Scattering matrices of mineral dust aerosols: a refinement of the refractive index impact

Dust, as one of the most important aerosols, plays a crucial role in the atmosphere by directly interacting with 10 radiation, while there are significant uncertainties in determining dust optical properties to quantify radiative effects and to retrieve their properties. Both laboratory and in situ measurements show variations in dust refractive indices (RIs), and different RIs have been applied in different numerical studies of model developments, aerosol retrievals, and radiative forcing simulations. This study reveals the importance of the dust RI for the model development of its optical properties. The Koch-fractal polyhedron is used as the modeled geometry, and the pseudo-spectral time domain method and improved 15 geometric-optics method are combined to cover optical property simulations over the complete size range. We find that the scattering matrix elements of different kinds of dust particles are reasonably reproduced by choosing appropriate RIs even using a fixed particle geometry. The uncertainty of the RI would greatly affect the determination of the geometric model, as a change in the RI, even in the widely accepted RI range, strongly affects the appropriate shape parameters to reproduce the measured dust phase matrix elements. A further comparison shows that the RI influences the scattering matrix elements 20 differently from geometric factors, and, more specifically, the P11, P12, and P22 elements seem more sensitive to dust RI. In summary, more efforts should be devoted to account for the uncertainties on the dust RI in modeling its optical properties, and the development of corresponding optical models can potentially be simplified by considering only variations over different RIs. Considerably more research, especially from direct measurements, should be carried out to better constrain the uncertainties related to the dust aerosol RIs. 25 https://doi.org/10.5194/acp-2019-812 Preprint. Discussion started: 25 October 2019 c © Author(s) 2019. CC BY 4.0 License.


Introduction
Atmospheric aerosols play an important role in the global radiation balance directly by scattering and absorbing incident solar radiation and indirectly by influencing cloud formation as CCN or IN (Chýlek et al., 1978;Sokolik et al., 2001;Yi et al., 2011). According to the IPCC Fifth Assessment Report (IPCC, 2014), aerosol is still one of the largest sources of uncertainty in the total radiative forcing estimation. As a major type of aerosol, mineral dust is widely distributed around the globe, 5 especially in arid regions. Mineral dust single-scattering properties are fundamental not only to quantify their radiative effects, but also to develop satellite retrieval algorithms from optical observations (Kahn et al., 2005;Huang et al., 2014;Xu et al., 2017a).
Several aerosol optical property databases, e.g., the Global Aerosol Data Set (GADS; Koepke et al., 1997) and the Optical Properties of Aerosols and Clouds (OPAC; Hess et al., 1998), have been built to meet the needs of aerosol remote 10 sensing and radiation studies for all aerosols as well as for particular kinds of aerosols (Meng et al., 2010;Bi et al., 2018a;Liu et al., 2019). However, for simplification, the optical properties of atmospheric aerosols are often investigated by assuming a relatively simple model, e.g., using spheres and spheroids, and this simplification has resulted in obvious errors (Mishchenko et al., 1997;Feng et al., 2009). For example, the measured phase functions of dust particles are clearly different from the phase functions of spherical particles at sideward and backward scattering angles (Koepke and Hess, 1988;15 Dubovik et al., 2002;Nousiainen, 2009). Databases with better accuracy in characterizing dust optical properties can contribute to many aspects, such as radiative forcing calculations and remote sensing applications. (Mishchenko et al., 1997;Dubovik et al., 2002;Ge et al., 2011;Merikallio et al., 2011), ellipsoids (Bi et al., 2009;Meng et al., 2010;Kemppinen et al., 2015), and superellipsoids (Bi et al., 2018b). Additionally, more complex and irregular particles have also been considered, e.g., spatial Poisson-Voronoi tessellation (Ishimoto et al., 2010), Gaussian random field (GRF) particles (Grynko et al., 2013), Koch-fractal particles (Liu et al., 2013;Jin et al., 2016), and nonsymmetric hexahedra (Bi et al., 2010;Liu et al., 2014). These "irregular" geometries as well as spheroids can achieve close agreement with 5 measurements by using appropriate shape parameters or combining the results from multiple shapes, which indicates that certain geometries may be optically similar or equivalent with respect to scattering light. Nousiainen and Kandler (2015) found that scattering properties of a cube-like dust particle can be mimicked by those of spheroids with a suitable shape distribution. Liu et al. (2014) found that the surface roughness and irregularity also share an optical equivalence. Lin et al.
(2018) revealed that the scattering matrix elements of different types of dust particles can be achieved by changing only two 10 parameters to specify the geometry of the superspheroids. Such "optical equivalences" are important for practical applications using dust optical properties because we can use those from a relatively simple numerical model, instead those based on the exact and actual dust particles, for downstream remote sensing and radiative transfer applications. In addition to the particle geometry, there are also significant uncertainties related to the dust RI, which is often considered an inherent characteristic (Kahnert and Nousiainen, 2006;Stegmann and Yang, 2017), to which much less 15 attention has been devoted during the model development. Previous studies often assumed a fixed RI during simulations of the corresponding optical properties at the interested incident wavelength. The real part (Re) of dust RI is normally set to approximately 1.5, and the imaginary part (Im) is normally set between 0.001 and 0.01 at visible wavelengths, except for hematite and magnetite (Sokolik and Toon, 1999;Volten et al., 2001;Kandler et al., 2007;Meng et al., 2010). Previous studies using both measurements and numerical investigations reveal that a relatively large variation in the RI of dust 20 materials in different regions or with different components does exist (Meng et al., 2010;Bi et al., 2011;Stegmann and Yang, 2017). Kemppinen et al. (2015) revealed that the retrieved dust RIs based on the comparisons of scattering matrices between simulations and laboratory measurements deviate from the true dust RIs. Figure 1 shows some examples of the RIs around the visual wavelengths, and we illustrate values from various different studies, i.e., those from two well-accepted optical properties databases: the Global Aerosol Data Set (GADS, Koepke et al., 25 mineral dust particles and found that the corresponding optical properties represent those from measurements. Figure 2 shows three examples of third-generation Koch-fractal particle geometries. The sequential number of the Koch-fractal generation indicates the complexity of the surface structure, and an irregular ratio defines the random movements of higher-order polyhedra to specify particle irregularity. Irregular ratio (IR) is a real number within the range [0, 0.5] to specify the random movement of the positions of successor-generation tetrahedra apexes to generate irregular 5 particles. A larger IR makes the Koch-fractal geometry surfaces more irregular and asymmetrical. Liu et al. (2013) introduced the aspect ratio (AR, the ratio of height to width) to generate prolate or oblate particles. Macke et al. (1996) and Liu et al. (2013) include more details on the definition of the Koch-fractal particles. In Figure 2, the Koch-fractal particle on the left is the nearly regular particle with an AR of 1.0 (1.06 for a regular particle) and an IR of 0. The middle particle has an IR of 0 but an AR of 2.5, and the particle on the right, which is the most irregular particle, has an AR of 2.5 and an IR of 0.3. 10 Previous studies indicate that geometries with proper irregularity have better potential to characterize the geometric features of several types of actual dust particles. To constrain the geometric variations considered, all particles will be the third-generation Koch-fractal particles with an IR of 0.3, and we will only consider different ARs in this study.
To account for the irregular geometries, multiple numerical models are available to calculate the single-scattering properties of nonspherical particles (Yang and Liou, 1996a;Mishchenko et al., 1997;Yurkin and Hoekstra, 2011;Bi et al., 15 2013). Following Liu et al. (2013), this study uses a combination of the pseudo-spectral time domain method (Liu, 1997;Liu et al., 2012) and the improved geometric-optics method (Yang and Liou, 1996b;Yang and Liou, 1998;Bi et al., 2014) to cover the required range of dust size parameters at visible incident wavelengths. For the irregular fractal particles, we use r, i.e., the radius of a volume-equivalent sphere, to define their sizes, so the size parameter x is defined as x=2πr/λ (with λ being the wavelength). Simultaneous size measurements by the AGLSD have sample sizes ranging from 0.076 µm to 105 µm 20 Muñoz et al., 2012), so we perform numerical simulations within the same range. The pseudo-spectral time domain method is applied to deal with the optical properties of geometries with size parameters up to 30, and those with size parameters over 30 are calculated by the improved geometric-optics method (Liu et al., 2013). For the computations of the PSTD, the optical properties of randomly oriented particles are averaged over those from 128 different orientations, which result in relatively smooth scattering matrix elements. After integration of the optical properties over the 25 simultaneously measured dust size distributions given by the AGLSD, the resulting bulk scattering matrix elements of certain RIs and particle geometries can be compared with the AGLSD measurements, and the agreements between the measurements and simulations are used to specify the potentials of the corresponding methods.
The square values of the differences between the simulated and measured results are used as the indicator to quantify the differences between the simulations and observations. The difference d is defined for P11 as Eq. (1): 5 where 00 789 ( ) is the measured P11 element at scattering angle θ and 00 ;<7= ( ) is for simulated model. Note that the AGLSD provides the scattering matrix elements with scattering angles between 5° and 173°. The numerical model that gives the smallest d will be defined as our optimal model for each dust sample. Actually, we also compared the differences among other scattering matrix elements, and the optimal case is mostly consistent with the one considering only P 11 . As a result, we 10 try to keep the evaluation simple, and use only d as a criterion.
The scattering matrices of feldspar, quartz, loess, Lokon (volcanic ash), and red clay from the AGLSD are considered as the references, and again, this study emphasizes the role of the RI of dust. These actual dust particles have different compositions, shapes, and size distributions, which cause their unique optical properties. Therefore, by considering multiple kinds of dust particles, we attempt to demonstrate that the effects of the RI generally hold. Furthermore, the influences of 15 particle geometries can hardly be isolated or avoided during the study, so the roles of the RI and geometry will be compared.
Noted that the phase functions will be presented by normalizing P11(30°) to 1, i.e., showing P11(q) ⁄ P11(30°), and the other nonzero scattering matrix elements are normalized with respect to P11.

The impact of the refractive index
As discussed in Section 1, large variations in the RIs at visible wavelengths do exist for dust particles in different regions due 20 to the differences in their components. To take advantage of the numerical investigation, we consider relatively larger ranges of RIs. The Re ranges from 1.4 to 2.2 in steps of 0.1 (9 values), and values from 10 -4 to 10 -2 in steps of 10 -0.5 (in logarithmical scale, i.e., 5 values) are used for the Im. We mostly focus on the optical properties at an incident wavelength of 633 nm, and the spectral consistency will be briefly discussed at the end of Section 3. Third-generation Koch-fractal particles with an AR of 2.5 and an IR of 0.3 are applied. Figure 3 clearly shows that different scattering matrix elements have distinct sensitivities to the changes in the Re. The effects of Re on the normalized phase function P11 ⁄P11(30°) mainly appear in the sideward and backward directions, and the scattering at scattering angles larger 5 than 30° becomes stronger as Re increases. With an increase in Re, the P12 ⁄P11 becomes closer to zero, and the P22 ⁄P11 departs from 1. The differences for the other three elements, i.e., P33 ⁄P11, P43 ⁄P11, and P44 ⁄P11, are less significant. Another noteworthy phenomenon is that the differences between the computed scattering matrix elements with Re between 1.4 and 1.6 are more obvious than those with Re values of 2.0 and 2.2, illustrating that the scattering matrix elements are more sensitive to the changes in Re when the value of Re is relatively small (e.g., 1.4). 10 Figure 4 is similar to Figure 3 but for results with different Im values. Im directly affects particle absorption, but its impact on particle scattering properties cannot be ignored. We consider a dust sample with relatively larger sizes to better demonstrate the effect of the Im. The bulk scattering matrix elements are obtained based on the size distribution of Lokon samples, which have an effective radius of 7.1 µm. Again, different scattering matrix elements have distinct sensitivities to the change in the Im, and the P 11 , P 12 , and P 22 elements are more sensitive to the Im. With increasing Im, the P11 ⁄P11(30°) 15 decreases at scattering angles from 30° to 180°, indicating weaker side and backward scattering. The P22/P11 increases as the Im increases. The P33 ⁄P11, P43 ⁄P11 and P44 ⁄P11 show less variation for different Im values. The scattering matrix elements show similar variation as Re increases or Im decreases. Generally, the scattering matrix elements change in the same directions as the Re increases or the Im decreases.
Figures 3 and 4 clearly show the impacts of the RIs on the modeling particle scattering matrix elements. With the size 20 distribution measured simultaneously, the RI and geometry both remain variables for the numerical studies. In contrast to previous studies with a fixed RI but with variable particle shapes (Merikallio et al., 2011(Merikallio et al., , 2013Ishimoto et al., 2010;Tang and Lin, 2013;Bi et al., 2010;Nousiainen and Kandler, 2015), this study tests whether the scattering matrix elements of different dust aerosols can be reproduced by models with a fixed particle shape but different RIs. Figure 5 compares the simulated scattering matrices of five dust species with measurements: feldspar, quartz, loess, red 25 clay, and Lokon (from left to right respectively). For the modeling results, a fixed geometry, i.e., the third-generation Koch-fractal particle with an AR of 2.5 and an IR of 0.3, is used for all simulations. Most of the previous numerical and observational studies suggest that the Re values lie between 1.5 and 1.6, and the scattering matrix elements are more sensitive to the Re change when the Re values are relatively small. Therefore, we include an additional Re of 1.55 in this study, which results in a total of 50 complex RIs (10 Re and 5 Im values). The blue shaded regions in Figure 5 indicate the 5 variations in the simulated matrix elements with the 50 RIs, and the red curves correspond to the optimal cases that give the minimum d among the 50 cases. Even with a fixed geometry, the simulated results of the five dust samples can achieve reasonable agreement with the measurements, especially for the P11 ⁄P11(30°), P12 ⁄P11, and P33 ⁄P11. For feldspar sample, P11 ⁄P11(30°), P12 ⁄P11, P33 ⁄P11, and P44 ⁄P11 of the optimal case agree closely with the measurements. Differences are only noticed for P22 ⁄P11 at the scattering angles from 60° to 150° and the P43 ⁄P11 from 75° to 150°. Similar results are obtained for quartz 10 and loess samples. The optimal results for red clay sample are less consistent with the measurements when compared with the results for the three samples above. Certain deviations between the computed and measured results appear at the forward direction for every nonzero matrix element of red clay except for the P11 ⁄P11(30°). Furthermore, RI of the corresponding optimal case for red clay sample is also obviously different from these discussed above, i.e. 1.8 for the Re and 10 -2 for the Im.
The computed results for Lokon particles achieve a relatively accurate agreement with the measurements with a Re much 15 larger than expected values, i.e., 2.2. However, the reproductions of the forward directions of P12 ⁄P11 and P43 ⁄P11 are not satisfactory. Most of the RIs obtained for the optimal cases have a real part of 1.5-1.6 and an imaginary part between 10 -4 and 10 -3 , consistent with generally accepted values and those suggested by the AGLSD. Table 1 lists the estimated RIs of five types of dust given by the AGLSD and the corresponding optimal RIs based on the particular geometry. Both the simulated and measured P12 ⁄P11 show considerable variations, and the simulated results match the measurements by mainly changing 20 the real part of the RI. However, the computed and measured P22 ⁄P11 show obvious differences. Both the simulated and measured P33 ⁄P11 show less variation, indicating that P33 ⁄P11 is less sensitive to the changes in the RI, and the simulated results with almost any RI satisfactorily agree with the measurements. The P44 ⁄P11 show similar features to the P33 ⁄P11, but the consistencies between the numerical results and the measurements are slightly worse than those of the P33 ⁄P11. Generally, the scattering matrices of different dust samples can be reproduced by applying proper RIs with a fixed geometry, although 25 the differences between the simulated and measured quantity of some particular elements (e.g., the P22 ⁄P11) are noticeable, which is the same as models considering geometric variations. Figure 6 compares the computed and measured scattering matrices of feldspar at two different incident wavelengths, in which the blue and red colors represent the results at incident wavelengths of 633 nm and 442 nm, respectively. Again, the numerical results are based on the fixed Koch-fractal geometry and 50 different RIs, as mentioned above. The optimal 5 numerical results show similar agreement with the measurements at the two wavelengths, as discussed above, i.e., close agreement for the P11 ⁄P11(30°), P12 ⁄P11, P33 ⁄P11, and P44 ⁄P11 while relatively larger differences for the P22 ⁄P11 and P43 ⁄P11.
Furthermore, the spectral differences regarding the measured P22 ⁄P11 are not shown by the simulated results, while other elementst show little spectral differences or agree with the simulations. The optimal cases for both wavelengths correspond to the same RI of 1.55+10 -3 i, which indicates relatively small wavelength independence of feldspar RI. In other words, the 10 Koch-fractal particle applied has a clear spectral consistency for modeling dust optical properties at multiple wavelengths, which can hardly be achieved by spheroid models (Merikallio et al., 2011;Dubovik et al., 2006;Lin et al., 2018). We also compare the spectral performance for the red clay and quartz scattering matrices, and similar results (the same optimal RI at the two incident wavelengths) are obtained.
However, the results for loess and Lokon are slightly different. Figure 7 illustrates the results for the loess sample as an 15 example. The optimal results at wavelengths of 442 nm and 633 nm correspond to RIs of 2.2+10 -2 i and 1.6+10 -4 i, respectively. Additionally, the consistencies of the computed and measured results at the wavelength of 633 nm are slightly better than those at the wavelength of 442 nm, especially for the forward directions of the P12 ⁄P11 and P43 ⁄P11. For Lokon, the same real part of RI (Re = 2.2) is obtained at the two wavelengths, while the imaginary parts are slightly different (10 -3 i at 442 nm and 10 -3.5 i at 633 nm). This indicates that loess and Lokon may have stronger spectral differences with respect to 20 their optical properties, which have to be considered for downstream radiative studies and remote sensing applications.

Refractive index vs. geometry
The importance of particle geometry in modeling dust optical properties has been well studied (Bi et al., 2010;Osborne et al., 2011;Lin et al., 2018), and Section 3 indicates the clear role of the RI. Thus, with particle size relatively well constrained, it becomes interesting to investigate whether the geometry or RI plays the same or different roles in modeling the dust optical properties for remote sensing and radiative forcing studies.
We first test whether different presumed RIs influence the determination of particle geometries for developing dust optical models. Figure 8 gives the measured and simulated scattering matrix elements of quartz, and the simulated results of Koch-fractal particles with three different geometries (different ARs only) and two different RIs are illustrated. The particles 5 with ARs of 0.25, 1.0, and 3.0 and the RIs of 1.5+10 -3 i (the red curves) and 1.7+10 -3 i (the blue curves) are used. If the quartz RI is assumed to be 1.5+10 -3 i for the numerical simulations, the modeled results based on the Koch-fractal particles with an AR of 1.0 agree most closely with the measurements (for almost all six elements), and those with larger (3.0) or smaller (0.25) ARs both depart from the measurements. However, if the RI is assumed to be 1.7+10 -3 i, then the results based on the particles with ARs of 0.25 and 3.0 agree more closely with the measurements, except for the P22 ⁄P11. Clearly, Figure 8 indicates that if 10 different RIs are assumed, then different geometries must be applied to represent the scattering properties of actual aerosols.
Similar results are obtained for the loess and red clay samples as well (not shown here). These comparisons illustrate that the RI can significantly influence the determination of appropriate geometries in modeling studies of dust optical properties.
Furthermore, we directly compare the roles of the RI and geometry in reproducing dust scattering matrices. To assess the effects of the geometry, third-generation Koch-fractal geometries with different ARs and a fixed IR of 0.3 are tested. 15 Because the AR shows the most significant influences on the scattering properties for our Koch-fractal particles, 10 different ARs (0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0) are considered in the tests. The RI is fixed at 1.6±10 -3.5 i (close to the RIs of feldspar, quartz, loess, and red clay obtained for Figure 5). For comparison, the effects of different RIs will be illustrated by considering particles with the fixed geometry used above. Figure 9 illustrates the scattering matrices of Lokon as well as the simulated results for particles with different ARs and 20 RIs. The blue curves indicate the optimal case (AR=1.0) among those with different ARs and the same RI, and the red curves are for the optimal case with an RI of 2.2+10 -3.5 i among those with different RIs. First, we discuss the results for P11. The results for particles with different geometries, i.e., ARs, but a fixed RI are illustrated by the blue areas. With the RI close to the widely accepted values, the results based on any geometry differ from the measurements, especially for the values with scattering angles larger than 90°. However, the red areas can cover the measurements at the edge and indicate that simulated 25 results with an extreme RI can better reproduce the scattering matrix of Lokon. For other elements, the optimal results, i.e., the red and blue curves, give similar agreement with the measurements, especially for P33 ⁄P11, P43 ⁄P11, and P44 ⁄P11. Overall, for the Lokon measurements, a more reasonable RI has to be used to reproduce their optical properties, and this may be the reason why few results on modeled Lokon samples have been previously published. The other features illustrated by Figure   9 are the coverage differences among the red and blue regions, which indicate the sensitivities of the particle geometry and 5 RI. For the P11, P12 ⁄P11, and P22 ⁄P11, the red areas clearly cover the blue ones, indicating that particles with different RIs result in larger variations in the corresponding elements than those with different geometries. The P33 ⁄P11 and P43 ⁄P11 for particles with either different geometries or different RIs show similar coverages, while the particle geometry may lead to larger variations in the P44 ⁄P11. These differences may become useful during the development of numerical dust optical models based on observed results such as the AGLSD. 10 Figure 10 is similar to Figure 9, except for the red clay particles. The blue curves correspond to results with an AR of 0.25, and the red curves correspond to those with an RI of 1.8+10 -2 i. Comparing the two optimal cases within the different ARs and RIs, the optimal results among particles with different ARs achieve a better consistency with the measurement for P11, P22 ⁄P11, P33 ⁄P11, and P44 ⁄P11, whereas the P12 ⁄P11 results from the RI optimal case is slightly better. The P43 ⁄P11 from both cases differs from the measurements. For this case, the agreement between the modeled and measured results is clearly 15 improved by changing the particle geometry. Similar to Figure 9, Figure 10 also illustrates that the changes in the geometry and RI can provide the simulations for different scattering matrix elements with different degrees of variation.
For the results of the feldspar particles in Figure 11, the most notable feature is that the two optimal cases with different variables are highly consistent; the reproductions of the matrix elements (except P22 element) are quite successful, and the simulated P22 ⁄P11 of the two optimal cases both deviate from the measurement with the scattering angles between 60° and 20 140°. For feldspar, i.e., the most extensively studied dust sample among the AGLSD (Bi et al., 2009;Dubovik et al., 2006;Liu et al., 2013;Lin et al., 2018;Merikallio et al., 2011;Volten et al., 2001), multiple models with appropriate combinations of the particle RI and geometry all result in close agreement with measurements. Reasonable results can be obtained by merely changing the RI even if the geometry is relatively different from reality and vice versa.
Obviously, both RI and geometry significantly affect mineral dust optical properties but quite differently, and, even 25 without consideration of the influence of particle size, an accurate RI has to be determined to develop an appropriate dust geometric model, and vice versa. However, if only an optically equivalent model at a single wavelength or a limited number of wavelengths is required, our results indicate that either RI or geometry can be treated as a variable while fixing the other.
Thus, instead of constructing dust model by building different geometries (e.g., Mishchenko et al., 1997;Bi et al., 2010;Liu et al., 2012;Lin et al., 2018), it is also potentially possible to consider only results from a fixed particle geometry but with 5 various RIs. The later (fixing a geometry and changing only RI) may be more convenient, because the RI can be defined more quantitatively.

Conclusions
This study investigates the role of the RI in modeling the dust scattering matrix elements. Instead of reproducing the dust scattering properties by building one or a group of nonspherical geometries at a fixed RI (that may or may not be an accurate 10 RI due to its uncertainties), we emphasize the sensitivities of the scattering matrix on the particle RI. By simply changing the RI during the numerical modeling, it is possible to characterize the optical properties of different dust particles, even if the model geometry is fixed. As a result, it becomes possible to simplify the model developments for different mineral dust particles. To be more specific, instead of constructing and testing various geometric models, using results from particles with different RIs but a fixed geometry can also be a solution to calculate scattering properties of dust particles at different 15 wavelengths, which would be more flexible and computationally efficient.
As expected, if different RIs are considered for dust optical property simulations, the appropriate geometry that leads to the best agreement to the observation will change accordingly. By comparing the sensitivities of the dust scattering matrix elements, it is noticed that the reproductions of the scattering matrix elements of different dust particles respond differently to the change in the RI and geometry. With a known particle size distribution, the scattering matrix of some kinds of dust 20 (e.g., feldspar, quartz, and loess) can be well reproduced by adjusting either the RI or the geometry with the other parameters fixed, but those of other dust particles (e.g., red clay and Lokon) can only be reproduced by applying an extreme and fixed geometry or RI. As a result, more efforts should also be devoted to better constraining the particle RI during the development of aerosol optical properties for remote sensing and radiative transfer applications. Last but not least, to better constrain either particle RI or geometry for dust optical property studies, more observations on dust microphysical and optical properties should be considered.