The Experts below are selected from a list of 10773 Experts worldwide ranked by ideXlab platform
B N Holben - One of the best experts on this subject based on the ideXlab platform.
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advances in the ocean color component of the aerosol robotic network AERONET oc
Journal of Atmospheric and Oceanic Technology, 2021Co-Authors: Giuseppe Zibordi, D M Giles, Ilya Slutsker, B N Holben, Davide Dalimonte, Marco Talone, M G SorokinAbstract:22 pages, 14 figures, 5 tables, 2 appendixes.-- Data availability statement: AERONET-OC data are available from NASA (https://AERONET.gsfc.nasa.gov/new_web/ocean_color.html)
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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, J S Schafer, Ilya Slutsker, Alexander Sinyuk, A. Smirnov, T F Eck, B N Holben, 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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aerosol optical characteristics retrieved from cimel sun photometer measurements AERONET near st petersburg
Atmospheric and Oceanic Optics, 2018Co-Authors: K A Volkova, A. Smirnov, B N Holben, A V Poberovsky, Yu M Timofeev, D V Ionov, Ilya SlutskerAbstract:The measurements with a CIMEL CE 318 sun photometer near St. Petersburg within the AERONET international monitoring network are analyzed. Typical regional aerosol optical parameters (aerosol optical depth, Angstrom exponent, and single scattering albedo) and their variations are determined. Certain regularities in variations in the aerosol parameters over the northwest region of the Russian Federation are revealed. The study is supplemented by the joint analysis of data from the nearby AERONET observation sites in Finland and Estonia, satellite measurements, and MERRA reanalysis data.
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assimilation of AERONET and modis aot observations using variational and ensemble data assimilation methods and its impact on aerosol forecasting skill
Journal of Geophysical Research, 2017Co-Authors: B N Holben, Juli I Rubin, Jeffrey S Reid, James A Hansen, Jeffrey L Anderson, Peng Xian, Douglas L Westphal, Jianglong ZhangAbstract:Data assimilation of AERONET and MODIS Aerosol Optical Thickness (AOT) for aerosol forecasting was tested within the Navy Aerosol Analysis Prediction System (NAAPS) framework, using variational and ensemble data assimilation methods. Navy aerosol forecasting is currently comprised of a deterministic NAAPS simulation coupled to NAVDAS-AOD, a 2-dimensional variational data assimilation system, for MODIS AOT assimilation. An ensemble version of NAAPS (ENAAPS) coupled to an Ensemble Adjustment Kalman Filter (EAKF) from DART was recently developed, allowing for a range of data assimilation and forecasting experiments to be run with deterministic NAAPS and ENAAPS. The main findings are that the EAKF, with its flow dependent error covariances, makes better use of sparse observations such as AERONET AOT. Assimilating individual AERONET observations in the 2DVar can increase the analysis errors when observations are located in high AOT gradient regions. By including AERONET with MODIS AOT assimilation, the magnitudes of peak aerosol events (AOT > 1) were better captured with improved temporal variability, especially in India and Asia where aerosol prediction is a challenge. Assimilating AERONET AOT with MODIS had little impact on the 24-hour forecast skill compared to MODIS assimilation only, but differences were found downwind of AERONET sites. The 24-hour forecast skill was approximately the same for forecasts initialized with analyses from AERONET AOT assimilation alone compared to MODIS assimilation, particularly in regions where the AERONET network is dense; including the United States and Europe, indicating AERONET could serve as a backup observation network for over-land synoptic scale aerosol events.
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goci yonsei aerosol retrieval yaer algorithm and validation during the dragon ne asia 2012 campaign
Atmospheric Measurement Techniques, 2016Co-Authors: Myungje Choi, Young Je Park, Jhoon Kim, Jaehwa Lee, Mijin Kim, Ukkyo Jeong, Woogyung Kim, Hyunkee Hong, B N HolbenAbstract:Abstract. The Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorological Satellite (COMS) is the first multi-channel ocean color imager in geostationary orbit. Hourly GOCI top-of-atmosphere radiance has been available for the retrieval of aerosol optical properties over East Asia since March 2011. This study presents improvements made to the GOCI Yonsei Aerosol Retrieval (YAER) algorithm together with validation results during the Distributed Regional Aerosol Gridded Observation Networks – Northeast Asia 2012 campaign (DRAGON-NE Asia 2012 campaign). The evaluation during the spring season over East Asia is important because of high aerosol concentrations and diverse types of Asian dust and haze. Optical properties of aerosol are retrieved from the GOCI YAER algorithm including aerosol optical depth (AOD) at 550 nm, fine-mode fraction (FMF) at 550 nm, single-scattering albedo (SSA) at 440 nm, Angstrom exponent (AE) between 440 and 860 nm, and aerosol type. The aerosol models are created based on a global analysis of the Aerosol Robotic Networks (AERONET) inversion data, and covers a broad range of size distribution and absorptivity, including nonspherical dust properties. The Cox–Munk ocean bidirectional reflectance distribution function (BRDF) model is used over ocean, and an improved minimum reflectance technique is used over land. Because turbid water is persistent over the Yellow Sea, the land algorithm is used for such cases. The aerosol products are evaluated against AERONET observations and MODIS Collection 6 aerosol products retrieved from Dark Target (DT) and Deep Blue (DB) algorithms during the DRAGON-NE Asia 2012 campaign conducted from March to May 2012. Comparison of AOD from GOCI and AERONET resulted in a Pearson correlation coefficient of 0.881 and a linear regression equation with GOCI AOD = 1.083 × AERONET AOD − 0.042. The correlation between GOCI and MODIS AODs is higher over ocean than land. GOCI AOD shows better agreement with MODIS DB than MODIS DT. The other GOCI YAER products (AE, FMF, and SSA) show lower correlation with AERONET than AOD, but still show some skills for qualitative use.
T F Eck - 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, J S Schafer, Ilya Slutsker, Alexander Sinyuk, A. Smirnov, T F Eck, B N Holben, 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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intercomparison of aerosol single scattering albedo derived from AERONET surface radiometers and large in situ aircraft profiles during the 2011 dragon md and discover aq experiments
Journal of Geophysical Research, 2014Co-Authors: J S Schafer, D M Giles, Alexander Sinyuk, T F Eck, B N Holben, K L Thornhill, B E Anderson, E L Winstead, Luke D Ziemba, A J BeyersdorfAbstract:Single-scattering albedo (SSA) retrievals obtained with CIMEL Sun-sky radiometers from the Aerosol Robotic Network (AERONET) aerosol monitoring network were used to make comparisons with simultaneous in situ sampling from aircraft profiles carried out by the NASA Langley Aerosol Group Experiment (LARGE) team in the summer of 2011 during the coincident DRAGON-MD (Distributed Regional Aerosol Gridded Observational Network-Maryland) and DISCOVER-AQ (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality) experiments. The single-scattering albedos (interpolated to 550 nm) derived from AERONET measurements for aerosol optical depth (AOD) at 440 nm ≥ 0.4 (mean SSA: 0.979) were on average 0.011 lower than the values derived from the LARGE profile measurements (mean SSA: 0.99). The maximum difference observed was 0.023 with all the observed differences within the combined uncertainty for the stated SSA accuracy (0.03 for AERONET; 0.02 for LARGE). Single-scattering albedo averages were also analyzed for lower aerosol loading conditions (AOD ≥ 0.2) and a dependence on aerosol optical depth was noted with significantly lower single-scattering albedos observed for lower AOD in both AERONET and LARGE data sets. Various explanations for the SSA trend were explored based on other retrieval products including volume median radius and imaginary refractive index as well as column water vapor measurements. Additionally, these SSA trends with AOD were evaluated for one of the DRAGON-MD study sites, Goddard Space Flight Center, and two other Mid-Atlantic AERONET sites over the long-term record dating to 1999.
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Verification and application of the extended spectral deconvolution algorithm (SDA+) methodology to estimate aerosol fine and coarse mode extinction coefficients in the marine boundary layer
Copernicus Publications, 2014Co-Authors: K. C. Kaku, J. S. Reid, N. T. O'neill, P. K. Quinn, D. J. Coffman, T F EckAbstract:The spectral deconvolution algorithm (SDA) and SDA+ (extended SDA) methodologies can be employed to separate the fine and coarse mode extinction coefficients from measured total aerosol extinction coefficients, but their common use is currently limited to AERONET (AErosol RObotic NETwork) aerosol optical depth (AOD). Here we provide the verification of the SDA+ methodology on a non-AERONET aerosol product, by applying it to fine and coarse mode nephelometer and particle soot absorption photometer (PSAP) data sets collected in the marine boundary layer. Using data sets collected on research vessels by NOAA-PMEL(National Oceanic and Atmospheric Administration – Pacific Marine Environmental Laboratory), we demonstrate that with accurate input, SDA+ is able to predict the fine and coarse mode scattering and extinction coefficient partition in global data sets representing a range of aerosol regimes. However, in low-extinction regimes commonly found in the clean marine boundary layer, SDA+ output accuracy is sensitive to instrumental calibration errors. This work was extended to the calculation of coarse and fine mode scattering coefficients with similar success. This effort not only verifies the application of the SDA+ method to in situ data, but by inference verifies the method as a whole for a host of applications, including AERONET. Study results open the door to much more extensive use of nephelometers and PSAPs, with the ability to calculate fine and coarse mode scattering and extinction coefficients in field campaigns that do not have the resources to explicitly measure these values
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analysis of the weekly cycle of aerosol optical depth using AERONET and modis data
Journal of Geophysical Research, 2008Co-Authors: Xiangao Xia, T F Eck, B N Holben, Goloub Phillippe, Hongbin ChenAbstract:[1] Multi-year Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) data are used to study AOD weekly variations at the global scale. A clear weekly cycle of AOD is observed in the United States (U.S.) and Central Europe. AOD during the weekday is larger than that during the weekend in 36 out of 43 AERONET sites in the U.S. The average U.S. weekend effect (the percent difference in AOD during the weekday and the weekend) is 3.8%. A weekly periodicity with lower AODs on Sunday and Monday and higher AODs from Wednesday until Saturday is revealed over Central Europe and the average weekend effect there is 4.0%. The weekly cycle in urban sites is greater than that in rural sites. AOD during the weekday is also significantly larger than that during the weekend in urban AERONET sites in South America and South Korea. However, a reversed AOD weekly cycle is observed in the Middle East and India. AODs on Thursday and Friday, the “weekend” for Middle East cultures, are relatively lower than AODs on other days. There is no clear weekly variation of AOD over eastern China. The striking feature in this region is the occurrence of much higher AOD on Sunday and this phenomenon is independent of season. The analysis of MODIS aerosol data is in good agreement with that of AERONET data.
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a synthesis of single scattering albedo of biomass burning aerosol over southern africa during safari 2000
Geophysical Research Letters, 2007Co-Authors: L V Leahy, T F Eck, Theodore L Anderson, R W BergstromAbstract:[1] We present a synthesis of single scattering albedo for biomass burning aerosol from the SAFARI 2000 field campaign. Values at 550 nm were derived from three methods: airborne in situ measurements of aerosol scattering and absorption; airborne flux radiometry; and ground-based sun-photometer/radiometer retrievals from AERONET. Collocated comparisons indicate that uncertainties are well understood for all three methods. The new (Version 2) AERONET retrieval gives substantially lower single scattering albedo over bright surfaces, and the comparisons herein represent the first independent check of this retrieval. Combined in situ and AERONET data yield a regional value of 0.85 ± 0.02 (mean and total uncertainty), which we propose is representative of single scattering albedo for the Southern African region during the biomass burning season. This value agrees with the “highly absorbing smoke” model used in MODIS aerosol retrievals, but indicates that many of the AeroCom models overestimate single scattering albedo for this region and season.
Oleg Dubovik - One of the best experts on this subject based on the ideXlab platform.
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a correlation based inversion method for aerosol property cimap retrieval from AERONET measurements
Journal of Quantitative Spectroscopy & Radiative Transfer, 2021Co-Authors: Taozhong Huang, Lan Gao, Connor J Flynn, Oleg DubovikAbstract:Abstract We developed a principal-component based inversion approach – CIMAP, to retrieve aerosol properties from AERONET measurements of aerosol optical depths (AODs) and sky radiances. Capitalizing on the correlation in aerosol properties, CIMAP reduces the aerosol retrieval parameter space and saves retrieval time by more than 80% from adopting 7-8 principal components in the retrieval process, compared to retrievals in regular parameter space. As algorithm test, CIMAP is implemented to retrieve 60 sets of AERONET observations in several selected areas in southern California, southern Africa, and north China around Beijing, and the results are compared to the AERONET measurement and operational retrieval products. The mean absolute differences are found to be 0.004, 0.019, 0.032, 0.039, 0.003 and 0.044µm for AOD, single scattering albedo, Angstrom exponent, real and imaginary parts of refractive index and effective radius of aerosols, respectively. These differences are within or close to AERONET measurement or retrieval uncertainties.
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advanced characterisation of aerosol size properties from measurements of spectral optical depth using the grasp algorithm
Atmospheric Measurement Techniques, 2016Co-Authors: Benjamin Torres, Oleg Dubovik, Philippe Goloub, Gregory L Schuster, Tatyana Lapyonok, David Fuertes, V E Cachorro, Luc Blarel, Africa Barreto, Marc MalletAbstract:Abstract. This study evaluates the potential of using aerosol optical depth (τa) measurements to characterise the microphysical and optical properties of atmospheric aerosols. With this aim, we used the recently developed GRASP (Generalized Retrieval of Aerosol and Surface Properties) code for numerical testing of six different aerosol models with different aerosol loads. The direct numerical simulations (self-consistency tests) indicate that the GRASP-AOD retrieval provides modal aerosol optical depths (fine and coarse) to within 0.01 of the input values. The retrieval of the fine-mode radius, width and volume concentration are stable and precise if the real part of the refractive index is known. The coarse-mode properties are less accurate, but they are significantly improved when additional a priori information is available. The tests with random simulated errors show that the uncertainty in the bimodal log-normal size distribution parameters increases as the aerosol load decreases. Similarly, the reduction in the spectral range diminishes the stability of the retrieved parameters. In addition to these numerical studies, we used optical depth observations at eight AERONET locations to validate our results with the standard AERONET inversion products. We found that bimodal log-normal size distributions serve as useful input assumptions, especially when the measurements have inadequate spectral coverage and/or limited accuracy, such as moon photometry. Comparisons of the mode median radii between GRASP-AOD and AERONET indicate average differences of 0.013 µm for the fine mode and typical values of 0.2–0.3 µm for the coarse mode. The dominant mode (i.e. fine or coarse) indicates a 10 % difference in mode radii between the GRASP-AOD and AERONET inversions, and the average of the difference in volume concentration is around 17 % for both modes. The retrieved values of the fine-mode τa(500) using GRASP-AOD are generally between those values obtained by the standard AERONET inversion and the values obtained by the AERONET spectral deconvolution algorithm (SDA), with differences typically lower than 0.02 between GRASP-AOD and both algorithms. Finally, we present some examples of application of GRASP-AOD inversion using moon photometry and the airborne PLASMA sun photometer during the ChArMEx summer 2013 campaign in the western Mediterranean.
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retrieval of aerosol microphysical properties from AERONET photo polarimetric measurements 2 a new research algorithm and case demonstration
Journal of Geophysical Research, 2015Co-Authors: Jun Wang, Oleg Dubovik, Jing Zeng, Robert Spurr, Xiong Liu, Michael I Mishchenko, Aliaksandr Siniuk, B N HolbenAbstract:A new research algorithm is presented here as the second part of a two-part study to retrieve aerosol microphysical properties from the multispectral and multiangular photopolarimetric measurements taken by Aerosol Robotic Network's (AERONET's) new-generation Sun photometer. The algorithm uses an advanced UNified and Linearized Vector Radiative Transfer Model and incorporates a statistical optimization approach. While the new algorithm has heritage from AERONET operational inversion algorithm in constraining a priori and retrieval smoothness, it has two new features. First, the new algorithm retrieves the effective radius, effective variance, and total volume of aerosols associated with a continuous bimodal particle size distribution (PSD) function, while the AERONET operational algorithm retrieves aerosol volume over 22 size bins. Second, our algorithm retrieves complex refractive indices for both fine and coarse modes, while the AERONET operational algorithm assumes a size-independent aerosol refractive index. Mode-resolved refractive indices can improve the estimate of the single-scattering albedo (SSA) for each aerosol mode and thus facilitate the validation of satellite products and chemistry transport models. We applied the algorithm to a suite of real cases over Beijing_RADI site and found that our retrievals are overall consistent with AERONET operational inversions but can offer mode-resolved refractive index and SSA with acceptable accuracy for the aerosol composed by spherical particles. Along with the retrieval using both radiance and polarization, we also performed radiance-only retrieval to demonstrate the improvements by adding polarization in the inversion. Contrast analysis indicates that with polarization, retrieval error can be reduced by over 50% in PSD parameters, 10–30% in the refractive index, and 10–40% in SSA, which is consistent with theoretical analysis presented in the companion paper of this two-part study.
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vertical profiles of pure dust and mixed smoke dust plumes inferred from inversion of multiwavelength raman polarization lidar data and comparison to AERONET retrievals and in situ observations
Applied Optics, 2013Co-Authors: Detlef Muller, Albert Ansmann, Matthias Tesche, Igor Veselovskii, Alexei Kolgotin, Oleg DubovikAbstract:We present for the first time vertical profiles of microphysical properties of pure mineral dust (largely unaffected by any other aerosol types) on the basis of the inversion of optical data collected with multiwavelength polarization Raman lidar. The data were taken during the Saharan Mineral Dust Experiment (SAMUM) in Morocco in 2006. We also investigated two cases of mixed dust–smoke plumes on the basis of data collected during the second SAMUM field campaign that took place in the Republic of Cape Verde in 2008. Following the experience of the Aerosol Robotic Network (AERONET), the dust is modeled as a mixture of spherical particles and randomly oriented spheroids. The retrieval is performed from the full set of lidar input data (three backscatter coefficients, two extinction coefficients, and one depolarization ratio) and from a reduced set of data in which we exclude the depolarization ratio. We find differences of the microphysical properties depending on what kind of optical data combination we use. For the case of pure mineral dust, the results from these two sets of optical data are consistent and confirm the validity of the spheroid particle model for data inversion. Our results indicate that in the case of pure mineral dust we do not need depolarization information in the inversion. For the mixture of dust and biomass burning, there seem to be more limitations in the retrieval accuracy of the various data products. The evaluation of the quality of our data products is done by comparing our lidar-derived data products (vertically resolved) to results from AERONET Sun photometer observations (column-averaged) carried out at the lidar field site. Our results for dust effective radius show agreement with the AERONET observations within the retrieval uncertainties. Regarding the complex refractive index a comparison is difficult, as AERONET provides this parameter as wavelength-dependent quantity. In contrast, our inversion algorithm provides this parameter as a wavelength-independent quantity. We also show some comparison to results from airborne in situobservation. A detailed comparison to in situ results will be left for a future contribution.
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comparison of calipso aerosol optical depth retrievals to AERONET measurements and a climatology for the lidar ratio of dust
Atmospheric Chemistry and Physics, 2012Co-Authors: Gregory L Schuster, Mark A Vaughan, Oleg Dubovik, D. M. Winker, David G. Macdonnell, Tatyana Lapyonok, Charles R. TrepteAbstract:Abstract. We compared CALIPSO column aerosol optical depths at 0.532 μm to measurements at 147 AERONET sites, synchronized to within 30 min of satellite overpass times during a 3-yr period. We found 677 suitable overpasses, and a CALIPSO bias of −13% relative to AERONET for the entire data set; the corresponding absolute bias is −0.029, and the standard deviation of the mean (SDOM) is 0.014. Consequently, the null hypothesis is rejected at the 97% confidence level, indicating a statistically significant difference between the datasets. However, if we omit CALIPSO columns that contain dust from our analysis, the relative and absolute biases are reduced to −3% and −0.005 with a standard error of 0.016 for 449 overpasses, and the statistical confidence level for the null hypothesis rejection is reduced to 27%. We also analyzed the results according to the six CALIPSO aerosol subtypes and found relative and absolute biases of −29% and −0.1 for atmospheric columns that contain the dust subtype exclusively, but with a relatively high correlation coefficient of R = 0.58; this indicates the possibility that the assumed lidar ratio (40 sr) for the CALIPSO dust retrievals is too low. Hence, we used the AERONET size distributions, refractive indices, percent spheres, and forward optics code for spheres and spheroids to compute a lidar ratio climatology for AERONET sites located in the dust belt. The highest lidar ratios of our analysis occur in the non-Sahel regions of Northern Africa, where the median lidar ratio at 0.532 μm is 55.4 sr for 229 retrievals. Lidar ratios are somewhat lower in the African Sahel (49.7 sr for 929 retrievals), the Middle East (42.6 sr for 489 retrievals), and Kanpur, India (43.8 sr for 67 retrievals). We attribute this regional variability in the lidar ratio to the regional variability of the real refractive index of dust, as these two parameters are highly anti-correlated (correlation coefficients range from −0.51 to −0.85 for the various regions). The AERONET refractive index variability is consistent with the variability of illite concentration in dust across the dust belt.
Lorraine A Remer - One of the best experts on this subject based on the ideXlab platform.
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validation of modis 3 km land aerosol optical depth from nasa s eos terra and aqua missions
Atmospheric Measurement Techniques, 2018Co-Authors: Lorraine A Remer, Robert C Levy, Pawan Gupta, Shana MattooAbstract:Abstract. In addition to the standard resolution product (10 km), the MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6 (C006) data release included a higher resolution (3 km). Other than accommodations for the two different resolutions, the 10 and 3 km Dark Target (DT) algorithms are basically the same. In this study, we perform global validation of the higher-resolution aerosol optical depth (AOD) over global land by comparing against AErosol RObotic NETwork (AERONET) measurements. The MODIS–AERONET collocated data sets consist of 161 410 high-confidence AOD pairs from 2000 to 2015 for Terra MODIS and 2003 to 2015 for Aqua MODIS. We find that 62.5 and 68.4 % of AODs retrieved from Terra MODIS and Aqua MODIS, respectively, fall within previously published expected error bounds of ± (0.05 + 0.2 × AOD), with a high correlation ( R= 0.87). The scatter is not random, but exhibits a mean positive bias of ∼ 0.06 for Terra and ∼ 0.03 for Aqua. These biases for the 3 km product are approximately 0.03 larger than the biases found in similar validations of the 10 km product. The validation results for the 3 km product did not have a relationship to aerosol loading (i.e., true AOD), but did exhibit dependence on quality flags, region, viewing geometry, and aerosol spatial variability. Time series of global MODIS–AERONET differences show that validation is not static, but has changed over the course of both sensors' lifetimes, with Terra MODIS showing more change over time. The likely cause of the change of validation over time is sensor degradation, but changes in the distribution of AERONET stations and differences in the global aerosol system itself could be contributing to the temporal variability of validation.
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multiangle implementation of atmospheric correction maiac 2 aerosol algorithm
Journal of Geophysical Research, 2011Co-Authors: Ralph A Kahn, Lorraine A Remer, Alexei Lyapustin, Yujie Wang, Istvan Laszlo, Sergey Korkin, Robert C LevyAbstract:[1] An aerosol component of a new multiangle implementation of atmospheric correction (MAIAC) algorithm is presented. MAIAC is a generic algorithm developed for the Moderate Resolution Imaging Spectroradiometer (MODIS), which performs aerosol retrievals and atmospheric correction over both dark vegetated surfaces and bright deserts based on a time series analysis and image-based processing. The MAIAC look-up tables explicitly include surface bidirectional reflectance. The aerosol algorithm derives the spectral regression coefficient (SRC) relating surface bidirectional reflectance in the blue (0.47 μm) and shortwave infrared (2.1 μm) bands; this quantity is prescribed in the MODIS operational Dark Target algorithm based on a parameterized formula. The MAIAC aerosol products include aerosol optical thickness and a fine-mode fraction at resolution of 1 km. This high resolution, required in many applications such as air quality, brings new information about aerosol sources and, potentially, their strength. AERONET validation shows that the MAIAC and MOD04 algorithms have similar accuracy over dark and vegetated surfaces and that MAIAC generally improves accuracy over brighter surfaces due to the SRC retrieval and explicit bidirectional reflectance factor characterization, as demonstrated for several U.S. West Coast AERONET sites. Due to its generic nature and developed angular correction, MAIAC performs aerosol retrievals over bright deserts, as demonstrated for the Solar Village Aerosol Robotic Network (AERONET) site in Saudi Arabia.
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global evaluation of the collection 5 modis dark target aerosol products over land
Atmospheric Chemistry and Physics, 2010Co-Authors: Robert C Levy, Richard G Kleidman, Lorraine A Remer, Charles Ichoku, Shana Mattoo, Ralph A KahnAbstract:Abstract. NASA's MODIS sensors have been observing the Earth from polar orbit, from Terra since early 2000 and from Aqua since mid 2002. We have applied a consistent retrieval and processing algorithm to both sensors to derive the Collection 5 (C005) dark-target aerosol products over land. Here, we validate the MODIS along-orbit Level 2 products by comparing to quality assured Level 2 AERONET sunphotometer measurements at over 300 sites. From 85 463 collocations, representing mutually cloud-free conditions, we find that >66% (one standard deviation) of MODIS-retrieved aerosol optical depth (AOD) values compare to AERONET-observed values within an expected error (EE) envelope of ±(0.05 + 15%), with high correlation (R = 0.9). Thus, the MODIS AOD product is validated and quantitative. However, even though we can define EEs for MODIS-reported Angstrom exponent and fine AOD over land, these products do not have similar physical validity. Although validated globally, MODIS-retrieved AOD does not fall within the EE envelope everywhere. We characterize some of the residual biases that are related to specific aerosol conditions, observation geometry, and/or surface properties, and relate them to situations where particular MODIS algorithm assumptions are violated. Both Terra's and Aqua's–retrieved AOD are similarly comparable to AERONET, however, Terra's global AOD bias changes with time, overestimating (by ~0.005) before 2004, and underestimating by similar magnitude after. This suggests how small calibration uncertainties of
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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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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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advances in the ocean color component of the aerosol robotic network AERONET oc
Journal of Atmospheric and Oceanic Technology, 2021Co-Authors: Giuseppe Zibordi, D M Giles, Ilya Slutsker, B N Holben, Davide Dalimonte, Marco Talone, M G SorokinAbstract:22 pages, 14 figures, 5 tables, 2 appendixes.-- Data availability statement: AERONET-OC data are available from NASA (https://AERONET.gsfc.nasa.gov/new_web/ocean_color.html)
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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, J S Schafer, Ilya Slutsker, Alexander Sinyuk, A. Smirnov, T F Eck, B N Holben, 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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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
'Copernicus GmbH', 2019Co-Authors: D M Giles, M G Sorokin, J S Schafer, Alexander Sinyuk, A. SmirnovAbstract: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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intercomparison of aerosol single scattering albedo derived from AERONET surface radiometers and large in situ aircraft profiles during the 2011 dragon md and discover aq experiments
Journal of Geophysical Research, 2014Co-Authors: J S Schafer, D M Giles, Alexander Sinyuk, T F Eck, B N Holben, K L Thornhill, B E Anderson, E L Winstead, Luke D Ziemba, A J BeyersdorfAbstract:Single-scattering albedo (SSA) retrievals obtained with CIMEL Sun-sky radiometers from the Aerosol Robotic Network (AERONET) aerosol monitoring network were used to make comparisons with simultaneous in situ sampling from aircraft profiles carried out by the NASA Langley Aerosol Group Experiment (LARGE) team in the summer of 2011 during the coincident DRAGON-MD (Distributed Regional Aerosol Gridded Observational Network-Maryland) and DISCOVER-AQ (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality) experiments. The single-scattering albedos (interpolated to 550 nm) derived from AERONET measurements for aerosol optical depth (AOD) at 440 nm ≥ 0.4 (mean SSA: 0.979) were on average 0.011 lower than the values derived from the LARGE profile measurements (mean SSA: 0.99). The maximum difference observed was 0.023 with all the observed differences within the combined uncertainty for the stated SSA accuracy (0.03 for AERONET; 0.02 for LARGE). Single-scattering albedo averages were also analyzed for lower aerosol loading conditions (AOD ≥ 0.2) and a dependence on aerosol optical depth was noted with significantly lower single-scattering albedos observed for lower AOD in both AERONET and LARGE data sets. Various explanations for the SSA trend were explored based on other retrieval products including volume median radius and imaginary refractive index as well as column water vapor measurements. Additionally, these SSA trends with AOD were evaluated for one of the DRAGON-MD study sites, Goddard Space Flight Center, and two other Mid-Atlantic AERONET sites over the long-term record dating to 1999.
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assessment of error in aerosol optical depth measured by AERONET due to aerosol forward scattering
Geophysical Research Letters, 2012Co-Authors: Alexander Sinyuk, D M Giles, J S Schafer, Ilya Slutsker, A. Smirnov, Brent N. Holben, M G SorokinAbstract:[1] We present an analysis of the effect of aerosol forward scattering on the accuracy of aerosol optical depth (AOD) measured by CIMEL Sun photometers. The effect is quantified in terms of AOD and solar zenith angle using radiative transfer modeling. The analysis is based on aerosol size distributions derived from multi-year climatologies of AERONET aerosol retrievals. The study shows that the modeled error is lower than AOD calibration uncertainty (0.01) for the vast majority of AERONET level 2 observations, ∼99.53%. Only ∼0.47% of the AERONET database corresponding mostly to dust aerosol with high AOD and low solar elevations has larger biases. We also show that observations with extreme reductions in direct solar irradiance do not contribute to level 2 AOD due to low Sun photometer digital counts below a quality control cutoff threshold.