Model of Randomly Oriented Spheroids for the Retrieval of Non-Spherical Particle Microphysical Parameters from 3 β + 2 α + 3 δ Lidar Measurements, Part 2: ATLAS (Version 2.0) Retrieval Algorithm.
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| Title: | Model of Randomly Oriented Spheroids for the Retrieval of Non-Spherical Particle Microphysical Parameters from 3 β + 2 α + 3 δ Lidar Measurements, Part 2: ATLAS (Version 2.0) Retrieval Algorithm. |
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| Authors: | Kolgotin, Alexei1 (AUTHOR), Müller, Detlef2 (AUTHOR) dgmueller@whu.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1897. 31p. |
| Subjects: | Particle size distribution, LIDAR, Parameter estimation, Atmospheric aerosols, Computer simulation |
| Abstract: | Highlights: What are the main findings? The novel algorithm ATLAS (version 2.0), which can be used for the retrieval of microphysical parameters of nonspherical particles from 3β + 2α + 3δ lidar measurements, has been developed. ATLAS2.0 can be used to find the solutions to particle size distributions that are described as non-spherical monomodal (NM), non-spherical–spherical bimodal (NSB), non-spherical monomodal–spherical bimodal (NMSB), non-spherical bimodal–spherical monomodal (NBSM), or non-spherical bimodal–spherical bimodal (NBSB). What are the implications of the main findings? Comparison of the results retrieved with ATLAS2.0 and TiARA2.1 in numerical simulation shows that the uncertainties of the parameters obtained with ATLAS2.0 are approximately three times less than the uncertainties found with TiARA2.1. In the next part of this research work, ATLAS2.0 will be used in case studies with 3β + 2α + 3δ lidar measurements, which were carried out in the framework of a field campaign. The results will be compared to data obtained from in situ observations. We present a novel algorithm for the retrieval of non-spherical particle microphysical parameters (PMP) from 3β + 2α + 3δ optical data taken with multiwavelength lidar. The 3β + 2α + 3δ optical datasets describe particle backscatter coefficients (β) at three wavelengths, λ = 355, 532, and 1064 nm, particle extinction coefficients (α) at two wavelengths, λ = 355 and 532 nm, and particle linear depolarization ratios (PLDR, δ) at three wavelengths, λ = 355, 532, and 1064 nm. The algorithm can be used for retrieving bimodal particle size distributions (PSDs). The PSDs can comprise mixtures of spheres and spheroids (SS). One or both modes can comprise spheroid-shaped particles or spherically shaped particles. The spheroids are used for approximating an arbitrary ensemble of non-spherical particles. The algorithm works on the basis of a combination of direct and analytical inversion methods. The algorithm uses the spheroid reference look-up table (RLUT) we developed and presented in part 1 of our research work. The algorithm uses constraints regarding the particle complex refractive index (CRI) and information on relative humidity (RH) in the atmosphere (in the case of aerosol lidar observation) for suppressing retrieval uncertainties. We carried out a numerical simulation study to evaluate the algorithm's performance. In these numerical simulations, we considered perturbed synthetic 3β + 2α + 3δ optical data that mimic different organic carbon (OC)–dust (D) mixtures. Such mixtures are suitable examples for describing bimodal PSDs that consist of a fine mode of spherical particles and a coarse mode of non-spherical particles. The results of the numerical simulation show that (1) the PMPs of each mode of these particle mixtures can be found separately, (2) the mean retrieval errors of the effective radius, number, surface-area, and volume concentrations of these mixtures are 25%, 52%, 9%, and 28%, respectively, and (3) the mean retrieval error of single-scattering albedo (SSA) at 355 nm of these mixtures is as low as ±0.02. SSA retrieval accuracies at 532 and 1064 nm degrade because the complex refractive index (CRI) of OC and D particles depends on the measurement wavelength. In future studies, we will upgrade the algorithm such that it takes into account a spectrally dependent CRI. We also compare the results of our novel algorithm with our TiARA2.1 algorithm. The errors obtained from the TiARA2.1 algorithm are approximately three times larger compared to the errors we obtain with our novel ATLAS algorithm for the case of the OC-D mixtures considered in the present study. We explain the higher accuracy of the PMP retrievals by the use of three PLDRs and the extra constraints placed on CRI and RH. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? The novel algorithm ATLAS (version 2.0), which can be used for the retrieval of microphysical parameters of nonspherical particles from 3β + 2α + 3δ lidar measurements, has been developed. ATLAS2.0 can be used to find the solutions to particle size distributions that are described as non-spherical monomodal (NM), non-spherical–spherical bimodal (NSB), non-spherical monomodal–spherical bimodal (NMSB), non-spherical bimodal–spherical monomodal (NBSM), or non-spherical bimodal–spherical bimodal (NBSB). What are the implications of the main findings? Comparison of the results retrieved with ATLAS2.0 and TiARA2.1 in numerical simulation shows that the uncertainties of the parameters obtained with ATLAS2.0 are approximately three times less than the uncertainties found with TiARA2.1. In the next part of this research work, ATLAS2.0 will be used in case studies with 3β + 2α + 3δ lidar measurements, which were carried out in the framework of a field campaign. The results will be compared to data obtained from in situ observations. We present a novel algorithm for the retrieval of non-spherical particle microphysical parameters (PMP) from 3β + 2α + 3δ optical data taken with multiwavelength lidar. The 3β + 2α + 3δ optical datasets describe particle backscatter coefficients (β) at three wavelengths, λ = 355, 532, and 1064 nm, particle extinction coefficients (α) at two wavelengths, λ = 355 and 532 nm, and particle linear depolarization ratios (PLDR, δ) at three wavelengths, λ = 355, 532, and 1064 nm. The algorithm can be used for retrieving bimodal particle size distributions (PSDs). The PSDs can comprise mixtures of spheres and spheroids (SS). One or both modes can comprise spheroid-shaped particles or spherically shaped particles. The spheroids are used for approximating an arbitrary ensemble of non-spherical particles. The algorithm works on the basis of a combination of direct and analytical inversion methods. The algorithm uses the spheroid reference look-up table (RLUT) we developed and presented in part 1 of our research work. The algorithm uses constraints regarding the particle complex refractive index (CRI) and information on relative humidity (RH) in the atmosphere (in the case of aerosol lidar observation) for suppressing retrieval uncertainties. We carried out a numerical simulation study to evaluate the algorithm's performance. In these numerical simulations, we considered perturbed synthetic 3β + 2α + 3δ optical data that mimic different organic carbon (OC)–dust (D) mixtures. Such mixtures are suitable examples for describing bimodal PSDs that consist of a fine mode of spherical particles and a coarse mode of non-spherical particles. The results of the numerical simulation show that (1) the PMPs of each mode of these particle mixtures can be found separately, (2) the mean retrieval errors of the effective radius, number, surface-area, and volume concentrations of these mixtures are 25%, 52%, 9%, and 28%, respectively, and (3) the mean retrieval error of single-scattering albedo (SSA) at 355 nm of these mixtures is as low as ±0.02. SSA retrieval accuracies at 532 and 1064 nm degrade because the complex refractive index (CRI) of OC and D particles depends on the measurement wavelength. In future studies, we will upgrade the algorithm such that it takes into account a spectrally dependent CRI. We also compare the results of our novel algorithm with our TiARA2.1 algorithm. The errors obtained from the TiARA2.1 algorithm are approximately three times larger compared to the errors we obtain with our novel ATLAS algorithm for the case of the OC-D mixtures considered in the present study. We explain the higher accuracy of the PMP retrievals by the use of three PLDRs and the extra constraints placed on CRI and RH. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121897 |
