scholarly journals A Short Note on the Potential of Utilization of Spectral AERONET-Derived Depolarization Ratios for Aerosol Classification

Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 143 ◽  
Author(s):  
Il-Sung Zo ◽  
Sung-Kyun Shin

We herein present the spectral linear particle depolarization ratios (δp) from an Aerosol Robotics NETwork (AERONET) sun/sky radiometer with respect to the aerosol type. AERONET observation sites, which are representative of each aerosol type, were selected for our study. The observation data were filtered using the Ångström exponent (Å), fine-mode fraction (FMF) and single scattering albedo (ω) to ensure that the obtained values of δp were representative of each aerosol condition. We report the spectral δp values provided in the recently released AERONET version 3 inversion product for observation of the following aerosol types: dust, polluted dust, smoke, non-absorbing, moderately-absorbing and high-absorbing pollution. The AERONET-derived δp values were generally within the range of the δp values measured from lidar observations for each aerosol type. In addition, it was found that the spectral variation of δp differed according to the aerosol type. From the obtained results, we concluded that our findings provide potential insight into the identification and classification of aerosol types using remote sensing techniques.

2021 ◽  
Vol 13 (7) ◽  
pp. 1268
Author(s):  
Wonei Choi ◽  
Hanlim Lee ◽  
Daewon Kim ◽  
Serin Kim

The spatial coverage of satellite aerosol classification was improved using a random forest (RF) model trained with observational data including target (aerosol type) and input (satellite measurement) variables. The AErosol RObotic NETwork (AERONET) aerosol-type dataset was used for the target variables. Satellite input variables with many missing data or low mean-decrease accuracy were excluded from the final input variable set, and good performance in aerosol-type classification was achieved. The performance of the RF-based model was evaluated on the basis of the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical SSA wavelength dependence for individual aerosol types was consistent with that obtained for aerosol types by the RF-based model. The spatial coverage of the RF-based model was also compared with that of previously developed models in a global-scale case study. The study demonstrates that the RF-based model allows satellite aerosol classification with improved spatial coverage, with a performance similar to that of previously developed models.


2013 ◽  
Vol 6 (1) ◽  
pp. 1815-1858 ◽  
Author(s):  
S. P. Burton ◽  
R. A. Ferrare ◽  
M. A. Vaughan ◽  
A. H. Omar ◽  
R. R. Rogers ◽  
...  

Abstract. Aerosol classification products from the NASA Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL-1) on the NASA B200 aircraft are compared with coincident V3.01 aerosol classification products from the CALIOP instrument on the CALIPSO satellite. For CALIOP, aerosol classification is a key input to the aerosol retrieval, and must be inferred using aerosol loading-dependent observations and location information. In contrast, HSRL-1 makes direct measurements of aerosol intensive properties, including the lidar ratio, that provide information on aerosol type. In this study, comparisons are made for 109 underflights of the CALIOP orbit track. We find that 62% of the CALIOP marine layers and 54% of the polluted continental layers agree with HSRL-1 classification results. In addition, 80% of the CALIOP desert dust layers are classified as either dust or dusty mix by HSRL-1. However, agreement is less for CALIOP smoke (13%) and polluted dust (35%) layers. Specific case studies are examined, giving insight into the performance of the CALIOP aerosol type algorithm. In particular, we find that the CALIOP polluted dust type is overused due to an attenuation-related depolarization bias. Furthermore, the polluted dust type frequently includes mixtures of dust plus marine aerosol. Finally, we find that CALIOP's identification of internal boundaries between different aerosol types in contact with each other frequently do not reflect the actual transitions between aerosol types accurately. Based on these findings, we give recommendations which may help to improve the CALIOP aerosol type algorithms.


2012 ◽  
Vol 5 (1) ◽  
pp. 73-98 ◽  
Author(s):  
S. P. Burton ◽  
R. A. Ferrare ◽  
C. A. Hostetler ◽  
J. W. Hair ◽  
R. R. Rogers ◽  
...  

Abstract. The NASA Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL) on the NASA B200 aircraft has acquired extensive datasets of aerosol extinction (532 nm), aerosol optical depth (AOD) (532 nm), backscatter (532 and 1064 nm), and depolarization (532 and 1064 nm) profiles during 18 field missions that have been conducted over North America since 2006. The lidar measurements of aerosol intensive parameters (lidar ratio, depolarization, backscatter color ratio, and spectral depolarization ratio) are shown to vary with location and aerosol type. A methodology based on observations of known aerosol types is used to qualitatively classify the extensive set of HSRL aerosol measurements into eight separate types. Several examples are presented showing how the aerosol intensive parameters vary with aerosol type and how these aerosols are classified according to this new methodology. The HSRL-based classification reveals vertical variability of aerosol types during the NASA ARCTAS field experiment conducted over Alaska and northwest Canada during 2008. In two examples derived from flights conducted during ARCTAS, the HSRL classification of biomass burning smoke is shown to be consistent with aerosol types derived from coincident airborne in situ measurements of particle size and composition. The HSRL retrievals of AOD and inferences of aerosol types are used to apportion AOD to aerosol type; results of this analysis are shown for several experiments.


2015 ◽  
Vol 15 (18) ◽  
pp. 10597-10618 ◽  
Author(s):  
M. J. M. Penning de Vries ◽  
S. Beirle ◽  
C. Hörmann ◽  
J. W. Kaiser ◽  
P. Stammes ◽  
...  

Abstract. Detecting the optical properties of aerosols using passive satellite-borne measurements alone is a difficult task due to the broadband effect of aerosols on the measured spectra and the influences of surface and cloud reflection. We present another approach to determine aerosol type, namely by studying the relationship of aerosol optical depth (AOD) with trace gas abundance, aerosol absorption, and mean aerosol size. Our new Global Aerosol Classification Algorithm, GACA, examines relationships between aerosol properties (AOD and extinction Ångström exponent from the Moderate Resolution Imaging Spectroradiometer (MODIS), UV Aerosol Index from the second Global Ozone Monitoring Experiment, GOME-2) and trace gas column densities (NO2, HCHO, SO2 from GOME-2, and CO from MOPITT, the Measurements of Pollution in the Troposphere instrument) on a monthly mean basis. First, aerosol types are separated based on size (Ångström exponent) and absorption (UV Aerosol Index), then the dominating sources are identified based on mean trace gas columns and their correlation with AOD. In this way, global maps of dominant aerosol type and main source type are constructed for each season and compared with maps of aerosol composition from the global MACC (Monitoring Atmospheric Composition and Climate) model. Although GACA cannot correctly characterize transported or mixed aerosols, GACA and MACC show good agreement regarding the global seasonal cycle, particularly for urban/industrial aerosols. The seasonal cycles of both aerosol type and source are also studied in more detail for selected 5° × 5° regions. Again, good agreement between GACA and MACC is found for all regions, but some systematic differences become apparent: the variability of aerosol composition (yearly and/or seasonal) is often not well captured by MACC, the amount of mineral dust outside of the dust belt appears to be overestimated, and the abundance of secondary organic aerosols is underestimated in comparison with GACA. Whereas the presented study is of exploratory nature, we show that the developed algorithm is well suited to evaluate climate and atmospheric composition models by including aerosol type and source obtained from measurements into the comparison, instead of focusing on a single parameter, e.g., AOD. The approach could be adapted to constrain the mix of aerosol types during the process of a combined data assimilation of aerosol and trace gas observations.


2017 ◽  
Vol 17 (10) ◽  
pp. 6271-6290 ◽  
Author(s):  
Youngmin Noh ◽  
Detlef Müller ◽  
Kyunghwa Lee ◽  
Kwanchul Kim ◽  
Kwonho Lee ◽  
...  

Abstract. The linear particle depolarization ratios at 440, 675, 870, and 1020 nm were derived using data taken with the AERONET sun–sky radiometer at Seoul (37.45° N, 126.95° E), Kongju (36.47° N, 127.14° E), Gosan (33.29° N, 126.16° E), and Osaka (34.65° N, 135.59° E). The results are compared to the linear particle depolarization ratio measured by lidar at 532 nm. The correlation coefficient R2 between the linear particle depolarization ratio derived by AERONET data at 1020 nm and the linear particle depolarization ratio measured with lidar at 532 nm is 0.90, 0.92, 0.79, and 0.89 at Seoul, Kongju, Gosan, and Osaka, respectively. The correlation coefficients between the lidar-measured depolarization ratio at 532 nm and that retrieved by AERONET at 870 nm are 0.89, 0.92, 0.76, and 0.88 at Seoul, Kongju, Gosan, and Osaka, respectively. The correlation coefficients for the data taken at 675 nm are lower than the correlation coefficients at 870 and 1020 nm, respectively. Values are 0.81, 0.90, 0.64, and 0.81 at Seoul, Kongju, Gosan, and Osaka, respectively. The lowest correlation values are found for the AERONET-derived linear particle depolarization ratio at 440 nm, i.e., 0.38, 0.62, 0.26, and 0.28 at Seoul, Kongju, Gosan, and Osaka, respectively. We should expect a higher correlation between lidar-measured linear particle depolarization ratios at 532 nm and the ones derived from AERONET at 675 and 440 nm as the lidar wavelength is between the two AERONET wavelengths. We cannot currently explain why we find better correlation between lidar and AERONET linear particle depolarization ratios for the case that the AERONET wavelengths (675, 870, and 1020 nm) are significantly larger than the lidar measurement wavelength (532 nm). The linear particle depolarization ratio can be used as a parameter to obtain insight into the variation of optical and microphysical properties of dust when it is mixed with anthropogenic pollution particles. The single-scattering albedo increases with increasing measurement wavelength for low linear particle depolarization ratios, which indicates a high share of fine-mode anthropogenic pollution. In contrast, single-scattering albedo increases with increasing wavelength for high linear particle depolarization ratios, which indicated a high share of coarse-mode mineral dust particles. The retrieved volume particle size distributions are dominated by the fine-mode fraction if linear particle depolarization ratios are less than 0.15 at 532 nm. The fine-mode fraction of the size distributions decreases and the coarse-mode fraction of the size distribution increases for increasing linear particle depolarization ratio at 1020 nm. The dust ratio based on using the linear particle depolarization ratio derived from AERONET data is 0.12 to 0.17. These values are lower than the coarse-mode fraction derived from the volume concentrations of particle size distributions, in which case we can compute the coarse-mode fraction of dust.


2019 ◽  
Vol 12 (7) ◽  
pp. 3789-3803 ◽  
Author(s):  
Sung-Kyun Shin ◽  
Matthias Tesche ◽  
Youngmin Noh ◽  
Detlef Müller

Abstract. This study proposes an aerosol-type classification based on the particle linear depolarization ratio (PLDR) and single-scattering albedo (SSA) provided in the AErosol RObotic NETwork (AERONET) version 3 level 2.0 inversion product. We compare our aerosol-type classification with an earlier method that uses fine-mode fraction (FMF) and SSA. Our new method allows for a refined classification of mineral dust that occurs as a mixture with other absorbing aerosols: pure dust (PD), dust-dominated mixed plume (DDM), and pollutant-dominated mixed plume (PDM). We test the aerosol classification at AERONET sites in East Asia that are frequently affected by mixtures of Asian dust and biomass-burning smoke or anthropogenic pollution. We find that East Asia is strongly affected by pollution particles with high occurrence frequencies of 50 % to 67 %. The distribution and types of pollution particles vary with location and season. The frequency of PD and dusty aerosol mixture (DDM+PDM) is slightly lower (34 % to 49 %) than pollution-dominated mixtures. Pure dust particles have been detected in only 1 % of observations. This suggests that East Asian dust plumes generally exist in a mixture with pollution aerosols rather than in pure form. In this study, we have also considered data from selected AERONET sites that are representative of anthropogenic pollution, biomass-burning smoke, and mineral dust. We find that average aerosol properties obtained for aerosol types in our PLDR–SSA-based classification agree reasonably well with those obtained at AERONET sites representative for different aerosol types.


Author(s):  
Tang-Huang Lin ◽  
Gin-Rong Liu ◽  
Chian-Yi Liu

In general, the type of atmospheric aerosols can be efficiently identified with the characteristics of optical properties, such as Ångström exponent (AE) and single scattering albedo (SSA). However, the retrieval of SSA is not frequently available to global area which may cause the difficulty in the identification of aerosol type. Since aerosol optical depth (AOD) can be easily requested, a novel index in terms of AOD, Normalized Gradient Aerosol Index (NGAI), is proposed to get over the constraint on SSA providing. With the NGAI derived from MODIS AOD products, the type of atmospheric aerosols can be clearly categorized between mineral dusts, biomass burning and anthropogenic pollutants. The results of aerosol type categorization show the well agreement with the ground-based observations (AERONET) in AE and SSA properties, implying that the proposed index equips highly practical for the application of aerosols type categorization by means of remote sensing. In addition, the fraction of AOD compositions can be potentially determined according to the value of index after compared with the products of CALIPSO Aerosol Subtype.


2013 ◽  
Vol 13 (10) ◽  
pp. 26627-26656 ◽  
Author(s):  
Y. Choi ◽  
Y. S. Ghim ◽  
B. N. Holben

Abstract. Dominant aerosols were distinguished from level 2 inversion products for the Anmyon Aerosol Robotic Network (AERONET) site between 1999 and 2007. Secondary inorganic ions, black carbon (BC) and organic carbon (OC) were separated from fine mode aerosols, and mineral dust (MD), MD mixed with carbon, mixed coarse particles were separated from coarse mode aerosols. Four parameters (aerosol optical depth, single scattering albedo, absorption Angstrom exponent, and fine mode fraction) were used for this classification. Monthly variation of the occurrence rate of each aerosol type reveals that MD and MD mixed with carbon are frequent in spring. Although the fraction among dominant aerosols and occurrence rates of BC and OC tend to be high in cold season for heating, their contributions are variable but consistent due to various combustion sources. Secondary inorganic ions are most prevalent from June to August; the effective radius of these fine mode aerosols increases with water vapor content because of hygroscopic growth. To evaluate the validity of aerosol types identified, dominant aerosols at worldwide AERONET sites (Beijing, Mexico City, Goddard Space Flight Center, Mongu, Alta Floresta, Cape Verde), which have distinct source characteristics, were classified into the same aerosol types. The occurrence rate and fraction of the aerosol types at the selected sites confirm that the classification in this study is reasonable. However, mean optical properties of the aerosol types are generally influenced by the aerosol types with large fractions. The present work shows that the identification of dominant aerosols is effective even at a single site, provided that the archive of the data set is properly available.


2007 ◽  
Vol 7 (3) ◽  
pp. 7347-7397 ◽  
Author(s):  
D. G. Kaskaoutis ◽  
H. D. Kambezidis ◽  
N. Hatzianastassiou ◽  
P. G. Kosmopoulos ◽  
K. V. S. Badarinath

Abstract. The Ångström exponent, α, is often used as a qualitative indicator of aerosol particle size. In this study, aerosol optical depth (AOD) and Ångström exponent (α) data were analyzed to obtain information about the adequacy of the simple use of the Ångström exponent for characterizing aerosols, and for exploring possibilities for a more efficient characterization of aerosols. This was made possible by taking advantage of the spectral variation of α, the so-called curvature. The data were taken from four selected AERONET stations, which are representative of four aerosol types, i.e. biomass burning, pollution, desert dust and maritime. Using the least-squares method, the Ångström-α was calculated in the spectral interval 340–870 nm, along with the coefficients α1 and α2 of the second order polynomial fit to the plotted logarithm of AOD versus the logarithm of wavelength, and the second derivative of α. The results show that the spectral curvature can provide important additional information about the different aerosol types, and can be effectively used to discriminate between them, since the fine-mode particles exhibit negative curvature, while the coarse-mode aerosols positive. In addition, the curvature has always to be taken into account in the computations of Ångström exponent values in the spectral intervals 380–440 nm and 675–870 nm, since fine-mode aerosols exhibit larger α675–870 than α380–440 values, and vice-versa for coarse-mode particles. A second-order polynomial fit simulates the spectral dependence of the AODs very well, while the associated constant term varies proportionally to the aerosol type. The correlation between the coefficients α1 and α2 of the second-order polynomial fit and the Ångström exponent α, and the atmospheric turbidity, is further investigated. The obtained results reveal important features, which can be used for better discriminating between different aerosol types.


2015 ◽  
Vol 15 (9) ◽  
pp. 13551-13605
Author(s):  
M. J. M. Penning de Vries ◽  
S. Beirle ◽  
C. Hörmann ◽  
J. W. Kaiser ◽  
P. Stammes ◽  
...  

Abstract. Detecting the optical properties of aerosols using passive satellite-borne measurements alone is a difficult task due to the broad-band effect of aerosols on the measured spectra and the influences of surface and cloud reflection. We present another approach to determine aerosol type, namely by studying the relationship of aerosol optical depth (AOD) with trace gas abundance, aerosol absorption, and mean aerosol size. Our new Global Aerosol Classification Algorithm, GACA, examines relationships between aerosol properties (AOD and extinction Ångström exponent from the Moderate Resolution Imaging Spectroradiometer (MODIS), UV Aerosol Index from the second Global Ozone Monitoring Experiment, GOME-2) and trace gas column densities (NO2, HCHO, SO2 from GOME-2, and CO from MOPITT, the Measurements of Pollution in the Troposphere instrument) on a monthly mean basis. First, aerosol types are separated based on size (Ångström exponent) and absorption (UV Aerosol Index), then the dominating sources are identified based on mean trace gas columns and their correlation with AOD. In this way, global maps of dominant aerosol type and main source type are constructed for each season and compared with maps of aerosol composition from the global MACC (Monitoring Atmospheric Composition and Climate) model. Although GACA cannot correctly characterize transported or mixed aerosols, GACA and MACC show good agreement regarding the global seasonal cycle, particularly for urban/industrial aerosols. The seasonal cycles of both aerosol type and source are also studied in more detail for selected 5° × 5° regions. Again, good agreement between GACA and MACC is found for all regions, but some systematic differences become apparent: the variability of aerosol composition (yearly and/or seasonal) is often not well captured by MACC, the amount of mineral dust outside of the dust belt appears to be overestimated, and the abundance of secondary organic aerosols is underestimated in comparison with GACA. Whereas the presented study is of exploratory nature, we show that the developed algorithm is well suited to evaluate climate and atmospheric composition models by including aerosol type and source obtained from measurements into the comparison, instead of focusing on a single parameter, e.g. AOD. The approach could be adapted to constrain the mix of aerosol types during the process of a combined data assimilation of aerosol and trace gas observations.


Sign in / Sign up

Export Citation Format

Share Document