scholarly journals Look−up tables resolved by complex refractive index to correct particle sizes measured by common research−grade optical particle counters

2021 ◽  
Author(s):  
Paola Formenti ◽  
Claudia Di Biagio ◽  
Yue Huang ◽  
Jasper Kok ◽  
Marc Daniel Mallet ◽  
...  
Keyword(s):  
Particle Size ◽  
Refractive Index ◽  
Light Scattering ◽  
Size Distribution ◽  
Atmospheric Aerosols ◽  
Complex Refractive Index ◽  
Spherical Particles ◽  
Correction Factors ◽  
Number Size Distribution ◽  
Wide Range

Abstract. Optical particle counters (OPC) are widely used to measure the aerosol particle number size distribution at atmospheric ambient conditions and over a large size range. Their measurement principle is based on the dependence of light scattering on particle size. However, this dependence is not monotonic at all sizes and light scattering also depends on the particle composition (i.e., the complex refractive index, CRI) and morphology. Therefore, the conversion of the measured scattered intensity to the desired particle size depends on the microphysical properties of the sampled aerosol population and might not be unique at all sizes. While these complexities have been addressed before, corrections are typically applied ad-hoc and are not standardised. This paper addresses this issue by providing a consistent and extended database of pre−computed correction factors for a wide range of complex refractive index values representing the composition variability of atmospheric aerosols. These correction factors are calculated for five different commercial OPCs (USHAS, PCASP, FSSP, GRIMM and its airborne version Sky− GRIMM, CDP) by assuming Mie theory for homogeneous spherical particles, and by varying the real part of the CRI between 1.33 and 1.75 in steps of 0.01 and the imaginary part between 0.0 and 0.4 in steps of 0.001. Correction factors for mineral dust are provided at the CRI of 1.53 – 0.003i and account for the asphericity of these particles. The datasets described in this paper are distributed at open-access repository: https://doi.org/10.25326/234 (license CC BY, Formenti et al., 2021) maintained by the French national center for Atmospheric data and services AERIS to data users/geophysicists who number size distribution measurements from OPC for their research on atmospheric aerosols. Application and caveats of the CRI-corrections factors are presented and discussed. The dataset presented in this paper is not only useful for correcting the size distribution from an OPC when the particle refractive index is known, but even when only assumptions can be made. Furthermore, this dataset can be useful in calculating uncertainties or sensitivities of aerosol volume/mass/extinction from OPCs given no or limited knowledge of refractive index.

scholarly journals Simultaneous retrieval of the complex refractive index and particle size distribution

2015 ◽  
Vol 23 (15) ◽  
pp. 19328 ◽  
Author(s):  
Yatao Ren ◽  
Hong Qi ◽  
Qin Chen ◽  
Liming Ruan ◽  
Heping Tan
Keyword(s):  
Particle Size ◽  
Refractive Index ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Complex Refractive Index

scholarly journals Statistical estimation of stratospheric particle size distribution by combining optical modelling and lidar scattering measurements

2008 ◽  
Vol 8 (17) ◽  
pp. 5435-5448 ◽  
Author(s):  
J. Jumelet ◽  
S. Bekki ◽  
C. David ◽  
P. Keckhut
Keyword(s):  
Particle Size ◽  
Refractive Index ◽  
Standard Deviation ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Optical Model ◽  
Solid Particles ◽  
Spherical Particles ◽  
Lidar Measurements ◽  
Distribution Parameters

Abstract. A method for estimating the stratospheric particle size distribution from multiwavelength lidar measurements is presented. It is based on matching measured and model-simulated backscatter coefficients. The lidar backscatter coefficients measured at the three commonly used wavelengths 355, 532 and 1064 nm are compared to a precomputed look-up table of model-calculated values. The optical model assumes that particles are spherical and that their size distribution is unimodal. This inverse problem is not trivial because the optical model is highly non-linear with a strong sensitivity to the size distribution parameters in some cases. The errors in the lidar backscatter coefficients are explicitly taken into account in the estimation. The method takes advantage of the statistical properties of the possible solution cluster to identify the most probable size distribution parameters. In order to discard model-simulated outliers resulting from the strong non-linearity of the model, solutions farther than one standard deviation of the median values of the solution cluster are filtered out, because the most probable solution is expected to be in the densest part of the cluster. Within the filtered solution cluster, the estimation algorithm minimizes a cost function of the misfit between measurements and model simulations. Two validation cases are presented on Polar Stratospheric Cloud (PSC) events detected above the ALOMAR observatory (69° N – Norway). A first validation is performed against optical particle counter measurements carried out in January 1996. In non-depolarizing regions of the cloud (i.e. spherical particles), the parameters of an unimodal size distribution and those of the optically dominant mode of a bimodal size distribution are quite successfully retrieved, especially for the median radius and the geometrical standard deviation. As expected, the algorithm performs poorly when solid particles drive the backscatter coefficient. A small bias is identified in modelling the refractive index when compared to previous works that inferred PSC type Ib refractive indices. The accuracy of the size distribution retrieval is improved when the refractive index is set to the value inferred in the reference paper. Our results are then compared to values retrieved with another similar method that does not account for the effect of the measurements errors and the non-linearity of the optical model on the likelihood of the solution. The case considered is a liquid PSC observed over northern Scandinavia on January 2005. An excellent agreement is found between the two methods when our algorithm is applied without any statistical filtering of the solution cluster. However, the solution for the geometrical standard deviation appears to be rather unlikely with a value close to unity (σ≈1.04). When our algorithm is applied with solution filtering, a more realistic value of the standard deviation (σ≈1.27) is found. This highlights the importance of taking into account the non linearity of the model together with the lidar errors, when estimating particle size distribution parameters from lidar measurements.

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