| 22 Oct 2025
Ground-based MFRSR UV-Vis spectral retrievals of Saharan dust absorption at Izaña Observatory
Abstract. This paper presents a multi-instrument synergistic technique to retrieve atmospheric dust aerosol columnar effective imaginary refractive index (k), single scattering albedo (SSA), and absorption aerosol optical depth (AAOD). The technique combines: (a) aerosol information derived from the narrow field-of-view measurements by filter sun-moon-sky radiometer within the Aerosol Robotic Network (AERONET): spectral aerosol optical depth (AOD) and inversion properties; (b) the total, direct, and diffuse sky irradiance measurements from UV- and Vis-Multifilter Rotating Shadowband Radiometers (MFRSR); (c) trace gas columns from satellite measurements (OMI and OMPS). The approach is demonstrated on the data collected at the Izaña Atmospheric Observatory (IZO), located at an altitude of 2.4 km on Tenerife Island, a unique site for Saharan dust column optical properties retrievals due to very clean background condition for calibrating the instrument. This multi-instrument synergy enables consistent column absorption retrievals from ultraviolet (UV) to visible (VIS) wavelengths, while effectively accounting separately for aerosol and gaseous (Ozone-O3, and Nitrogen Dioxide-NO2) absorption. The MFRSR calibration procedure relies on observations acquired on cleaner days (AOD<0.1 at 440 nm) to eliminate the observed dependency of the calibration constant on increasing dust aerosol loading leading to an inefficient correction for the forward scattering (aureole effect). The retrieval algorithm 1) integrates the temporally collocated AERONET-retrieved particle size distribution and the real part of the refractive index into the radiative transfer simulations, while accounting for the pre-defined spheroidal shape distribution of the dust aerosols, and 2) fits the measured ratio of diffuse to direct-normal irradiance for discrete wavelengths (325 nm to 440 nm) to the pre-calculated, on-the-fly look-up table to retrieve the spectral imaginary part of the refractive index. The retrieved SSA at 440 nm shows good agreement with AERONET inversions, mostly within ±0.03 for AOD>0.2, and ±0.02 at higher AOD (>0.4). This close correspondence confirms the consistency between the two fundamentally distinct inversion techniques and enhances confidence in the concurrent MFRSR UV wavelength inversions. We present a multi-year (2019–2023) MFRSR aerosol absorption record revealing enhanced dust absorption at UV wavelengths with noticeable intraseasonal and interannual variabilities, which are indicative of a varying composition of minerals (iron oxides) in the dust. The spectral aerosol absorption effects reduce the amount of surface-reaching UV radiation and slow down tropospheric photochemistry, which can have implications for air quality, human health, and ecosystem dynamics. The ongoing AERONET and MFRSR measurements currently made at the Santa Cruz ground-level site on Tenerife Island will continue to produce a unique, long-term ground-based UV spectral Saharan dust absorption dataset, providing a valuable reference for evaluating space-based UV aerosol absorption retrievals from instruments such as DSCOVR-EPIC, S5P-TROPOMI, and the most recently launched PACE-OCI. In addition to deriving spectral absorption properties, the enhanced sensitivity of UV measurements to the dust spectral absorption, demonstrated with the MFRSR inversion in this work, can be exploited for inferring the mineralogical composition of the dust aerosols, which is critical to improving the dust representation in Earth System Models.
Competing interests: The lead author and at least one of the (co-)authors are members of the editorial board of Atmospheric Measurement Techniques.
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General comments
Article presents a multi-instrument L2 synergy to estimate absorption of desert dust particles from ground-based observation in the UV spectrum, providing data on dust that are of high demand by the scientific community. Article is well structured, with good English, however some methodology description parts could be improved. I also have several concerns about some statements, notably related to the assumptions of shape distribution used on the dust particles, and its impact on the retrievals not fully justified. Below I address these issues in details, and would recommend a major revision to streamline the work presentation, and make the teams’ impressive results really shine.
Major comments
Section 3.2:
I found the whole approach of particle shape justification rather questionable.
First of all, authors suggest different shape distribution, that is quite badly justified, instead of one that was explicitly designed for the observations they use in their synergetic retrievals. Satellites and ground-based observations work in different scattering angle ranges.
Dubovik et al., 2006 and AERONET uses spheroidal model ONLY in a combination with spheres, where spheroidal particles represent an extreme case of non-spherical particles, and the fraction of spheres is the parameter that is fitted from sky observations and it is never 0. Why not use that one observed by AERONET, instead of basically turning shape distribution inside out and creating inconsistency between different parts of synergy?
Lines 300-315: I would also insist that aspect ratio distributions of a spheroidal model doesn’t necessarily (actually quite definitely not) have something to do with the aspect ratios of actual particles, it is an extra dimension in the model, that allows modelling scattering with something that is compatible with spheres but at the same time is not spherical, the only connection to the reality it has are the observations it fits. And these were scrumptiously selected and precisely in-situ measured scattering matrices (all six elements of them). Not like OMI that possibly has some sensitivity to P11 elements in forward and backward direction. I would also like to emphasise that the model of dust described in Lopatin et al., 2021, uses same shape distribution of spheroids as suggested in Dubovik et al., 2006 and was successfully used in a large variety of satellite retrievals, including multi-angular polarimeters (e.g. Chen et al., 2020), and S5p/TROPOMI (which shouldn’t be that far from OMI) (Chen et al., 2024; Litvinov et al., 2024) and ground-based observations as well their synergetic combinations (Litvinov et al., 2025) without any modifications, and no biases over dust dominated areas were observed so far. No matter what referenced studies claim, applicability of dust samples shape analysis to an idealisation developed to be applicable to a large bulk of non-spherical dust particles, in general, has little to no support.
Additionally, I found usage of OMI as justification of dust shape distribution rather confusing, above it is indicated that OMI is used only for trace gases’ corrections. The impact of AERONET using one shape distribution, OMI using another, retrieving gas correction using these assumptions, and then SSA retrieved from another observation should be better studied, there are possible biases that propagate from one instrument to another due to the differences in such assumptions, please discuss.
Authors did compare retrievals with AERONET shape distribution (under 0% spheres assumption in the mixture), and frankly, statistically the observed differences are not very convincing, it is a rather small dataset with validation changes not very significantly different from the retrieval accuracy. More on that below.
Lines 366-370: “A total of 21 bins of aspect ratio distribution ranging from about 0.4 (oblate) to about 2.5 (prolate) are prescribed with associated weighting factors shown as the red curve in Figure 5. The 22-bin volume size distribution of AERONET was used as direct input to the SDLS package. Using these parameters as input, the phase matrix elements were simulated at a total of 181 scattering angles at a 1-degree resolution.”
Did authors compared at 440 nm with AERONET provided phase matrices? These will include a “proper” mix of spheres and spheroids that fits the almucantar observations. Also, it would be a nice exercise to see the AERONET provided phase functions for several cases and ones calculated using same refractive index, PSD but using the suggested shape distribution. Ideally simulate almucantar observations to compare the fits. If fitting difference are not that substantial, it also can provide additional justification of the proposed method. Possibly fits of the shorter WL will be even better with updated shape distribution.
In general, it would be also nice to see validations of AODs estimated using the retrieved absorption, and assumed size/shape distribution and real refractive index in UV with AERONET observations (e.g. 340 and 380), I believe Izana should had several CIMELs capable of providing such data.
Also, I got completely lost how LUT’s are generated/used. Are they dynamic and depend on AERONET due to the multiplication factors? Are they static and calculated to a specific grid? Please, provide more details.
Minor comments
Line 246: “inversion parameters of PSD and the real part of the refractive index” it is not clear how the real refractive index is extrapolated to UV, please clarify
Line 373: “The vertical profile of aerosols is assumed to follow the Gaussian distribution with the peak concentration at 3 km.” Any particular reason to use this profile? And 3km is above sea level or Izana station? And what half width was assumed?
Line 380: “The entire inversion procedure was applied to each of the five wavelengths of the MFRSR independently.”
Was spectral dependence controlled in any way? Are there any examples how spectral behaviour of such retrievals looks like? Is it reasonable? Has it spikes, does it have a trend? Would be nice to see plots of examples of full spectrum imaginary refractive index, combined with AERONET data just to have a glimpse what could be expected from dust using this technique.
Line 390: what retrieval is considered a “success”? Please, clarify. Are they treated case-wise or wavelenth-wise, for e.g.? If one channel “failed” is all retrieval discarted?
Lines 433 – 439, Line 550: I’m not sure such comparison is rather fair. First of all as mentioned above there should be certain persentage of spherical particles retrieved by AERONET, so the distribution won’t be exactly as in Dubovik 2006, and maybe be somewhat closer in resulting phase function to what authors suggest. Also it is not clear do they compare sucessed cases of their retrievals only or all of them, maybe choise of shape affect success rates? Also I’m confused how method using the same refractive index, same psd and as claimed same shape distribution as in AERONET (case a) shows bias with AERONET retrieval itself, I mean these SSA values retrieved under exactly the same assumptions, it is clear that the shape distribution can’t be the not only reason in that case.
Figure 12-13: Why wishers are so much bigger for July-August? Please discuss
Line 482-485: “the imaginary part of the refractive index and AAOD both exhibit a weak spectral trend in the visible to near-IR region (AERONET) but a distinct increasing trend towards shorter UV wavelengths—a typical and expected spectral absorption behavior of coarse-mode dust aerosols” if I understood correctly “multipication factors” in table 2 there’s little to no chance that method will retrieve imaginary part of refractive index below the one of AERONET, and it seems that a trend for decreasing absorption with wavelengths is kinda “bult-in” through these factors.
Table 2: It is not clear how “multiplying factor” are used actually these are iportant and not mentioned anywhere else. It is a significant flaw in method description. Also if imaginary part of refractive index is retrieved a a factor to AERONET it is not completely clear how LUTs are generated, are they individual for every case? Or it is the factos that are retrieved, please, provide a more comprehensive description of this part of the method. And why such specific selection of factors? They are quite different for the UV and blue for e.g.
Line 657: “The original FORTRAN code was translated to C/C++, as this work was initiated as part of translation of MAIAC’s (Lyapustin et al., 2021) polarized radiative transfer solver IPOL (Korkin and Lyapustin, 2023) from FORTRAN into C.”
It is not clear which translation is mentioned, was code manully re-written in C? FORTRAN and C share compliler and their translator makes same pseudocode for further compilation, this doesn’t affect the speed of execution.
Generally the whole Appendix part of the DLS package modifications looks a bit weird to me. Especially for a user of DLS package. It looks like the package wasn’t used in the optimal way, and instead of changing several parameters in the it was re-written… I presume the explicit permissions for such code use were provided.
Majority of statements are either not directly related to the DLS package performance, but rather to the use case that was not optimal, FORTRAN and C binds naturally so the whole C translation for the performance looks a bit superficial.
Lines 639-644: it is not clear why loading kernes was such an issue, since they can be loaded once and then every-minute retrieval be performed with all the matrices already loaded. I do understand that binary format is more practical and faster, but after all reading could be done only once per large retrieval sample. And if compared to radiative transfer computational efforts, kernel reading and even interpolations shouldn’t be such a performance issue… Besides authors keep saying that LUTs containing imag parts were used for the retrievals, i.e. multiple running and reading of phase functions kernels as well as RT calculations for different imaginary parts supposedly was done only once, and then LUTs were re-used, or I’m missing something, please provide more details on this.
I’m no expert in this, but I believe a clear statement that original DLS package re-use was done with explicit permission of its authors is required in this appendix or proper authorship affiliations should be provided in the linked repository. Otherwise it gives a rather weird feeling to say the least. For e.g. git repository contains binary kernels that contain transformed information from the text files of the original DLS package without any authorship affiliation nor licence mentioned, and due to this transformation (which to my understanding is not completely justified, see above) these can’t be automatically compared. To be frank, these are the essence of the package, non-spherical part being the important improvement in this study, and making these from scratch is not as easy as loading and interpolating between the already calculated nodes. And the only “link” with the kernels authors in repository with its authors is an image, representing a screen shot of the original article in the doc section… I encourage authors to make the coding contributions more transparent and suitable for automatic affiliation research. Ideally, publish the code that converted kernels to binaries with proper link to the original kernel repository.
Technical comments
Line 117: “multiple” I’d suggest replacing with “five”
Line 121: “these wavelengths”, are these 6 or 5?
Line 392: “higher AERONET SSA”, please provide wavelength, is it 440?
Figure 8: Consider making it double Y plot with AOD on the right, it is bit messy, too many fine text in color around points, quite hard to analyse.
Line 431: “440 nm to 325 nm” I would suggest “325 to 440” this way it will be clearer where trend increases.
Figure 11: Consider making text bigger, and what are these tiny numbers below?
Figure 12-13: Generally hard to follow spectral and temporal dependencies and the font is rather small and hard to read, is there a better way to present these data?