Depolarization Ratios Retrieved by AERONET Sun/Sky Radiometer Data and Comparison to Depolarization Ratios Measured With Lidar

Abstract. The linear particle depolarization ratios at 440, 675, 870, and 1020 nm were derived using data taken with 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. A good correlation between the lidar-measured depolarization ratio at 532 nm and the one retrieved by AERONET at 870 nm. We find correlation coefficients R2 of 0.89, 0.92, 0.76, and 0.88 at Seoul, Kongju, Gosan, and Osaka, respectively. The correlation coefficient for the data at 675 nm is lower than the correlation coefficient at 870 and 1020 nm. We find correlation values of 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. We find values of 0.38, 0.62, 0.26, and 0.28 at Seoul, Kongju, Gosan, and Osaka, respectively. 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 mixed with anthropogenic pollution particles. The single-scattering albedo decreases with increasing measurement wavelength for low linear particle depolarization ratios. In contrast, single-scattering albedo increases with decreasing wavelength for high linear particle depolarization ratios. 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 the 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 lower than the coarse-mode fraction derived from the volume concentrations of particle size distributions in which case we can compute the coarse-mode fractions of dust.

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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.

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The Influence of Wildfires on Aerosol Size Distributions in Rural Areas

The Scientific World JOURNAL ◽

10.1100/2012/735697 ◽

2012 ◽

Vol 2012 ◽

pp. 1-13 ◽

Cited By ~ 8

Author(s):

E. Alonso-Blanco ◽

A. I. Calvo ◽

R. Fraile ◽

A. Castro

Keyword(s):

Particle Size ◽

Rural Areas ◽

Size Distributions ◽

Average Increase ◽

Particle Size Distributions ◽

Before And After ◽

Fine Mode ◽

The Mean ◽

The One ◽

Number Of Particles

The number of particles and their size distributions were measured in a rural area, during the summer, using a PCASP-X. The aim was to study the influence of wildfires on particle size distributions. The comparative studies carried out reveal an average increase of around ten times in the number of particles in the fine mode, especially in sizes between 0.10 and 0.14 μm, where the increase is of nearly 20 times. An analysis carried out at three different points in time—before, during, and after the passing of the smoke plume from the wildfires—shows that the mean geometric diameter of the fine mode in the measurements affected by the fire is smaller than the one obtained in the measurements carried out immediately before and after (0.14 μm) and presents average values of 0.11 μm.

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The MESSy aerosol submodel MADE3 (v2.0b): description and a box model test

Geoscientific Model Development ◽

10.5194/gmd-7-1137-2014 ◽

2014 ◽

Vol 7 (3) ◽

pp. 1137-1157 ◽

Cited By ~ 16

Author(s):

J. C. Kaiser ◽

J. Hendricks ◽

M. Righi ◽

N. Riemer ◽

R. A. Zaveri ◽

...

Keyword(s):

Particle Size ◽

Box Model ◽

Aerosol Size Distribution ◽

Sea Spray ◽

Test Case ◽

Size Distributions ◽

Particle Size Distributions ◽

Aerosol Dynamics ◽

Coarse Mode ◽

Made In

Abstract. We introduce MADE3 (Modal Aerosol Dynamics model for Europe, adapted for global applications, 3rd generation; version: MADE3v2.0b), an aerosol dynamics submodel for application within the MESSy framework (Modular Earth Submodel System). MADE3 builds on the predecessor aerosol submodels MADE and MADE-in. Its main new features are the explicit representation of coarse mode particle interactions both with other particles and with condensable gases, and the inclusion of hydrochloric acid (HCl) / chloride (Cl) partitioning between the gas and condensed phases. The aerosol size distribution is represented in the new submodel as a superposition of nine lognormal modes: one for fully soluble particles, one for insoluble particles, and one for mixed particles in each of three size ranges (Aitken, accumulation, and coarse mode size ranges). In order to assess the performance of MADE3 we compare it to its predecessor MADE and to the much more detailed particle-resolved aerosol model PartMC-MOSAIC in a box model simulation of an idealised marine boundary layer test case. MADE3 and MADE results are very similar, except in the coarse mode, where the aerosol is dominated by sea spray particles. Cl is reduced in MADE3 with respect to MADE due to the HCl / Cl partitioning that leads to Cl removal from the sea spray aerosol in our test case. Additionally, the aerosol nitrate concentration is higher in MADE3 due to the condensation of nitric acid on coarse mode particles. MADE3 and PartMC-MOSAIC show substantial differences in the fine particle size distributions (sizes &lesssim; 2 μm) that could be relevant when simulating climate effects on a global scale. Nevertheless, the agreement between MADE3 and PartMC-MOSAIC is very good when it comes to coarse particle size distributions (sizes &gtrsim; 2 μm), and also in terms of aerosol composition. Considering these results and the well-established ability of MADE in reproducing observed aerosol loadings and composition, MADE3 seems suitable for application within a global model.

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Application of Regularization Algorithm to HSRL-2 Observations During Oracles Campaign: Comparison of Retrieved and In Situ Particle Size Distributions and Single Scattering Albedo

EPJ Web of Conferences ◽

10.1051/epjconf/202023702008 ◽

2020 ◽

Vol 237 ◽

pp. 02008

Author(s):

Alexei Kolgotin ◽

Igor Veselovskii ◽

Mikhail Korenskiy ◽

Detlef Müller

Keyword(s):

Particle Size ◽

Single Scattering ◽

Single Scattering Albedo ◽

Size Distributions ◽

Correction Factors ◽

Particle Size Distributions ◽

Regularization Algorithm ◽

Particle Characteristics ◽

Good Agreement

Data obtained from HSRL-2 observations carried out on 20 September 2016 during the ORACLES campaign are publicly accessible. In our presentation we invert 3β+2α data into (1) particle size distributions with a regularization algorithm, and subsequently compute (2) single scattering albedo. We carry out a first comparison to the same particle characteristics measured with airborne in-situ instruments. We find good agreement of the data products. However, a more detailed study is needed as correction factors and sources of retrieval and measurement uncertainties need to be tested.

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Retrieval of Intensive Aerosol Microphysical Parameters from Multiwavelength Raman/HSRL Lidar: Feasibility Study with Artificial Neural Networks

10.5194/amt-2016-7 ◽

2016 ◽

Cited By ~ 4

Author(s):

M. Mustafa Mamun ◽

Detlef Müller

Keyword(s):

Neural Networks ◽

Particle Size ◽

Artificial Neural Networks ◽

Imaginary Part ◽

Effective Radius ◽

Optical Data ◽

Size Distributions ◽

Particle Size Distributions ◽

Microphysical Parameters ◽

532 Nm

Abstract. We present results of a feasibility study that uses Artificial Neural Networks (ANN) for the retrieval of intensive microphysical parameters of atmospheric pollution from combinations of backscatter (β) and extinction coefficients (α) that can be measured with multiwavelength Raman and high-spectral resolution lidar at 355, 532, and 1064 nm. We investigated particle effective radius, and the real and imaginary part of the complex refractive index. ANN could be a useful alternative or supplementary method over the traditional approach of retrieving microphysical particle properties with classical inversion algorithms because data analysis with ANN is significantly faster and allows for investigating the information content of the optical input data. We investigated the data combinations 3β+2α, 3β+1α (355 and or 532 nm), 2β (532, 1064 nm) +1α (532 nm), and 3β with Feedforward Backpropagation Multilayer Perceptron Neural Networks. The synthetic optical data were computed with a Mie-scattering algorithm for monomodal particle size distributions. Mean radii of the size distributions ranged between 0.01 and 0.5 µm, and mode widths ranged between 1.4 and 2.5 resulting in effective radii between 0.13 and 4.1 µm. We tested real parts between 1.2 and 2, and imaginary parts between 0.0i and 0.1i. The complexity of developing the networks did not allow us to test the influence of measurement errors of the optical data but the error produced by the ANN can be quantified. From the five basic data combinations, our current network design allows us to derive effective radius with an accuracy of approximately ±16 to ±35 %, and ±17 to ±39 % if the true mean radii is in the range from 110−250 nm, and 260−500 nm, respectively. The real part can be derived with an accuracy of approximately ±7 to ±10 %. We find retrieval errors of approximately ±31 to ±38 % for the imaginary part. We show that ANN can potentially estimate some particle parameters with various levels of uncertainty not only from what we denote as 3β+2α information but also from data combinations of 3β+1α (355 or 532), 2β (532, 1064) +1α (532), and 3β. We hypothesize that the ANN carries out first a pre-selections of various values of extinction-based Ångström exponents with regard to effective radius and then uses this information to create the strong correlation between particle effective radius and lidar ratios in all particle size distributions (PSDs) we investigated.

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