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B N Holben - One of the best experts on this subject based on the ideXlab platform.
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advancements in the aerosol Robotic Network aeronet version 3 database automated near real time quality control algorithm with improved cloud screening for sun photometer aerosol optical depth aod measurements
Atmospheric Measurement Techniques, 2019Co-Authors: D M Giles, Ilya Slutsker, A. Smirnov, T F Eck, B N Holben, J S Schafer, Alexander Sinyuk, M Sorokin, Jasper R LewisAbstract:Abstract. The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloud-screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-field-deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty estimate for which average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of + 0.002 with a ± 0.02 SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases.
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observations of the interaction and transport of fine mode aerosols with cloud and or fog in northeast asia from aerosol Robotic Network and satellite remote sensing
Journal of Geophysical Research, 2018Co-Authors: D M Giles, A. Smirnov, T F Eck, B N Holben, Jeffrey S Reid, Peng Xian, A Sinyuk, J S Schafer, Ilya SlutskerAbstract:Analysis of sun photometer measured and satellite retrieved aerosol optical depth (AOD) data has shown that major aerosol pollution events with very high fine mode AOD (>1.0 in mid-visible) in the China/Korea/Japan region are often observed to be associated with significant cloud cover. This makes remote sensing of these events difficult even for high temporal resolution sun photometer measurements. Possible physical mechanisms for these events that have high AOD include a combination of aerosol humidification, cloud processing, and meteorological co-variation with atmospheric stability and convergence. The new development of Aerosol Robotic Network (AERONET) Version 3 Level 2 AOD with improved cloud screening algorithms now allow for unprecedented ability to monitor these extreme fine mode pollution events. Further, the Spectral Deconvolution Algorithm (SDA) applied to Level 1 data (L1; no cloud screening) provides an even more comprehensive assessment of fine mode AOD than L2 in current and previous data versions. Studying the 2012 winter-summer period, comparisons of AERONET L1 SDA daily average fine mode AOD data showed that Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote sensing of AOD often did not retrieve and/or identify some of the highest fine mode AOD events in this region. Also, compared to models that include data assimilation of satellite retrieved AOD, the L1 SDA fine mode AOD was significantly higher in magnitude, particularly for the highest AOD events that were often associated with significant cloudiness.
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cloud droplet size and liquid water path retrievals from zenith radiance measurements examples from the atmospheric radiation measurement program and the aerosol Robotic Network
Atmospheric Chemistry and Physics, 2012Co-Authors: J C Chiu, D M Giles, Alexander Marshak, Chiunghuei Huang, Tamas Varnai, Robin J Hogan, B N Holben, Ewan J OconnorAbstract:Abstract. The ground-based Atmospheric Radiation Measurement Program (ARM) and NASA Aerosol Robotic Network (AERONET) routinely monitor clouds using zenith radiances at visible and near-infrared wavelengths. Using the transmittance calculated from such measurements, we have developed a new retrieval method for cloud effective droplet size and conducted extensive tests for non-precipitating liquid water clouds. The underlying principle is to combine a liquid-water-absorbing wavelength (i.e., 1640 nm) with a non-water-absorbing wavelength for acquiring information on cloud droplet size and optical depth. For simulated stratocumulus clouds with liquid water path less than 300 g m −2 and horizontal resolution of 201 m, the retrieval method underestimates the mean effective radius by 0.8 μm, with a root-mean-squared error of 1.7 μm and a relative deviation of 13%. For actual observations with a liquid water path less than 450 g m −2 at the ARM Oklahoma site during 2007–2008, our 1.5-min-averaged retrievals are generally larger by around 1 μm than those from combined ground-based cloud radar and microwave radiometer at a 5-min temporal resolution. We also compared our retrievals to those from combined shortwave flux and microwave observations for relatively homogeneous clouds, showing that the bias between these two retrieval sets is negligible, but the error of 2.6 μm and the relative deviation of 22% are larger than those found in our simulation case. Finally, the transmittance-based cloud effective droplet radii agree to better than 11% with satellite observations and have a negative bias of 1 μm. Overall, the retrieval method provides reasonable cloud effective radius estimates, which can enhance the cloud products of both ARM and AERONET.
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fog and cloud induced aerosol modification observed by the aerosol Robotic Network aeronet
Journal of Geophysical Research, 2012Co-Authors: D M Giles, T F Eck, B N Holben, Jeffrey S Reid, Miguel Rivas, Ramesh P Singh, S N Tripathi, Carol J Bruegge, Steven PlatnickAbstract:[1] Large fine mode–dominated aerosols (submicron radius) in size distributions retrieved from the Aerosol Robotic Network (AERONET) have been observed after fog or low-altitude cloud dissipation events. These column-integrated size distributions have been obtained at several sites in many regions of the world, typically after evaporation of low-altitude cloud such as stratocumulus or fog. Retrievals with cloud-processed aerosol are sometimes bimodal in the accumulation mode with the larger-size mode often ∼0.4–0.5μm radius (volume distribution); the smaller mode, typically ∼0.12 to ∼0.20 μm, may be interstitial aerosol that were not modified by incorporation in droplets and/or aerosol that are less hygroscopic in nature. Bimodal accumulation mode size distributions have often been observed from in situ measurements of aerosols that have interacted with clouds, and AERONET size distribution retrievals made after dissipation of cloud or fog are in good agreement with particle sizes measured by in situ techniques for cloud-processed aerosols. Aerosols of this type and large size range (in lower concentrations) may also be formed by cloud processing in partly cloudy conditions and may contribute to the “shoulder” of larger-size particles in the accumulation mode retrievals, especially in regions where sulfate and other soluble aerosol are a significant component of the total aerosol composition. Observed trends of increasing aerosol optical depth (AOD) as fine mode radius increased suggests higher AOD in the near-cloud environment and higher overall AOD than typically obtained from remote sensing owing to bias toward sampling at low cloud fraction.
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multiangle imaging spectroradiometer global aerosol product assessment by comparison with the aerosol Robotic Network
Journal of Geophysical Research, 2010Co-Authors: Ralph A Kahn, David J. Diner, Michael J. Garay, A. Smirnov, Barbara J Gaitley, B N HolbenAbstract:[1] A statistical approach is used to assess the quality of the Multiangle Imaging SpectroRadiometer (MISR) version 22 (V22) aerosol products. Aerosol optical depth (AOD) retrieval results are improved relative to the early postlaunch values reported by [Kahn et al. (2005)], which varied with particle type category. Overall, about 70% to 75% of MISR AOD retrievals fall within 0.05 or 20% × AOD of the paired validation data from the Aerosol Robotic Network (AERONET), and about 50% to 55% are within 0.03 or 10% x AERONET AOD, except at sites where dust or mixed dust and smoke are commonly found. Retrieved particle microphysical properties amount to categorical values, such as three size groupings: "small," "medium," and "large." For particle size, ground-based AERONET sun photometer Angstrom exponents are used to assess statistically the corresponding MISR values, which are interpreted in terms of retrieved size categories. Coincident single-scattering albedo (SSA) and fraction AOD spherical data are too limited for statistical validation. V22 distinguishes two or three size bins, depending on aerosol type, and about two bins in SSA (absorbing vs. nonabsorbing), as well as spherical vs. nonspherical particles, under good retrieval conditions. Particle type sensitivity varies considerably with conditions and is diminished for midvisible AODs below about 0.15 or 0.2. On the basis of these results, specific algorithm upgrades are proposed and are being investigated by the MISR team for possible implementation in future versions of the product.
A. Smirnov - One of the best experts on this subject based on the ideXlab platform.
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advancements in the aerosol Robotic Network aeronet version 3 database automated near real time quality control algorithm with improved cloud screening for sun photometer aerosol optical depth aod measurements
Atmospheric Measurement Techniques, 2019Co-Authors: D M Giles, Ilya Slutsker, A. Smirnov, T F Eck, B N Holben, J S Schafer, Alexander Sinyuk, M Sorokin, Jasper R LewisAbstract:Abstract. The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloud-screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-field-deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty estimate for which average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of + 0.002 with a ± 0.02 SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases.
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observations of the interaction and transport of fine mode aerosols with cloud and or fog in northeast asia from aerosol Robotic Network and satellite remote sensing
Journal of Geophysical Research, 2018Co-Authors: D M Giles, A. Smirnov, T F Eck, B N Holben, Jeffrey S Reid, Peng Xian, A Sinyuk, J S Schafer, Ilya SlutskerAbstract:Analysis of sun photometer measured and satellite retrieved aerosol optical depth (AOD) data has shown that major aerosol pollution events with very high fine mode AOD (>1.0 in mid-visible) in the China/Korea/Japan region are often observed to be associated with significant cloud cover. This makes remote sensing of these events difficult even for high temporal resolution sun photometer measurements. Possible physical mechanisms for these events that have high AOD include a combination of aerosol humidification, cloud processing, and meteorological co-variation with atmospheric stability and convergence. The new development of Aerosol Robotic Network (AERONET) Version 3 Level 2 AOD with improved cloud screening algorithms now allow for unprecedented ability to monitor these extreme fine mode pollution events. Further, the Spectral Deconvolution Algorithm (SDA) applied to Level 1 data (L1; no cloud screening) provides an even more comprehensive assessment of fine mode AOD than L2 in current and previous data versions. Studying the 2012 winter-summer period, comparisons of AERONET L1 SDA daily average fine mode AOD data showed that Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote sensing of AOD often did not retrieve and/or identify some of the highest fine mode AOD events in this region. Also, compared to models that include data assimilation of satellite retrieved AOD, the L1 SDA fine mode AOD was significantly higher in magnitude, particularly for the highest AOD events that were often associated with significant cloudiness.
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multiangle imaging spectroradiometer global aerosol product assessment by comparison with the aerosol Robotic Network
Journal of Geophysical Research, 2010Co-Authors: Ralph A Kahn, David J. Diner, Michael J. Garay, A. Smirnov, Barbara J Gaitley, B N HolbenAbstract:[1] A statistical approach is used to assess the quality of the Multiangle Imaging SpectroRadiometer (MISR) version 22 (V22) aerosol products. Aerosol optical depth (AOD) retrieval results are improved relative to the early postlaunch values reported by [Kahn et al. (2005)], which varied with particle type category. Overall, about 70% to 75% of MISR AOD retrievals fall within 0.05 or 20% × AOD of the paired validation data from the Aerosol Robotic Network (AERONET), and about 50% to 55% are within 0.03 or 10% x AERONET AOD, except at sites where dust or mixed dust and smoke are commonly found. Retrieved particle microphysical properties amount to categorical values, such as three size groupings: "small," "medium," and "large." For particle size, ground-based AERONET sun photometer Angstrom exponents are used to assess statistically the corresponding MISR values, which are interpreted in terms of retrieved size categories. Coincident single-scattering albedo (SSA) and fraction AOD spherical data are too limited for statistical validation. V22 distinguishes two or three size bins, depending on aerosol type, and about two bins in SSA (absorbing vs. nonabsorbing), as well as spherical vs. nonspherical particles, under good retrieval conditions. Particle type sensitivity varies considerably with conditions and is diminished for midvisible AODs below about 0.15 or 0.2. On the basis of these results, specific algorithm upgrades are proposed and are being investigated by the MISR team for possible implementation in future versions of the product.
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misr calibration and implications for low light level aerosol retrieval over dark water
Journal of the Atmospheric Sciences, 2005Co-Authors: Ralph A Kahn, Oleg Dubovik, David J. Diner, Barbara J Gaitley, John V Martonchik, B N Holben, Carol J Bruegge, W A Abdou, Wenhao Li, A. SmirnovAbstract:Abstract Studying aerosols over ocean is one goal of the Multiangle Imaging Spectroradiometer (MISR) and other spaceborne imaging systems. But top-of-atmosphere equivalent reflectance typically falls in the range of 0.03 to 0.12 at midvisible wavelengths and can be below 0.01 in the near-infrared, when an optically thin aerosol layer is viewed over a dark ocean surface. Special attention must be given to radiometric calibration if aerosol optical thickness, and any information about particle microphysical properties, are to be reliably retrieved from such observations. MISR low-light-level vicarious calibration is performed in the vicinity of remote islands hosting Aerosol Robotic Network (AERONET) sun- and sky-scanning radiometers, under low aerosol loading, low wind speed, relatively cloud free conditions. MISR equivalent reflectance is compared with values calculated from a radiative transfer model constrained by coincident, AERONET-retrieved aerosol spectral optical thickness, size distribution, and s...
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maritime component in aerosol optical models derived from aerosol Robotic Network data
Journal of Geophysical Research, 2003Co-Authors: Oleg Dubovik, A. Smirnov, T F Eck, Robert Frouin, B N Holben, Ilya SlutskerAbstract:[1] Aerosol optical properties above the oceans vary considerably, depending on contributions of major aerosol components, i.e., urban/industrial pollution, desert dust, biomass burning, and maritime. The optical characterization of these aerosols is fundamental to the parameterization of radiative forcing models as well as to the atmospheric correction of ocean color imagery. We present a model of the maritime aerosol component derived using Aerosol Robotic Network (AERONET) data from three island locations: Bermuda (Atlantic Ocean), Lanai, Hawaii (Pacific Ocean), and Kaashidhoo, Maldives (Indian Ocean). To retrieve the maritime component, we have considered the data set with aerosol optical depth at a wavelength 500 nm less than 0.15 and Angstrom parameter α less than 1. The inferred maritime component in the columnar size distribution, which was found to be very similar for the three study sites, is bimodal with a fine mode at an effective radius (reff) ∼ 0.11–0.14 μm and a coarse mode reff of ∼1.8–2.1 μm. The results are comparable with size distributions reported in the literature. The refractive index is spectrally independent and estimated to be 1.37-0.001i (single-scattering albedo is about 0.98), based on the single-component homogenous particle composition assumption. Fractional contributions of the fine and coarse modes to the computed τa (500 nm) are within the range of τfine ∼ 0.03–0.05 and τcoarse ∼ 0.05–0.06 correspondingly. Angstrom parameters vary from ∼0.8 to 1.0 computed in the UV-visible (340–670 nm) and from 0.4 to 0.5 estimated in the near IR (870–2130 nm) spectral ranges. Aerosol phase functions are very similar for all three sites considered. The maritime aerosol component presented in this paper can serve as a candidate model in atmospheric correction algorithms.
Oleg Dubovik - One of the best experts on this subject based on the ideXlab platform.
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comparison of moderate resolution imaging spectroradiometer modis and aerosol Robotic Network aeronet remote sensing retrievals of aerosol fine mode fraction over ocean
Journal of Geophysical Research, 2005Co-Authors: Richard G Kleidman, Oleg Dubovik, T F Eck, Yoram J Kaufman, N T Oneill, Lorraine A Remer, D Tanre, B N HolbenAbstract:[1] Aerosol particle size is one of the fundamental quantities needed to determine the role of aerosols in forcing climate, modifying the hydrological cycle, and affecting human health and to separate natural from man-made aerosol components. Aerosol size information can be retrieved from remote-sensing instruments including satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based radiometers such as Aerosol Robotic Network (AERONET). Both satellite and ground-based instruments measure the total column ambient aerosol characteristics. Aerosol size can be characterized by a variety of parameters. Here we compare remote-sensing retrievals of aerosol fine mode fraction over ocean. AERONET retrieves fine mode fraction using two methods: the Dubovik inversion of sky radiances and the O'Neill inversion of spectral Sun measurements. Relative to the Dubovik inversion of AERONET sky measurements, MODIS slightly overestimates fine fraction for dust-dominated aerosols and underestimates in smoke- and pollution-dominated aerosol conditions. Both MODIS and the Dubovik inversion overestimate fine fraction for dust aerosols by 0.1–0.2 relative to the O'Neill method of inverting AERONET aerosol optical depth spectra. Differences between the two AERONET methods are principally the result of the different definitions of fine and coarse mode employed in their computational methodologies. These two methods should come into better agreement as a dynamic radius cutoff for fine and coarse mode is implemented for the Dubovik inversion. MODIS overestimation in dust-dominated aerosol conditions should decrease significantly with the inclusion of a nonspherical model.
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a normalized description of the direct effect of key aerosol types on solar radiation as estimated from aerosol Robotic Network aerosols and moderate resolution imaging spectroradiometer albedos
Journal of Geophysical Research, 2005Co-Authors: Mi Zhou, Oleg Dubovik, Hongbin Yu, Robert E Dickinson, B N HolbenAbstract:[1] The interactions between aerosols and solar radiation are determined by a combination of aerosol properties (i.e., types), surface properties (i.e., albedo) and clouds. These determining factors vary for different regions. We examine how these differences contribute to the impact of aerosols on the top-of-atmosphere (TOA) and surface radiation. In this study, the AERONET (Aerosol Robotic Network) aerosol climatology is used, in conjunction with surface albedo and cloud products from Moderate Resolution Imaging Spectroradiometer (MODIS), to calculate the aerosol direct radiative effect (ADRE) and its normalized form (NADRE). The NADRE is defined as the ADRE normalized by optical depth at 550 nm and is mainly determined by internal aerosol optical properties and geographical parameters. These terms are evaluated for cloud-free and cloudy conditions and for all-mode and fine-mode aerosols. Single-scattering albedo is an important variable determining ADRE of biomass burning. Because of stronger absorption by the smoke over South Africa the average NADRE over South America is ∼35% larger at the TOA but ∼38% smaller at the surface than that over South Africa. The surface albedo is another important factor in determining ADRE, especially for mineral dust. As the surface albedo varies from ∼0.1 to ∼0.35, it is observed that the dust NADRE ranges from −44 to −17 Wm−2 τ−1 at the TOA and from −80 to −48 Wm−2 τ−1 at the surface over the Saharan deserts, Arabian Peninsula, and their surrounding oceans. We also find that the NADRE of fine-mode aerosol is larger at the TOA but smaller at the surface in comparison to that of all-mode aerosol because of its larger single-scattering albedo and smaller asymmetry factor. Cloudy-sky ADRE is usually not negligible for the observed cloud optical thickness, but the TOA ADRE with clouds is sensitive to the relative location of aerosols and cloud layer.
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development of global aerosol models using cluster analysis of aerosol Robotic Network aeronet measurements
Journal of Geophysical Research, 2005Co-Authors: Ali Omar, Oleg Dubovik, Jaegwang Won, David M Winker, Soonchang Yoon, Patrick M MccormickAbstract:[1] We use radiance measurements and inversions of the Aerosol Robotic Network (AERONET) (Dubovik and King, 2000; Holben et al., 1998; Holben et al., 2001) to classify global atmospheric aerosols using the complete archive of the AERONET data set as of December 2002 and dating back to 1993 for some sites. More than 143,000 records of AERONET solar radiance measurements, derived aerosol size distributions, and complex refractive indices are used to generate the optical properties of the aerosol at more than 250 sites worldwide. Each record is used in a clustering algorithm as an object, with 26 variables comprising both microphysical and optical properties to obtain six significant clusters. Using the mean values of the optical and microphysical properties together with the geographic locations, we identified these clusters as desert dust, biomass burning, urban industrial pollution, rural background, polluted marine, and dirty pollution. When the records in each cluster are subdivided by optical depth class, the trends of the class size distributions show that the extensive properties (mode amplitude and total volume) vary by optical depth, while the intensive properties (mean radius and standard deviation) are relatively constant. Seasonal variations of aerosol types are consistent with observed trends. In particular, the periods of intense biomass burning activity and desert dust generation can be discerned from the data and the results of the analyses. Sensitivity and uncertainty analyses show that the clustering algorithm is quite robust. When subsets of the data set are randomly created and the clustering algorithm applied, we found that more than 94% of the records retain their classification. Adding 10% random noise to the microphysical properties and propagating this error through the scattering calculations, followed by the clustering algorithm, results in a misclassification rate of less than 9% when compared with the noise-free data.
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inferring black carbon content and specific absorption from aerosol Robotic Network aeronet aerosol retrievals
Journal of Geophysical Research, 2005Co-Authors: Gregory L Schuster, Oleg Dubovik, B N Holben, Eugene E ClothiauxAbstract:[1] Black carbon is ubiquitous in the atmosphere and is the main anthropogenic absorbing particulate. Absorption by black carbon is thought to be comparable to the cooling associated with sulfate aerosols, although present-day satellites are incapable of obtaining this measurement, and model estimates are highly uncertain. More measurements of black carbon concentration are necessary for improving and validating transport and general circulation models. The Aerosol Robotics Network (AERONET) of 180 worldwide radiometers offers an opportunity to obtain these measurements. We use the Maxwell Garnett effective medium approximation to infer the column-averaged black carbon concentration and specific absorption of AERONET retrievals at 46 locations. The yearly averaged black carbon column concentrations exhibit the expected regional dependence, with remote island locations having values about an order of magnitude lower than the continental biomass burning locations. The yearly averaged black carbon specific absorption cross section is consistent with other measured values, 9.9 m2 g−1 for 19,591 retrievals, but varies from 7.7 to 12.5 m2 g−1. We attribute this variability to the details of the size distributions and the fraction of black carbon contained in the aerosol mixture. We also used the Maxwell Garnett equations to parameterize the imaginary refractive index with respect to the black carbon volume fraction, enabling simple but accurate absorption estimates for aerosol mixtures when the black carbon fraction and size distribution is known. The black carbon concentrations that we derive from AERONET measurements correctly describe the radiance field and represent an alternative to absorption optical thickness in the link between models and AERONET measurements.
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misr calibration and implications for low light level aerosol retrieval over dark water
Journal of the Atmospheric Sciences, 2005Co-Authors: Ralph A Kahn, Oleg Dubovik, David J. Diner, Barbara J Gaitley, John V Martonchik, B N Holben, Carol J Bruegge, W A Abdou, Wenhao Li, A. SmirnovAbstract:Abstract Studying aerosols over ocean is one goal of the Multiangle Imaging Spectroradiometer (MISR) and other spaceborne imaging systems. But top-of-atmosphere equivalent reflectance typically falls in the range of 0.03 to 0.12 at midvisible wavelengths and can be below 0.01 in the near-infrared, when an optically thin aerosol layer is viewed over a dark ocean surface. Special attention must be given to radiometric calibration if aerosol optical thickness, and any information about particle microphysical properties, are to be reliably retrieved from such observations. MISR low-light-level vicarious calibration is performed in the vicinity of remote islands hosting Aerosol Robotic Network (AERONET) sun- and sky-scanning radiometers, under low aerosol loading, low wind speed, relatively cloud free conditions. MISR equivalent reflectance is compared with values calculated from a radiative transfer model constrained by coincident, AERONET-retrieved aerosol spectral optical thickness, size distribution, and s...
Ralph A Kahn - One of the best experts on this subject based on the ideXlab platform.
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MISR empirical stray light corrections in high-contrast scenes
Atmospheric Measurement Techniques, 2015Co-Authors: James A. Limbacher, Ralph A KahnAbstract:Abstract. We diagnose the potential causes for the Multi-angle Imaging SpectroRadiometer's (MISR) persistent high aerosol optical depth (AOD) bias at low AOD with the aid of coincident MODerate-resolution Imaging Spectroradiometer (MODIS) imagery from NASA's Terra satellite. Stray light in the MISR instrument is responsible for a large portion of the high AOD bias in high-contrast scenes, such as broken-cloud scenes that are quite common over ocean. Discrepancies among MODIS and MISR nadir-viewing blue, green, red, and near-infrared images are used to optimize seven parameters individually for each wavelength, along with a background reflectance modulation term that is modeled separately, to represent the observed features. Independent surface-based AOD measurements from the AErosol Robotic Network (AERONET) and the Marine Aerosol Network (MAN) are compared with MISR research aerosol retrieval algorithm (RA) AOD retrievals for 1118 coincidences to validate the corrections when applied to the nadir and off-nadir cameras. With these corrections, plus the baseline RA corrections and enhanced cloud screening applied, the median AOD bias for all data in the mid-visible (green, 558 nm) band decreases from 0.006 (0.020 for the MISR standard algorithm (SA)) to 0.000, and the RMSE decreases by 5 % (27 % compared to the SA). For AOD558 nm
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multiangle imaging spectroradiometer global aerosol product assessment by comparison with the aerosol Robotic Network
Journal of Geophysical Research, 2010Co-Authors: Ralph A Kahn, David J. Diner, Michael J. Garay, A. Smirnov, Barbara J Gaitley, B N HolbenAbstract:[1] A statistical approach is used to assess the quality of the Multiangle Imaging SpectroRadiometer (MISR) version 22 (V22) aerosol products. Aerosol optical depth (AOD) retrieval results are improved relative to the early postlaunch values reported by [Kahn et al. (2005)], which varied with particle type category. Overall, about 70% to 75% of MISR AOD retrievals fall within 0.05 or 20% × AOD of the paired validation data from the Aerosol Robotic Network (AERONET), and about 50% to 55% are within 0.03 or 10% x AERONET AOD, except at sites where dust or mixed dust and smoke are commonly found. Retrieved particle microphysical properties amount to categorical values, such as three size groupings: "small," "medium," and "large." For particle size, ground-based AERONET sun photometer Angstrom exponents are used to assess statistically the corresponding MISR values, which are interpreted in terms of retrieved size categories. Coincident single-scattering albedo (SSA) and fraction AOD spherical data are too limited for statistical validation. V22 distinguishes two or three size bins, depending on aerosol type, and about two bins in SSA (absorbing vs. nonabsorbing), as well as spherical vs. nonspherical particles, under good retrieval conditions. Particle type sensitivity varies considerably with conditions and is diminished for midvisible AODs below about 0.15 or 0.2. On the basis of these results, specific algorithm upgrades are proposed and are being investigated by the MISR team for possible implementation in future versions of the product.
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multiangle imaging spectroradiometer misr global aerosol optical depth validation based on 2 years of coincident aerosol Robotic Network aeronet observations
Journal of Geophysical Research, 2005Co-Authors: Ralph A Kahn, David J. Diner, Barbara J Gaitley, John V Martonchik, Kathleen A Crean, Brent N. HolbenAbstract:Performance of the Multiangle Imaging Spectroradiometer (MISR) early postlaunch aerosol optical thickness (AOT) retrieval algorithm is assessed quantitatively over land and ocean by comparison with a 2-year measurement record of globally distributed AERONET Sun photometers. There are sufficient coincident observations to stratify the data set by season and expected aerosol type. In addition to reporting uncertainty envelopes, we identify trends and outliers, and investigate their likely causes, with the aim of refining algorithm performance. Overall, about 2/3 of the MISR-retrieved AOT values fall within [0.05 or 20% x AOT] of Aerosol Robotic Network (AERONET). More than a third are within [0.03 or 10% x AOT]. Correlation coefficients are highest for maritime stations (approx.0.9), and lowest for dusty sites (more than approx.0.7). Retrieved spectral slopes closely match Sun photometer values for Biomass burning and continental aerosol types. Detailed comparisons suggest that adding to the algorithm climatology more absorbing spherical particles, more realistic dust analogs, and a richer selection of multimodal aerosol mixtures would reduce the remaining discrepancies for MISR retrievals over land; in addition, refining instrument low-light-level calibration could reduce or eliminate a small but systematic offset in maritime AOT values. On the basis of cases for which current particle models are representative, a second-generation MISR aerosol retrieval algorithm incorporating these improvements could provide AOT accuracy unprecedented for a spaceborne technique.
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comparison of coincident multiangle imaging spectroradiometer and moderate resolution imaging spectroradiometer aerosol optical depths over land and ocean scenes containing aerosol Robotic Network sites
Journal of Geophysical Research, 2005Co-Authors: W A Abdou, David J. Diner, Ralph A Kahn, Barbara J Gaitley, John V Martonchik, Carol J Bruegge, Lorraine A Remer, K Crean, B N HolbenAbstract:[1] The Multiangle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), launched on 18 December 1999 aboard the Terra spacecraft, are making global observations of top-of-atmosphere (TOA) radiances. Aerosol optical depths and particle properties are independently retrieved from these radiances using methodologies and algorithms that make use of the instruments' corresponding designs. This paper compares instantaneous optical depths retrieved from simultaneous and collocated radiances measured by the two instruments at locations containing sites within the Aerosol Robotic Network (AERONET). A set of 318 MISR and MODIS images, obtained during the months of March, June, and September 2002 at 62 AERONET sites, were used in this study. The results show that over land, MODIS aerosol optical depths at 470 and 660 nm are larger than those retrieved from MISR by about 35% and 10% on average, respectively, when all land surface types are included in the regression. The differences decrease when coastal and desert areas are excluded. For optical depths retrieved over ocean, MISR is on average about 0.1 and 0.05 higher than MODIS in the 470 and 660 nm bands, respectively. Part of this difference is due to radiometric calibration and is reduced to about 0.01 and 0.03 when recently derived band-to-band adjustments in the MISR radiometry are incorporated. Comparisons with AERONET data show similar patterns.
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misr calibration and implications for low light level aerosol retrieval over dark water
Journal of the Atmospheric Sciences, 2005Co-Authors: Ralph A Kahn, Oleg Dubovik, David J. Diner, Barbara J Gaitley, John V Martonchik, B N Holben, Carol J Bruegge, W A Abdou, Wenhao Li, A. SmirnovAbstract:Abstract Studying aerosols over ocean is one goal of the Multiangle Imaging Spectroradiometer (MISR) and other spaceborne imaging systems. But top-of-atmosphere equivalent reflectance typically falls in the range of 0.03 to 0.12 at midvisible wavelengths and can be below 0.01 in the near-infrared, when an optically thin aerosol layer is viewed over a dark ocean surface. Special attention must be given to radiometric calibration if aerosol optical thickness, and any information about particle microphysical properties, are to be reliably retrieved from such observations. MISR low-light-level vicarious calibration is performed in the vicinity of remote islands hosting Aerosol Robotic Network (AERONET) sun- and sky-scanning radiometers, under low aerosol loading, low wind speed, relatively cloud free conditions. MISR equivalent reflectance is compared with values calculated from a radiative transfer model constrained by coincident, AERONET-retrieved aerosol spectral optical thickness, size distribution, and s...
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advancements in the aerosol Robotic Network aeronet version 3 database automated near real time quality control algorithm with improved cloud screening for sun photometer aerosol optical depth aod measurements
Atmospheric Measurement Techniques, 2019Co-Authors: D M Giles, Ilya Slutsker, A. Smirnov, T F Eck, B N Holben, J S Schafer, Alexander Sinyuk, M Sorokin, Jasper R LewisAbstract:Abstract. The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloud-screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-field-deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty estimate for which average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of + 0.002 with a ± 0.02 SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases.
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observations of the interaction and transport of fine mode aerosols with cloud and or fog in northeast asia from aerosol Robotic Network and satellite remote sensing
Journal of Geophysical Research, 2018Co-Authors: D M Giles, A. Smirnov, T F Eck, B N Holben, Jeffrey S Reid, Peng Xian, A Sinyuk, J S Schafer, Ilya SlutskerAbstract:Analysis of sun photometer measured and satellite retrieved aerosol optical depth (AOD) data has shown that major aerosol pollution events with very high fine mode AOD (>1.0 in mid-visible) in the China/Korea/Japan region are often observed to be associated with significant cloud cover. This makes remote sensing of these events difficult even for high temporal resolution sun photometer measurements. Possible physical mechanisms for these events that have high AOD include a combination of aerosol humidification, cloud processing, and meteorological co-variation with atmospheric stability and convergence. The new development of Aerosol Robotic Network (AERONET) Version 3 Level 2 AOD with improved cloud screening algorithms now allow for unprecedented ability to monitor these extreme fine mode pollution events. Further, the Spectral Deconvolution Algorithm (SDA) applied to Level 1 data (L1; no cloud screening) provides an even more comprehensive assessment of fine mode AOD than L2 in current and previous data versions. Studying the 2012 winter-summer period, comparisons of AERONET L1 SDA daily average fine mode AOD data showed that Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote sensing of AOD often did not retrieve and/or identify some of the highest fine mode AOD events in this region. Also, compared to models that include data assimilation of satellite retrieved AOD, the L1 SDA fine mode AOD was significantly higher in magnitude, particularly for the highest AOD events that were often associated with significant cloudiness.
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fog and cloud induced aerosol modification observed by the aerosol Robotic Network aeronet
Journal of Geophysical Research, 2012Co-Authors: D M Giles, T F Eck, B N Holben, Jeffrey S Reid, Miguel Rivas, Ramesh P Singh, S N Tripathi, Carol J Bruegge, Steven PlatnickAbstract:[1] Large fine mode–dominated aerosols (submicron radius) in size distributions retrieved from the Aerosol Robotic Network (AERONET) have been observed after fog or low-altitude cloud dissipation events. These column-integrated size distributions have been obtained at several sites in many regions of the world, typically after evaporation of low-altitude cloud such as stratocumulus or fog. Retrievals with cloud-processed aerosol are sometimes bimodal in the accumulation mode with the larger-size mode often ∼0.4–0.5μm radius (volume distribution); the smaller mode, typically ∼0.12 to ∼0.20 μm, may be interstitial aerosol that were not modified by incorporation in droplets and/or aerosol that are less hygroscopic in nature. Bimodal accumulation mode size distributions have often been observed from in situ measurements of aerosols that have interacted with clouds, and AERONET size distribution retrievals made after dissipation of cloud or fog are in good agreement with particle sizes measured by in situ techniques for cloud-processed aerosols. Aerosols of this type and large size range (in lower concentrations) may also be formed by cloud processing in partly cloudy conditions and may contribute to the “shoulder” of larger-size particles in the accumulation mode retrievals, especially in regions where sulfate and other soluble aerosol are a significant component of the total aerosol composition. Observed trends of increasing aerosol optical depth (AOD) as fine mode radius increased suggests higher AOD in the near-cloud environment and higher overall AOD than typically obtained from remote sensing owing to bias toward sampling at low cloud fraction.
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nocturnal aerosol optical depth measurements with a small aperture automated photometer using the moon as a light source
Journal of Atmospheric and Oceanic Technology, 2011Co-Authors: Timothy A Berkoff, Mikail Sorokin, Tom Stone, T F Eck, R M Hoff, Ellsworth J Welton, Brent N. HolbenAbstract:AbstractA method is described that enables the use of lunar irradiance to obtain nighttime aerosol optical depth (AOD) measurements using a small-aperture photometer. In this approach, the U.S. Geological Survey lunar calibration system was utilized to provide high-precision lunar exoatmospheric spectral irradiance predictions for a ground-based sensor location, and when combined with ground measurement viewing geometry, provided the column optical transmittance for retrievals of AOD. Automated multiwavelength lunar measurements were obtained using an unmodified Cimel-318 sunphotometer sensor to assess existing capabilities and enhancements needed for day/night operation in NASA’s Aerosol Robotic Network (AERONET). Results show that even existing photometers can provide the ability for retrievals of aerosol optical depths at night near full moon. With an additional photodetector signal-to-noise improvement of 10–100, routine use over the bright half of the lunar phase and a much wider range of wavelengths...
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comparison of moderate resolution imaging spectroradiometer modis and aerosol Robotic Network aeronet remote sensing retrievals of aerosol fine mode fraction over ocean
Journal of Geophysical Research, 2005Co-Authors: Richard G Kleidman, Oleg Dubovik, T F Eck, Yoram J Kaufman, N T Oneill, Lorraine A Remer, D Tanre, B N HolbenAbstract:[1] Aerosol particle size is one of the fundamental quantities needed to determine the role of aerosols in forcing climate, modifying the hydrological cycle, and affecting human health and to separate natural from man-made aerosol components. Aerosol size information can be retrieved from remote-sensing instruments including satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based radiometers such as Aerosol Robotic Network (AERONET). Both satellite and ground-based instruments measure the total column ambient aerosol characteristics. Aerosol size can be characterized by a variety of parameters. Here we compare remote-sensing retrievals of aerosol fine mode fraction over ocean. AERONET retrieves fine mode fraction using two methods: the Dubovik inversion of sky radiances and the O'Neill inversion of spectral Sun measurements. Relative to the Dubovik inversion of AERONET sky measurements, MODIS slightly overestimates fine fraction for dust-dominated aerosols and underestimates in smoke- and pollution-dominated aerosol conditions. Both MODIS and the Dubovik inversion overestimate fine fraction for dust aerosols by 0.1–0.2 relative to the O'Neill method of inverting AERONET aerosol optical depth spectra. Differences between the two AERONET methods are principally the result of the different definitions of fine and coarse mode employed in their computational methodologies. These two methods should come into better agreement as a dynamic radius cutoff for fine and coarse mode is implemented for the Dubovik inversion. MODIS overestimation in dust-dominated aerosol conditions should decrease significantly with the inclusion of a nonspherical model.
