Most global aerosol models approximate dust as spherical
particles, whereas most remote sensing retrieval algorithms approximate dust as spheroidal particles with a shape distribution that conflicts with
measurements. These inconsistent and inaccurate shape assumptions generate
biases in dust single-scattering properties. Here, we obtain dust
single-scattering properties by approximating dust as triaxial ellipsoidal
particles with observationally constrained shape distributions. We find
that, relative to the ellipsoidal dust optics obtained here, the spherical
dust optics used in most aerosol models underestimate dust single-scattering albedo, mass extinction efficiency, and asymmetry parameter for almost all dust sizes in both the shortwave and longwave spectra. We further find that the ellipsoidal dust optics are in substantially better agreement with observations of the scattering matrix and linear depolarization ratio than the spheroidal dust optics used in most retrieval algorithms. However, relative to observations, the ellipsoidal dust optics overestimate the lidar ratio by underestimating the backscattering intensity by a factor of
Desert dust aerosols are a key atmospheric component (Mahowald et al., 2014; Kok et al., 2021a, b; Adebiyi et al., 2023). Dust impacts the Earth system by modifying the radiation budget (Ito et al., 2021; Kok et al., 2023), hydrological cycle (Miller et al., 2004, 2006), cloud microphysics (Kiselev et al., 2017), and ocean biogeochemistry (Yu et al., 2015; Ito et al., 2019). Furthermore, dust impacts anthropogenic activities by degrading air quality and visibility (Mahowald et al., 2007; Huang et al., 2019) and harming human health (Giannadaki et al., 2014). To accurately estimate these dust impacts, global aerosol models and retrieval algorithms of passive and active remote sensing products need accurate dust single-scattering properties (Dubovik et al., 2006; Winker et al., 2007; Ansmann et al., 2012; Gliß et al., 2021).
Global aerosol models and remote sensing retrieval algorithms use
inconsistent and inaccurate dust shape quantifications. Most aerosol models
approximate dust as
Dust single-scattering properties highly depend on dust shape (Bi et al., 2009, 2010; Lindqvist et al., 2014; Nousianien and Kandler, 2015; Saito et al., 2021; Saito and Yang, 2021; Kong et al., 2022), but global aerosol models and remote sensing retrieval algorithms use inconsistent and inaccurate dust shape quantifications (Fig. 1). Specifically, almost all global aerosol models approximate dust as spherical particles (Fig. 1a; Gliß et al., 2021), whereas most retrieval algorithms approximate dust as spheroidal particles (Fig. 1b and c) and use the length-to-height ratio to quantify dust asphericity (Dubovik et al., 2006; Hsu et al., 2019). By assuming a spherical or spheroidal dust shape, aerosol models and retrieval algorithms equate at least two of three dust perpendicular axes. However, a recent study that compiled dozens of in situ measurements of dust shape worldwide found that the three perpendicular axes differ substantially for most dust particles and thus that the triaxial ellipsoidal shape assumption (Fig. 1d) is more realistic for dust aerosols (Huang et al., 2020). In addition, relative to the compiled observations, aerosol models and retrieval algorithms substantially underestimate dust asphericity (Fig. 1e). These problematic dust shape assumptions of aerosol models and retrieval algorithms generate biases in dust single-scattering properties that further propagate into the estimated dust impacts.
To facilitate accounting for more realistic dust shape in aerosol models and retrieval algorithms, here we obtain dust single-scattering properties by approximating dust as triaxial ellipsoidal particles with observationally constrained shape distributions (Sect. 2). In Sect. 2, we then compare the obtained ellipsoidal dust optics with the spherical dust optics used in most aerosol models and the spheroidal dust optics used in most retrieval algorithms; these three optics simulations are validated against laboratory and field observations of dust optics. In Sect. 3, our results show that the ellipsoidal dust optics agree with observations substantially better than the spherical and spheroidal dust optics. Thus, the ellipsoidal dust optics with observationally constrained shape distributions can help improve aerosol models and retrieval algorithms.
This section presents our methodology for obtaining and evaluating the single-scattering properties of triaxial ellipsoidal dust aerosols constrained by measured dust shape distributions. In Sect. 2.1, we first introduce the definitions of single-scattering properties used in global aerosol models and remote sensing retrieval algorithms. Then, in Sect. 2.2, we obtain the single-scattering properties of ellipsoidal dust ensembles accounting for observational constraints on dust shape. In Sect. 2.3, we introduce the laboratory and field observations used as the ground truth to evaluate our obtained ellipsoidal dust optics. This section also introduces the spherical and spheroidal dust optics used in most aerosol models and retrieval algorithms. By comparing the spherical, spheroidal, and ellipsoidal dust optics against observations, we can test our hypothesis that ellipsoidal dust optics constrained by measured dust shape distributions are more realistic than the spherical and spheroidal dust optics.
Single-scattering properties quantify how aerosols modify incident light after one instance of elastic scattering (Liou, 2002). Remote sensing retrieval algorithms and global aerosol models retrieve dust distributions and estimate dust impacts using seven key single-scattering properties, namely phase function, asymmetry factor, extinction efficiency, mass extinction efficiency, single-scattering albedo, linear depolarization ratio, and lidar ratio. We present the definitions of these single-scattering properties in detail below.
The modification of the incident light by aerosol scattering is quantified by the scattering cross section and the scattering matrix. The scattering cross section
In this section, we first introduce two shape descriptors and their probability distributions from measurement compilation. We use these two probability distributions to quantify the asphericity of dust aerosols, approximating dust as triaxial ellipsoidal particles. Second, we introduce an extensive database containing shape-resolved single-scattering properties of ellipsoidal dust aerosols. Finally, we obtain the single-scattering properties of ellipsoidal dust ensembles by combining the shape-resolved single-scattering properties database with the two probability distributions of dust shape.
Dozens of in situ measurements across the world have used the length-to-width ratio (
We seek to combine the two globally representative dust shape distributions
(Eqs. 9 and 10) with an extensive database containing single-scattering
properties of ellipsoidal dust aerosols (Meng et al., 2010). This database
combined four computational methods to compute the single-scattering
properties. The Lorenz–Mie theory was used for spherical particles with a size parameter
We combined the shape-resolved optics database (Meng et al., 2010) with the two globally representative probability distributions of dust shape (Eqs. 9 and 10) to obtain the single-scattering properties of ensembles of ellipsoidal dust particles. That is, at a given dust volume-equivalent diameter, the obtained optics are ensemble averages of the single-scattering
properties of 121 particle shapes (i.e., 11 values of LWR and 11 values of HWR; Meng et al., 2010); the weighting factor assigned to each particle shape,
Single-scattering properties of spherical and triaxial ellipsoidal dust aerosols in the shortwave and longwave spectra. The left column includes
Using the equations above, we obtain the single-scattering properties of ellipsoidal dust ensembles constrained by measured dust shape distributions. The obtained ellipsoidal dust optics for use in global aerosol models (
We treat the observations of dust optics as the ground truth to evaluate our obtained ellipsoidal dust optics (Sect. 2.2) and the spherical and spheroidal dust optics used in previous studies. In this section, we first introduce laboratory observations of the scattering matrix and field observations of the linear depolarization ratio and lidar ratio. Second, we introduce the spherical and spheroidal dust optics used in most global aerosol models and remote sensing retrieval algorithms. Third, we integrate the size-resolved spherical, spheroidal, and ellipsoidal dust optics simulations over the dust particle size distributions observed for the laboratory and field observations. This integration enables comparisons on an equal footing, since the three optics simulations are size-resolved, whereas the observations were obtained for a mixture of dust aerosols with various particle sizes. Finally, we calculate the root mean square errors between the optics simulations and observations to quantify the performance of the three optics simulations.
The Amsterdam–Granada Light Scattering Database (AGLSD; Muñoz et al., 2012) is publicly accessible (see code and data availability section) and has been widely regarded as the standard to evaluate dust optical models (e.g., Nousiainen and Vermeulen, 2003; Dubovik et al., 2006; Merikallio et al., 2011; Lindqvist et al., 2014; Saito and Yang, 2021). The AGLSD contains laboratory measurements of the scattering matrices at two visible wavelengths of tens of samples with simultaneous measurements of the particle size distributions of these samples. Among these samples, we select two dust samples (i.e., newGobi and newSaharaOSN) and one mineral sample (i.e., feldspar) to evaluate the simulated dust optics for the following reasons. The two dust samples were collected, respectively, during an intense Gobi dust event reaching Beijing (China) in 2006 and an intense Saharan dust event reaching the Observatory of Sierra Nevada in Granada (Spain) in 2004 (Gómez Martín et al., 2021). These two samples are deposited dust aerosols, which are different from the other mineral samples included in AGLSD that were either purchased from commercial sources or generated in the lab by grinding mineral rocks and are thus less accurate representations of dust aerosols (Muñoz et al., 2012; Gómez Martín et al., 2021). In addition to the two dust samples, we also select the mineral sample feldspar. Although the sample feldspar was generated from ground feldspar rocks (Volten et al., 2001), and its representativeness for natural dust aerosols remains uncertain, we still select it because it is the only sample used to constrain the retrieval algorithm of AERONET (AErosol RObotic NETwork; Dubovik et al., 2006), as the newGobi and newSaharaOSN samples have only recently become available (Gómez Martín et al., 2021).
A range of field campaigns has measured the linear depolarization ratio and lidar ratio for Saharan and Asian dust aerosols. During these field campaigns, ground-based or aircraft-carried lidars measured the linear depolarization ratio and lidar ratio of dust plumes at the three common lidar wavelengths of 355, 532, and 1064 nm. We combine the measurement compilations of Tesche et al. (2019) and Saito and Yang (2021) and a new measurement study published after 2021 (i.e., Haarig et al., 2022). This yields a total of six datasets of the linear depolarization ratio and eight datasets of lidar ratio at three wavelengths (Tesche et al., 2009, 2011; Groß et al., 2011, 2015; Burton et al., 2015; Haarig et al., 2017, 2022; Hofer et al., 2020; Hu et al., 2020). We neglect the minor effects of dust multiple scattering and dust mixing with other aerosols on the observation results, as Tesche et al. (2019) and Saito and Yang (2021) did.
Regarding the optics simulations, most global aerosol models use spherical
dust optics (Fig. 1a), and most remote sensing retrieval algorithms use
spheroidal dust optics (Fig. 1b and c) with a shape distribution that conflicts with observations. Aerosol models and retrieval algorithms use
lookup tables containing precalculated dust optics to reduce the computational costs. The lookup table of most aerosol models was calculated by the Lorenz–Mie theory (Liou, 2002). The most widely used lookup table of
retrieval algorithms was calculated by Dubovik et al. (2006), using the
following three steps. First, Dubovik et al. (2006) combined two computational methods (
We use the observations of dust optics as the ground truth to evaluate the
spherical, spheroidal, and ellipsoidal dust optics simulations. However, the
three optics simulations are resolved by dust particle size and refractive
index, whereas the observations were obtained for a mixture of dust aerosols
with various sizes and mineral compositions. The AGLSD laboratory observations measured the particle size distributions (PSDs) of the samples but did not measure their refractive indices, whereas the field lidar observations did not measure the PSDs or the refractive index of dust plumes. To enable comparisons between the optics simulations and observations on an equal footing, we make the following three assumptions about PSDs and the refractive index. First, for the three AGLSD samples (i.e., newGobi, newSaharaOSN, and feldspar), we assume that the PSDs measured by AGLSD are accurate; for the dust plumes observed by field lidar observations across the world, we use the dust PSDs obtained by Adebiyi and Kok (2020), who presented a globally representative PSD of atmospheric dust by leveraging aircraft observations and model simulations. Second, we set the cutoff diameter of all three optics simulations at 63
We obtained dust single-scattering properties by approximating dust as triaxial ellipsoidal particles with observationally constrained shape distributions. We compared these ellipsoidal dust optics with the spherical dust optics used in most global aerosol models and spheroidal dust optics used in most remote sensing retrievals. These comparisons help quantify the biases in global aerosol models and remote sensing retrievals due to problematic dust shape approximations.
We find that, relative to ellipsoidal dust optics, the spherical dust optics
used in most global aerosol models underestimate the four key dust single-scattering properties for almost all dust sizes in both the shortwave
and longwave spectra. First, most aerosol models underestimate the extinction efficiency (
Comparison of the laboratory-measured scattering matrix of AGLSD mineral sample feldspar against the spherical, spheroidal, and ellipsoidal dust optics simulations at the wavelengths of 441.6 nm
Same as Fig. 3, except for a comparison of the laboratory-measured scattering matrix of AGLSD dust sample newGobi against the spherical, spheroidal, and ellipsoidal dust optics simulations at the wavelengths of 488.0 and 647.0 nm.
Same as Fig. 3, except for comparison of the laboratory-measured scattering matrix of AGLSD dust sample newSaharaOSN against the spherical, spheroidal, and ellipsoidal dust optics simulations at the wavelengths of 488.0 and 647.0 nm.
We further find that the ellipsoidal dust optics can reproduce the laboratory-measured phase function (i.e.,
Root mean square errors (RMSEs) between the laboratory-measured and simulated scattering matrices at forward-, side-, and backscattering angles. The top column shows RMSEs at the smaller visible wavelength, which is 441.6 nm for AGLSD mineral sample feldspar and 488.0 nm for the other two dust samples (i.e., newGobi and newSaharaOSN). The bottom column shows RMSEs at the larger visible wavelength, which is 632.8 nm for feldspar and 647.0 nm for newGobi and newSaharaOSN. The vertical error bars denote uncertainties from the dust refractive index and dust shape distributions (see Sect. 2.2 and 2.3).
Comparison of the field-measured
In addition, we find that the ellipsoidal dust optics can reproduce the
laboratory-measured depolarization of incident polarized light (i.e.,
We obtained new dust single-scattering properties by approximating dust as
triaxial ellipsoidal particles with observationally constrained shape distributions (Fig. 1). We find that, relative to these ellipsoidal dust optics, the spherical dust optics used in most aerosol models underestimate dust extinction efficiency, mass extinction efficiency, single-scattering
albedo, and asymmetry parameter for almost all dust sizes in both the shortwave and longwave spectra (Fig. 2). Furthermore, we find that the ellipsoidal dust optics can reproduce the laboratory-measured depolarization of incident polarized light (Figs. 3, 4, and 5, and 6) and the field-measured linear depolarization ratio (Fig. 7a) substantially better than the spheroidal dust optics used in most retrieval algorithms. However, relative to laboratory observations, the ellipsoidal dust optics underestimate the phase function at backscattering angles by a factor of 2 (Figs. 3, 4, 5, and 6). As a result, the ellipsoidal dust optics overestimate the lidar ratio by a factor of What is the implication of the missing dust asphericity in most global aerosol models? What is the implication of the underestimated dust asphericity in most remote sensing retrieval algorithms? How far are we from a perfect dust optical model?
The approximation that dust aerosols are spherical, which is used in most
global aerosol models (Fig. 1; Gliß et al., 2021), generates biases in
dust single-scattering properties. Most aerosol models underestimate the
four single-scattering properties (i.e., dust extinction efficiency
The biases in dust single-scattering properties used in most models have several key implications. First, models underestimate the mass extinction efficiency at the wavelength of 550 nm. Since many models are tuned to match the dust aerosol optical depth at 550 nm inferred from remote sensing observations (Ridley et al., 2016; Gliß et al., 2021), our finding that dust extinguishes more light per unit mass loading than models assume (Fig. 2b) indicates that models overestimate the global dust mass loading. This implication is supported by a previous study (i.e., Kok et al., 2017) that found that dust asphericity can enhance dust mass extinction efficiency by
The second implication is that the dust single-scattering properties using the observed dust shape distributions can improve estimates of dust radiative
effects. For example, Ito et al. (2021) used our single-scattering properties of ellipsoidal dust aerosols to reevaluate the dust radiative effects at the top of the atmosphere (TOA) and the surface. They integrated the rapid radiative transfer model for general circulation models (RRTMG) online within the Integrated Massively Parallel Atmospheric Chemical Transport (IMPACT) model (Ito et al., 2020). They found that accounting for dust asphericity barely changes the dust radiative effect at TOA, whereas dust asphericity strongly enhances the dust cooling effect at the surface (see Table 5 of Ito et al., 2021). Specifically, at TOA, dust asphericity enhances the cooling effect in the shortwave spectrum by 0.04 W m
Most remote sensing retrieval algorithms approximate dust aerosols as being spheroidal particles with a shape distribution chosen to maximize agreement against the observed scattering matrix of AGLSD sample feldspar (Dubovik et al., 2006; Hsu et al., 2019). However, this shape distribution conflicts with observations of dust shape and substantially underestimates dust asphericity (Fig. 1). As a result, the shape approximation used in remote sensing retrievals might generate biases in the dust scattering matrix. Specifically, relative to AGLSD sample feldspar, the spheroidal dust optics, for which the shape distribution was fitted to maximize agreement with this sample, performs similarly to our ellipsoidal dust optics constrained by observed shape distributions (Figs. 3 and 6). Relative to the other two AGLSD samples (i.e., newGobi and newSaharaOSN), neither the spheroidal nor the ellipsoidal dust optics could reproduce the scattering matrix well, although the spheroidal dust optics perform better in reproducing the phase function, and the ellipsoidal dust optics perform better in reproducing the degree of linear polarization and the depolarization ratio (Figs. 4, 5, and 6). Drawing conclusions based on these two AGLSD samples is difficult because the spheroidal dust optics are constrained by sample feldspar instead of these two samples, making it difficult to link the biases in optics to the problematic dust shape approximation. These findings indicate that none of the three optics simulations (i.e., spherical, spheroidal, and ellipsoidal dust optics) could perfectly simulate the scattering matrix. On the one hand, the ellipsoidal dust optics could simulate the dust scattering matrix better than the spheroidal dust optics that are not constrained by the AGLSD sample feldspar. On the other hand, the ellipsoidal dust optics cannot simulate the phase function at backscattering angles well.
The biases in the dust scattering matrix can propagate into the depolarization ratio and lidar ratio, which are important to aerosol classification and aerosol retrieval algorithms of remote sensing products. For example, Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), as the first spaceborne polarization lidar, has measured the vertical profiles of depolarization ratio and attenuated backscatter ratio across the globe since 2006 (Winker et al., 2007, 2009). CALIOP's aerosol classification algorithm first uses a threshold of attenuated backscatter ratio at the wavelength of 532 nm (i.e.,
The link between the lidar ratio and depolarization ratio and dust microphysical properties is also key to retrievals of dust microphysical properties. With the development of advanced lidar sensors, simultaneous observations of lidar ratio and depolarization ratio at multiple wavelengths are available (Freudenthaler et al., 2009; Tesche et al., 2009, 2011; Groß et al., 2015; Haarig et al., 2017, 2022). These datasets enable the inversion of dust microphysical properties (such as effective radius and the real and imaginary refractive index) once the lookup table on the relationship between the lidar ratio and depolarization ratio and dust microphysical properties is given (Müller et al., 2012, 2013). The lookup table of Dubovik et al. (2006) that contains spheroidal dust optics remains the most popular in the retrieval algorithms of lidar products (Müller et al., 2013; Tesche et al., 2019). The biases in the spheroidal dust optics due to underestimated dust asphericity can propagate into the aerosol classification and aerosol retrieval algorithms that further bias the estimated dust impacts.
Although the ellipsoidal dust optics show excellent agreement with the linear depolarization ratio (Fig. 7a), they overestimate the lidar ratio (Fig. 7b) by underestimating the backscattering intensity by a factor of
A comparison between the ellipsoidal dust optical model (the present work) and the hexahedral dust optical model (Saito and Yang, 2021).
We developed a new dust optical model accounting for observational
constraints on dust shape distributions. The newly developed ellipsoidal
dust optics are in better agreement with measurements of the scattering
matrix and indicate that global aerosol models underestimate the four key
single-scattering properties. Although the ellipsoidal dust optics show
better agreement against measurements of the depolarization ratio than the
spheroidal dust optics used in most remote sensing retrievals, they overestimate the lidar ratio by a factor of We encourage more laboratory observations of the scattering matrices of atmospheric dust aerosols with simultaneous measurements of the microphysical properties of these samples, namely their size distribution, refractive index, and shape distribution. The AGLSD sample feldspar had been the only dataset used in evaluating the simulated scattering matrix of dust optical models (Dubovik et al., 2006) until 2021, when two more samples (newGobi and newSaharaOSN) were published (Gómez Martín et al., 2021). These three samples are problematic for the following three reasons. First, their representativeness for atmospheric dust aerosols remains unknown, since the sample feldspar are not natural dust aerosols but rather were generated by grinding feldspar rocks and the two other samples are of deposited dust and are substantially coarser than is typical of atmospheric dust (Kok et al., 2017; Ryder et al., 2019; Adebiyi et al., 2020, 2023; Liu et al., 2019, 2020). Second, the refractive indices and shape distributions of the three samples were not measured simultaneously. Most studies evaluated their optical models assuming a wide range of refractive indices and particle shapes and used the averages as the evaluation results (e.g., Nousiainen and Vermeulen, 2003; Dubovik et al., 2006; Veihelmann et al., 2006; Merikallio et al., 2011; Lindqvist et al., 2014; Saito and Yang, 2021; Saito et al., 2021). Future simultaneous observations of refractive index and particle shape will help narrow the uncertainty range and identify the primary source of error. Finally, the exact backscattering and forward-scattering properties of the three samples are not available, since laboratory measurements struggle with technical difficulties at We encourage a systematic investigation of the relative impacts of dust body shape, surface corners, and surface roughness on the backscattering properties. We compared the advantages and shortcomings of the ellipsoidal dust model (the present work) and the recently published hexahedral dust model (Saito and Yang, 2021) in Table 1. Both optical models have strong application potential because they extensively cover wide ranges of size parameter and dust refractive index. On the one hand, the ellipsoidal dust model is more advanced than the hexahedral dust model in being constrained against measured dust shape distributions (see Sect. 2.2, Fig. 1, and Table 1). The hexahedral dust model is constrained against the degree of sphericity that is converted from the mean length-to-width ratio of Huang et al. (2020) and ignores the dust asphericity due to the height-to-width ratio (see Fig. 2a of Saito and Yang, 2021). As such, the hexahedral dust model underestimates the dust asphericity relative to dust shape observations. On the other hand, the hexahedral dust model is more advanced than the ellipsoidal dust model in accounting for sharp corners and coherent backscattering enhancement. The hexahedral dust model uses the physical geometric optics method (PGOM; Yang and Liou, 1996, 1997) to simulate the scattering properties for large dust particles (size parameter Future work that defines descriptors for dust surface texture and observes the texture descriptors of atmospheric dust aerosols is needed. Although Huang et al. (2020) extensively compiled measurements of the macroscale shape characteristics of dust aerosols (i.e., dust body shape), few studies have measured the microscale shape characteristics of dust aerosols (i.e., surface corners and roughness). The two reasons that there are so few observations of the dust microscale shape are that these observations require more advanced microscopy techniques (Woodward et al., 2015) and that the descriptors to quantify the microscale shape characteristics are lacking (Nousiainen and Kandler, 2015). Advanced microscopy techniques have been used to image the microscale surface roughness of Arizona test dust less than 5
The single-scattering properties used in global aerosol models and remote sensing retrieval algorithms are critical for accurate simulations of dust distributions and dust impacts. Most global aerosol models approximate dust as spherical particles, whereas most remote sensing retrieval algorithms approximate dust as spheroidal particles with a shape distribution that conflicts with observations. These inconsistent and inaccurate shape assumptions generate biases in dust single-scattering properties.
Here, we obtain dust single-scattering properties by approximating dust as triaxial ellipsoidal particles with observationally constrained shape distributions. We find that, relative to the ellipsoidal dust optics obtained here, the spherical dust optics used in most global aerosol models underestimate dust extinction efficiency, mass extinction efficiency, single-scattering albedo, and asymmetry parameter for almost all dust sizes in both the shortwave and longwave spectra. These biases in the dust optics used in global aerosol models occur because these optics neglect or underestimate the effects of dust asphericity. The ellipsoidal dust optics developed in this work – and available at
We further find that our ellipsoidal dust optics show a mixed performance in
reproducing angle-dependent measurements that are important for remote
sensing retrievals. These optics reproduce laboratory measurements of the
depolarization of incident polarized light and field measurements of the
linear depolarization ratio substantially better than the spheroidal dust
optics that are used in most retrieval algorithms. However, the ellipsoidal
dust optics underestimate laboratory observations of the phase function of
dust at backscattering angles by a factor of
The Amsterdam–Granada light scattering database is publicly available at
YH designed the study, analyzed simulated and observational datasets, and wrote the paper. JFK co-designed and supervised the study. MS provided the Saito and Yang (2021) compilation on the observed lidar ratio and provided insightful discussions about the coherent backscatter enhancement and particle surface roughness. OM provided helpful guidance on the Amsterdam–Granada light scattering dataset. All authors edited and commented on the paper.
The contact author has declared that none of the authors has any competing interests.
The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. government.Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The authors thank Oleg Dubovik, for guidance on using the Dubovik et al. (2006) kernel on the spheroidal dust optics, Ping Yang and Bingqi Yi, for providing the Meng et al. (2010) database and interpolation code on the ellipsoidal dust optics, and Matthias Tesche, for providing the Tesche et al. (2019) compilation on the observed linear depolarization ratio. In addition, the authors thank Timo Nousiainen, Hannakaisa Lindqvist, Adeyemi Adebiyi, Jun Meng, Pablo Saide, Marcelo Chamecki, Yoshihide Takano, Yu Gu, and the late Kuo-Nan Liou, for insightful discussions.
Yue Huang received support from the Columbia University Earth Institute Postdoctoral Research Fellowship (2021–2023 fellow) and NASA (grant no. 80NSSC19K1346), awarded under the Future Investigators in NASA Earth and Space Science and Technology (FINESST) program. Jasper F. Kok received support by the NSF (grant nos. 1552519 and 1856389) and the Army Research Office (grant no. W911NF-20-2-0150). Masanori Saito received support by the Texas A&M University internal fund (grant no. 02-132503-00006). Olga Muñoz received support by the Agencia Estatal de Investigación (grant nos. RTI2018-095330-B-100, P18-RT-1854, and SEV-2017-0709).
This paper was edited by James Allan and reviewed by Lei Bi and Qixing Zhang.