Role of Coarse and Fine Mode Aerosols in MODIS AOD Retrieval: a case study over southern India

Abstract. In the present study we compare the MODIS (Moderate Resolution Imaging Spectroradiometer) derived aerosol optical depth (AOD) data with that obtained from operating sky-radiometer at a remote rural location in southern India (Gadanki, 13.45° N, 79.18° E) from April 2008 to March 2011. While the comparison between total (coarse mode + fine mode) AODs shows correlation coefficient (R) value of about 0.71 for Terra and 0.77 for Aqua, if one separates the AOD into fine and coarse mode, the comparison becomes very poor, particularly for fine mode with an R value of 0.44 for both Terra and Aqua. The coarse mode AOD derived from MODIS and sky-radiometer compare better with an R value of 0.74 for Terra and 0.66 for Aqua. The seasonal variation is also well captured by both ground-based and satellite measurements. It is shown that both the total AOD and fine mode AOD are significantly underestimated with slope of regression line 0.75 and 0.35 respectively, whereas the coarse mode AOD is overestimated with a slope value of 1.28 for Terra. Similar results are found for Aqua where the slope of the regression line for total AOD and fine mode AOD are 0.72 and 0.27 whereas 0.95 for coarse mode. The fine mode fraction derived from MODIS data is less than one-half of that derived from the sky-radiometer data. Based on these observations and comparison of single scattering albedo observed using sky-radiometer with that of MODIS aerosol models, we argue that the selection of aerosol types used in the MODIS retrieval algorithm may not be appropriate particularly in the case of southern India. Instead of selecting a moderately absorbing aerosol model (as being done currently in the MODIS retrieval) a more absorbing aerosol model could be a better fit for the fine mode aerosols, while reverse is true for the coarse mode aerosols, where instead of using "dust aerosols" which is relatively absorbing type, usage of coarse sea-salt particles which is less absorbing is more appropriate. However, not all the differences could be accounted based on aerosol model, other factors like errors in retrieval of surface reflectance may also be significant in causing underestimation of AOD by MODIS.

Download Full-text

Role of coarse and fine mode aerosols in MODIS AOD retrieval: a case study

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-6-9109-2013 ◽

2013 ◽

Vol 6 (5) ◽

pp. 9109-9132 ◽

Cited By ~ 1

Author(s):

M. N. Sai Suman ◽

H. Gadhavi ◽

V. Ravi Kiran ◽

A. Jayaraman ◽

S. V. B. Rao

Keyword(s):

South India ◽

Retrieval Algorithm ◽

Coarse Mode ◽

Modis Aod ◽

Moderate Resolution ◽

Fine Mode ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. In the present study we have compared the MODIS (Moderate Resolution Imaging Spectroradiometer) derived aerosol optical depth (AOD) data with that obtained from operating sky-radiometer at a remote rural location in South India (Gadanki, 13.45° N, 79.18° E). While the comparison between total (coarse mode + fine mode) AOD shows R2 value of about 0.71 with a negligible bias of 0.01, if one separates the AOD into fine and coarse mode, the comparison becomes very poor, particularly for fine mode with an R2 value of 0.44. The coarse mode AOD derived from MODIS and sky-radiometer compare better with an R2 value of 0.74 and also the seasonal variation is well captured by both measurements. It is shown that the fine mode fraction derived from MODIS data is more than a factor of two smaller than that derived from the sky-radiometer data. Based on these observations we argue that the selection of aerosol types used in the MODIS retrieval algorithm are not appropriate particularly in the case of South India. Instead of selecting a moderately absorbing aerosol type (as being done currently in the MODIS retrieval) a more absorbing type aerosol is better suited for fine mode aerosols, while reverse is true for the coarse mode aerosols, where instead of using "dust aerosols" which is relatively more absorbing, usage of coarse sea-salt particles which is less absorbing is more appropriate.

Download Full-text

A Theoretical Analysis for Improving Aerosol-Induced CO2 Retrieval Uncertainties Over Land Based on TanSat Nadir Observations Under Clear Sky Conditions

Remote Sensing ◽

10.3390/rs11091061 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1061 ◽

Cited By ~ 1

Author(s):

Xi Chen ◽

Yi Liu ◽

Dongxu Yang ◽

Zhaonan Cai ◽

Hongbin Chen ◽

...

Keyword(s):

Carbon Dioxide ◽

Refractive Index ◽

Estimation Method ◽

Radiative Transfer Model ◽

Retrieval Algorithm ◽

Transfer Model ◽

Aerosol Model ◽

Coarse Mode ◽

Fine Mode ◽

Aerosol Parameters

Aerosols significantly affect carbon dioxide (CO2) retrieval accuracy and precision by modifying the light path. Hyperspectral measurements in the near infrared and shortwave infrared (NIR/SWIR) bands from the generation of new greenhouse gas satellites (e.g., the Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite, TanSat) contain aerosol information for correction of scattering effects in the retrieval. Herein, a new approach is proposed for optimizing the aerosol model used in the TanSat CO2 retrieval algorithm to reduce CO2 uncertainties associated with aerosols. The weighting functions of hyperspectral observations with respect to elements in the state vector are simulated by a forward radiative transfer model. Using the optimal estimation method (OEM), the information content and each component of the CO2 column-averaged dry-air mole fraction (XCO2) retrieval errors from the TanSat simulations are calculated for typical aerosols which are described by Aerosol Robotic Network (AERONET) inversion products at selected sites based on the a priori and measurement assumptions. The results indicate that the size distribution parameters (reff, veff), real refractive index coefficient of fine mode (arf) and fine mode fraction (fmf) dominate the interference errors, with each causing 0.2–0.8 ppm of XCO2 errors. Given that only 4–7 degrees of freedom for signal (DFS) of aerosols can be obtained simultaneously and CO2 information decreases as more aerosol parameters are retrieved, four to seven aerosol parameters are suggested as the most appropriate for inclusion in CO2 retrieval. Focusing on only aerosol-induced XCO2 errors, forward model parameter errors, rather than interference errors, are dominant. A comparison of these errors across different aerosol parameter combination groups reveals that fewer aerosol-induced XCO2 errors are found when retrieving seven aerosol parameters. Therefore, the model selected as the optimal aerosol model includes aerosol optical depth (AOD), peak height of aerosol profile (Hp), width of aerosol profile (Hw), effective variance of fine mode aerosol (vefff), effective radius of coarse mode aerosol (reffc), coefficient a of the real part of the refractive index for the fine mode and coarse mode (arf and arc), with the lowest error of less than 1.7 ppm for all aerosol and surface types. For marine aerosols, only five parameters (AOD, Hp, Hw, reffc and arc) are recommended for the low aerosol information. This optimal aerosol model therefore offers a theoretical foundation for improving CO2 retrieval precision from real TanSat observations in the future.

Download Full-text

MODIS Retrieval of Aerosol Optical Depth over Turbid Coastal Water

10.20944/preprints201705.0157.v1 ◽

2017 ◽

Author(s):

Yi WANG ◽

Jun Wang ◽

Robert C Levy ◽

Xiaoguang Xu ◽

Jeffrey S Reid

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Coastal Water ◽

Visible Spectrum ◽

Single Scattering ◽

Retrieval Algorithm ◽

Modis Aod ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Large Water

We present a new approach to retrieve Aerosol Optical Depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) over the turbid coastal water. This approach supplements the operational Dark Target (DT) aerosol retrieval algorithm that currently don’t conduct any AOD retrieval in the regions with large water-leaving radiances in the visible spectrum. Over the global coastal water regions in all cloud-free conditions, this unavailability of AOD retrievals due to the inherent limitation in existing DT algorithm is ~20%. Here, we refine the MODIS DT algorithm by considering that water-leaving radiance at 2.1 μm is negligible regardless of water turbidity. This refinement, with the assumption that the aerosol single scattering properties over coastal turbid water are similar to that over the adjacent open-ocean pixels, yields ~18% more of MODIS-AERONET collocated pairs for six AEROENT stations in the coastal water regions. Furthermore, comparison with these AERONET observations show that the new AOD retrievals are in either equivalent or better accuracy than those retrieved by the MODIS operational algorithm (over coastal land and non-turbid coastal water). Combining the new retrievals with the existing MODIS operational retrievals not only yield an overall improvement of AOD over those coastal water regions, but also successfully extend the spatial and temporal coverage of MODIS AOD retrievals over the coastal regions where 60% of human population resides, and thereby, aerosol impacts on regional air quality and climate are expected to be significant.

Download Full-text

Indirect estimation of absorption properties for fine aerosol particles using AATSR observations: a case study of wildfires in Russia in 2010

Atmospheric Measurement Techniques ◽

10.5194/amt-8-3075-2015 ◽

2015 ◽

Vol 8 (8) ◽

pp. 3075-3085 ◽

Cited By ~ 3

Author(s):

E. Rodríguez ◽

P. Kolmonen ◽

T. H. Virtanen ◽

L. Sogacheva ◽

A.-M. Sundström ◽

...

Keyword(s):

Optical Depth ◽

Continuous Variation ◽

Radiative Transfer Model ◽

Retrieval Algorithm ◽

Transfer Model ◽

Retrieval Process ◽

Aerosol Model ◽

Coarse Mode ◽

Fine Mode ◽

Aerosol Properties

Abstract. The Advanced Along-Track Scanning Radiometer (AATSR) on board the ENVISAT satellite is used to study aerosol properties. The retrieval of aerosol properties from satellite data is based on the optimized fit of simulated and measured reflectances at the top of the atmosphere (TOA). The simulations are made using a radiative transfer model with a variety of representative aerosol properties. The retrieval process utilizes a combination of four aerosol components, each of which is defined by their (lognormal) size distribution and a complex refractive index: a weakly and a strongly absorbing fine-mode component, coarse mode sea salt aerosol and coarse mode desert dust aerosol). These components are externally mixed to provide the aerosol model which in turn is used to calculate the aerosol optical depth (AOD). In the AATSR aerosol retrieval algorithm, the mixing of these components is decided by minimizing the error function given by the sum of the differences between measured and calculated path radiances at 3–4 wavelengths, where the path radiances are varied by varying the aerosol component mixing ratios. The continuous variation of the fine-mode components allows for the continuous variation of the fine-mode aerosol absorption. Assuming that the correct aerosol model (i.e. the correct mixing fractions of the four components) is selected during the retrieval process, also other aerosol properties could be computed such as the single scattering albedo (SSA). Implications of this assumption regarding the ratio of the weakly/strongly absorbing fine-mode fraction are investigated in this paper by evaluating the validity of the SSA thus obtained. The SSA is indirectly estimated for aerosol plumes with moderate-to-high AOD resulting from wildfires in Russia in the summer of 2010. Together with the AOD, the SSA provides the aerosol absorbing optical depth (AAOD). The results are compared with AERONET data, i.e. AOD level 2.0 and SSA and AAOD inversion products. The RMSE (root mean square error) is 0.03 for SSA and 0.02 for AAOD lower than 0.05. The SSA is further evaluated by comparison with the SSA retrieved from the Ozone Monitoring Instrument (OMI). The SSA retrieved from both instruments show similar features, with generally lower AATSR-estimated SSA values over areas affected by wildfires.

Download Full-text

Improvement of aerosol optical depth retrieval from MODIS spectral reflectance over the global ocean using new aerosol models archived from AERONET inversion data and tri-axial ellipsoidal dust database

Atmospheric Chemistry and Physics ◽

10.5194/acp-12-7087-2012 ◽

2012 ◽

Vol 12 (15) ◽

pp. 7087-7102 ◽

Cited By ~ 44

Author(s):

J. Lee ◽

J. Kim ◽

P. Yang ◽

N. C. Hsu

Keyword(s):

Optical Properties ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Single Scattering ◽

Global Ocean ◽

Aerosol Size Distribution ◽

Scattering Angle ◽

Aerosol Model ◽

Fine Mode ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. New over-ocean aerosol models are developed by integrating the inversion data from the Aerosol Robotic Network (AERONET) sun/sky radiometers with a database for the optical properties of tri-axial ellipsoid particles. The new aerosol models allow more accurate retrieval of aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) in the case of high AOD (AOD > 0.3). The aerosol models are categorized by using the fine-mode fraction (FMF) at 550 nm and the single-scattering albedo (SSA) at 440 nm from the AERONET inversion data to include a variety of aerosol types found around the globe. For each aerosol model, the changes in the aerosol optical properties (AOPs) as functions of AOD are considered to better represent aerosol characteristics. Comparisons of AODs between AERONET and MODIS for the period from 2003 to 2010 show that the use of the new aerosol models enhances the AOD accuracy with a Pearson coefficient of 0.93 and a regression slope of 0.99 compared to 0.92 and 0.85 calculated using the MODIS Collection 5 data. Moreover, the percentage of data within an expected error of ± (0.03 + 0.05 × AOD) is increased from 62% to 64% for overall data and from 39% to 5% for AOD > 0.3. Errors in the retrieved AOD are further characterized with respect to the Ångström exponent (AE), scattering angle (Θ), SSA, and air mass factor (AMF). Due to more realistic AOPs assumptions, the new algorithm generally reduces systematic errors in the retrieved AODs compared with the current operational algorithm. In particular, the underestimation of fine-dominated AOD and the scattering angle dependence of dust-dominated AOD are significantly mitigated as results of the new algorithm's improved treatment of aerosol size distribution and dust particle nonsphericity.

Download Full-text

Comparison of MODIS 3 km and 10 km resolution aerosol optical depth retrievals over land with airborne sunphotometer measurements during ARCTAS summer 2008

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-2015-2014 ◽

2014 ◽

Vol 14 (4) ◽

pp. 2015-2038 ◽

Cited By ~ 26

Author(s):

J. M. Livingston ◽

J. Redemann ◽

Y. Shinozuka ◽

R. Johnson ◽

P. B. Russell ◽

...

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Retrieval Algorithm ◽

Grid Cells ◽

Data Set ◽

Clear Sky ◽

Modis Aod ◽

Research Aircraft ◽

Bright Surface ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. Airborne sunphotometer measurements acquired by the NASA Ames Airborne Tracking Sunphotometer (AATS-14) aboard the NASA P-3 research aircraft are used to evaluate dark-target over-land retrievals of extinction aerosol optical depth (AOD) from spatially and temporally near-coincident measurements by the Moderate Resolution Imaging Spectroradiometer (MODIS) during the summer 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field campaign. The new MODIS Collection 6 aerosol data set includes retrievals of AOD at both 10 km × 10 km and 3 km × 3 km (at nadir) resolution. In this paper we compare MODIS and AATS AOD at 553 nm in 58 10 km and 134 3 km retrieval grid cells. These AOD values were derived from data collected over Canada on four days during short time segments of five (four Aqua and one Terra) satellite overpasses of the P-3 during low-altitude P-3 flight tracks. Three of the five MODIS–AATS coincidence events were dominated by smoke: one included a P-3 transect of a well-defined smoke plume in clear sky, but two were confounded by the presence of scattered clouds above smoke. The clouds limited the number of MODIS retrievals available for comparison, and led to MODIS AOD retrievals that underestimated the corresponding AATS values. This happened because the MODIS aerosol cloud mask selectively removed 0.5 km pixels containing smoke and clouds before the aerosol retrieval. The other two coincidences (one Terra and one Aqua) occurred during one P-3 flight on the same day and in the same general area, in an atmosphere characterized by a relatively low AOD (< 0.3), spatially homogeneous regional haze from smoke outflow with no distinguishable plume. For the ensemble data set for MODIS AOD retrievals with the highest-quality flag, MODIS AOD agrees with AATS AOD within the expected MODIS over-land AOD uncertainty in 60% of the retrieval grid cells at 10 km resolution and 69% at 3 km resolution. These values improve to 65 % and 74%, respectively, when the cloud-affected case with the strongest plume is excluded. We find that the standard MODIS dark-target over-land retrieval algorithm fails to retrieve AOD for thick smoke, not only in cloud-contaminated regions but also in clear sky. We attribute this to deselection, by the cloud and/or bright surface masks, of 0.5 km resolution pixels that contain smoke.

Download Full-text

Multi-Satellite Retrieval of SSA using OMI-MODIS algorithm

10.5194/acp-2018-564 ◽

2018 ◽

Author(s):

Kruthika Eswaran ◽

Sreedharan Krishnakumari Satheesh ◽

Jayaraman Srinivasan

Keyword(s):

Radiative Forcing ◽

Single Scattering ◽

Retrieval Algorithm ◽

Ozone Monitoring Instrument ◽

Aerosol Composition ◽

Aerosol Radiative Forcing ◽

Aerosol Absorption ◽

Moderate Resolution Imaging Spectroradiometer ◽

Absorbing Aerosols ◽

Aerosol Type

Abstract. Single scattering albedo (SSA) represents a unique identification of aerosol type and aerosol radiative forcing. However, SSA retrievals are highly uncertain due cloud contamination and aerosol composition. Recent improvement in the SSA retrieval algorithm has combined the superior cloud masking technique of Moderate Resolution Imaging Spectroradiometer (MODIS) and the better sensitivity of Ozone Monitoring Instrument (OMI) to aerosol absorption. The combined OMI-MODIS algorithm has been validated over a small spatial and temporal scale only. The present study validates the algorithm over global oceans for the period 2008–2012. The geographical heterogeneity in the aerosol type and concentration over the Atlantic Ocean, the Arabian Sea and the Bay of Bengal was useful to delineate the effect of aerosol type on the retrieval algorithm. We also noted that OMI overestimates SSA when absorbing aerosols were present closer to the surface. We attribute this overestimation to data discontinuity in the aerosol height climatology derived from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. OMI uses pre-defined aerosol heights over regions where CALIPSO climatology is not present leading to overestimation of SSA. The importance of aerosol height was also studied using the Santa Barbara DISORT radiative transfer (SBDART) model. The results from the joint retrieval were validated with ground-based measurements and it was seen that OMI-MODIS SSA retrievals were better constrained than OMI only retrieval.

Download Full-text

Improving GOES Advanced Baseline Imager (ABI) Aerosol Optical Depth (AOD) Retrievals using an Empirical Bias Correction Algorithm

10.5194/amt-2020-115 ◽

2020 ◽

Author(s):

Hai Zhang ◽

Shobha Kondragunta ◽

Istvan Laszlo ◽

Mi Zhou

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Bias Correction ◽

Land Surface ◽

Infrared Imaging ◽

Retrieval Algorithm ◽

Correction Algorithm ◽

Time Step ◽

Modis Aod ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) series enables retrieval of aerosol optical depth (AOD) from geostationary satellites using a multi-band algorithm similar to those of polar-orbiting satellites’ sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Therefore, ABI AOD is expected to have accuracy and precision comparable to MODIS AOD and VIIRS AOD. However, this work demonstrates that the current version of GOES-16 (GOES-East) ABI AOD has diurnally varying biases due to errors in the land surface reflectance relationship between the bands used in the ABI AOD retrieval algorithm, which vary with respect to the Sun-satellite geometry. To reduce these biases, an empirical bias correction algorithm has been developed based on the lowest observed ABI AOD of an adjacent 30-day period and the background AOD at each time step and at each pixel. The bias correction algorithm improves the performance of ABI AOD compared to AErosol RObotic NETwork (AERONET) AOD, especially for the high and medium (top 2) quality ABI AOD. AOD data for the period August 6 to December 31, 2018 are used to validate the bias correction algorithm. For the top 2 qualities ABI AOD, after bias correction, the correlation between ABI AOD and AERONET AOD improves from 0.87 to 0.91, the mean bias improves from 0.04 to 0.00, and root mean square error (RMSE) improves from 0.09 to 0.05. These results for the bias corrected top 2 qualities ABI AOD are comparable to those of the uncorrected high-quality ABI AOD. Thus, by using the top 2 qualities of ABI AOD in conjunction with the bias correction algorithm, the area coverage of ABI AOD is substantially increased without loss of data accuracy.

Download Full-text

Closure on the single scattering albedo in the WRF-Chem framework using data from the MILAGRO campaign

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-9-5009-2009 ◽

2009 ◽

Vol 9 (1) ◽

pp. 5009-5054

Author(s):

J. C. Barnard ◽

J. D. Fast ◽

G. Paredes-Miranda ◽

W. P. Arnott

Keyword(s):

Optical Properties ◽

Black Carbon ◽

Chemical Properties ◽

Single Scattering ◽

Single Scattering Albedo ◽

Aerosol Optical Properties ◽

Mean Values ◽

Coarse Mode ◽

Fine Mode ◽

Using Data

Abstract. Data from the MILAGRO field campaign, which took place in the Mexico City Metropolitan Area (MCMA) during March 2006, is used to perform a closure experiment between aerosol chemical properties and aerosol optical properties. Measured aerosol chemical properties, obtained from the MILAGRO T1 site, are fed to two different "chemical to optical properties" modules. One module uses a sectional approach and is identical to that used in the WRF-Chem model, while the other is based on a modal approach. This modal code is employed as an independent check on the WRF-Chem module. Both modules compute aerosol optical properties and, in particular, the single-scattering albedo, ϖ0, as a function of time. The single-scattering albedos are compared to independent measurements obtained from a photoacoustic spectrometer (PAS). Because chemical measurements of the aerosol coarse mode were not available, and the inlet of the PAS could not ingest aerosols larger than about 2 to 3 μm, we focus here on the fine-mode ϖ0. At 870 nm, the wavelength of the PAS measurements, the agreement between the computed (modal and WRF-Chem) and observed fine-mode ϖ0, averaged over the course of the campaign, is reasonably good. The observed ϖ0 value is 0.77, while for both modules, the calculated value was 0.75 resulting in a difference of 0.02 between observations and both computational approaches. This difference is less than the uncertainty of the observed ϖ0 values (6%, or 0.05), and therefore "closure" is achieved, at least for mean values. After adjusting some properties of black carbon absorption and mass concentration within plausible uncertainty limits, the two modules simulate well the diurnal variation of ϖ0, and the absorption coefficient, Babs, but are less successful in calculating the variation of the scattering coefficient, Bscat. This difficulty is probably caused by the presence of larger particles during the day when windblown dust is ubiquitous; this dust likely increases the proportion of large particles introduced into the PAS. The dust also contributes to a very large aerosol mass loading in the coarse mode, and neglect of the coarse mode may cause significant errors, estimated to be as large as 0.07, in the calculation and measurement of ambient ϖ0. Finally, the observed ϖ0 is compared to the ϖ0 computed by the full WRF-Chem model, which includes prognostic aerosol chemistry. Unlike the results discussed above, a comparison between observed and simulated ϖ0 values reveals major differences. This large discrepancy is probably due, in part, to poor characterization of emissions near the T1 site, particularly black carbon emissions.

Download Full-text

Depolarization ratios retrieved by AERONET sun–sky radiometer data and comparison to depolarization ratios measured with lidar

Atmospheric Chemistry and Physics ◽

10.5194/acp-17-6271-2017 ◽

2017 ◽

Vol 17 (10) ◽

pp. 6271-6290 ◽

Cited By ~ 11

Author(s):

Youngmin Noh ◽

Detlef Müller ◽

Kyunghwa Lee ◽

Kwanchul Kim ◽

Kwonho Lee ◽

...

Keyword(s):

Correlation Coefficients ◽

Anthropogenic Pollution ◽

Single Scattering ◽

Size Distributions ◽

Depolarization Ratio ◽

Particle Size Distributions ◽

Coarse Mode ◽

Fine Mode ◽

High Share ◽

Depolarization Ratios

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.

Download Full-text