET) measurements are used to form absorption (i.e., w o and absorption Ångström exponent (a abs )) and size (i.e., extinction Ångström exponent (a ext ) and fine mode fraction of t) relationships to infer dominant aerosol types. Using the long-term AERONET data set (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010), 19 sites are grouped by aerosol type based on known source regions to (1) determine the average w o and a abs at each site (expanding upon previous work), (2) perform a sensitivity study on a abs by varying the spectral w o , and (3) test the ability of each absorption and size relationship to distinguish aerosol types. The spectral w o averages indicate slightly more aerosol absorption (i.e., a 0.0 < dw o ≤ 0.02 decrease) than in previous work, and optical mixtures of pollution and smoke with dust show stronger absorption than dust alone. Frequency distributions of a abs show significant overlap among aerosol type categories, and at least 10% of the a abs retrievals in each category are below 1.0. Perturbing the spectral w o by AE0.03 induces significant a abs changes from the unperturbed value by at least $AE0.6 for Dust, $AE0.2 for Mixed, and $AE0.1 for Urban/Industrial and Biomass Burning. The w o440nm and a ext440-870nm relationship shows the best separation among aerosol type clusters, providing a simple technique for determining aerosol type from surface-and future space-based instrumentation.

[1] High aerosol loading over the northern Indian subcontinent can result in poor air quality leading to human health consequences and climate perturbations. The international 2008 TIGERZ experiment intensive operational period (IOP) was conducted in the Indo-Gangetic Plain (IGP) around the industrial city of Kanpur (26.51°N, 80.23°E), India, during the premonsoon (April-June). Aerosol Robotic Network (AERONET) Sun photometers performed frequent measurements of aerosol properties at temporary sites distributed within an area covering ∼50 km 2 around Kanpur to characterize pollution and dust in a region where complex aerosol mixtures and semi-bright surface effects complicate satellite retrieval algorithms. TIGERZ IOP Sun photometers quantified aerosol optical depth (AOD) increases up to ∼0.10 within and downwind of the city, with urban emissions accounting for ∼10-20% of the IGP aerosol loading on deployment days. TIGERZ IOP area-averaged volume size distribution and single scattering albedo retrievals indicated spatially homogeneous, uniformly sized, spectrally absorbing pollution and dust particles. Aerosol absorption and size relationships were used to categorize black carbon and dust as dominant absorbers and to identify a third category in which both black carbon and dust dominate absorption. Moderate Resolution Imaging Spectroradiometer (MODIS) AOD retrievals with the lowest quality assurance (QA ≥ 0) flags were biased high with respect to TIGERZ IOP area-averaged measurements. MODIS AOD retrievals with QA ≥ 0 had moderate correlation (R 2 = 0.52-0.69) with the Kanpur AERONET site, whereas retrievals with QA > 0 were limited in number. Mesoscale-distributed Sun photometers quantified temporal and spatial variability of aerosol properties, and these results were used to validate satellite retrievals.Citation: Giles, D. M., et al. (2011), Aerosol properties over the Indo-Gangetic Plain: A mesoscale perspective from the TIGERZ experiment,

Abstract. Over the past 24 years, the AErosol RObotic NETwork (AERONET) program has provided highly accurate remote-sensing characterization of aerosol optical and physical properties for an increasingly extensive geographic distribution including all continents and many oceanic island and coastal sites. The measurements and retrievals from the AERONET global network have addressed satellite and model validation needs very well, but there have been challenges in making comparisons to similar parameters from in situ surface and airborne measurements. Additionally, with improved spatial and temporal satellite remote sensing of aerosols, there is a need for higher spatial-resolution ground-based remote-sensing networks. An effort to address these needs resulted in a number of field campaign networks called Distributed Regional Aerosol Gridded Observation Networks (DRAGONs) that were designed to provide a database for in situ and remote-sensing comparison and analysis of local to mesoscale variability in aerosol properties. This paper describes the DRAGON deployments that will continue to contribute to the growing body of research related to meso-and microscale aerosol features and processes. The research presented in this special issue illustrates the diversity of topics that has resulted from the application of data from these networks.

&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; The AErosol RObotic NETwork (AERONET) program over the past 24 years has provided highly accurate remote sensing characterization of aerosol optical and physical properties for an increasingly extensive geographic distribution that includes all continents and many island sites. The measurements and retrievals from the AERONET global network have addressed satellite and model validation needs very well, but there have been challenges in making comparisons to similar parameters from in situ surface and airborne measurements. Additionally, with improved spatial and temporal satellite remote sensing of aerosols, there is a need for higher spatial resolution ground-based remote sensing networks. An effort to address this need resulted in a number of field campaign networks called Distributed Regional Aerosol Gridded Observation Networks (DRAGONs) that were designed to provide a database for in situ and remote sensing comparison and analysis of local to meso-scale variability of aerosol properties. This paper describes the networks that that have contributed and will continue to contribute to that body of research. The research presented in this special issue illustrates the diversity of topics that has resulted from the application of data from these networks.&lt;/p&gt;

We discuss accuracy of our recently developed RT code SORD using 2 benchmark scenarios published by the IPRT group in 2015. These scenarios define atmospheres with a complicate dependence of scattering and absorption properties over height (profile). Equal step, dh=1km, is assumed in the profiles. We developed subroutines that split such atmospheres into layers of the same optical thickness, dτ. We provide full text of the subroutines with comments in Appendix. The dτ is a step for vertical integration in the method of successive orders. Modification of the input profiles from "equal step over h" to "equal step over τ" changes input for RT simulations. This may cause errors at or above the acceptable level of the measurement uncertainty. We show errors of the RT code SORD for both intensity and polarization. In addition to that, using our discrete ordinates RT code IPOL, we discuss one more IPRT scenario, in which changes in height profile indeed cause unacceptable errors. Clear understanding of source and magnitude of these errors is important, e.g. for the AERONET retrieval algorithm.

We report a new publicly available radiative transfer (RT) code for numerical simulation of polarized light scattering in plane-parallel atmosphere of the Earth. Using 44 benchmark tests, we prove high accuracy of the new RT code, SORD (Successive ORDers of scattering 1, 2 ). We describe capabilities of SORD and show run time for each test on two different machines. At present, SORD is supposed to work as part of the Aerosol Robotic NETwork 3 (AERONET) inversion algorithm. For natural integration with the AERONET software, SORD

Abstract. Knowledge of the global distribution of tropospheric aerosols is important for studying the effects of aerosols on global climate. Chemical transport models rely on archived meteorological fields, accounting for aerosol sources, transport and removal processes can simulate the global distribution of atmospheric aerosols. However, the accuracy of global aerosol modeling is limited. Uncertainty in location and strength of aerosol emission sources is a major factor in limiting modeling accuracy. This paper describes an effort to develop an algorithm for retrieving global sources of aerosol from satellite observations by inverting the GOCART aerosol transport model. To optimize inversion algorithm performance, the inversion was formulated as a generalized multi-term least-squares-type fitting. This concept uses the principles of statistical optimization and unites diverse retrieval techniques into a single flexible inversion procedure. It is particularly useful for choosing and refining a priori constraints in the retrieval algorithm. For example, it is demonstrated that a priori limitations on the partial derivatives of retrieved characteristics, which are widely used in atmospheric remote sensing, can also be useful in inverse modeling for constraining time and space variability of the retrieved global aerosol emissions. The similarities and differences with the standard "Kalman filter" inverse modeling approach and the "Phillips-Tikhonov-Twomey" constrained inversion widely used in remote sensing are discussed. In order to retain the originally high space and time resolution of the global model in the inversion of a long record of observations, the algorithm was expressed using adjoint operators in a form convenient for practical development of the inversion from codes implementing forward model simulations. The inversion algorithm was implemented using the GOCART aerosol transport model. The numerical tests we conducted showed successful retrievals of global aerosol emissions with a 2°×2.5° resolution by inverting the GOCART output. For achieving satisfactory retrieval from satellite sensors such as MODIS, the emissions were assumed constant within the 24 h diurnal cycle and aerosol differences in chemical composition were neglected. Such additional assumptions were needed to constrain the inversion due to limitations of satellite temporal coverage and sensitivity to aerosol parameters. As a result, the algorithm was defined for the retrieval of emission sources of fine and coarse mode aerosols from the MODIS fine and coarse mode aerosol optical thickness data respectively. Numerical tests showed that such assumptions are justifiable, taking into account the accuracy of the model and observations and that it provides valuable retrievals of the location and the strength of the aerosol emissions. The algorithm was applied to MODIS observations during two weeks in August 2000. The global placement of fine mode aerosol sources retrieved from inverting MODIS observations was coherent with available independent knowledge. This was particularly encouraging since the inverse method did not use any a priori information about the sources and it was initialized under a "zero aerosol emission" assumption. The retrieval reproduced the instantaneous global MODIS observations with a standard deviation in fitting of aerosol optical thickness of ~0.04. The optical thickness during high aerosol loading events was reproduced with a standard deviation of ~48%. Applications of the algorithm for the retrieval of coarse mode aerosol emissions were less successful, mainly due to the currently existing lack of MODIS data over high reflectance desert dust sources. Possibilities for enhancing the global satellite data inversion by using diverse a priori constraints on the retrieval are demonstrated. The potential and limitations of applying our approach for the retrieval of global aerosol sources from aerosol remote sensing are discussed.