Different strategies to retrieve aerosol properties at night-time with GRASP algorithm

This study evaluates the potential of GRASP algorithm (Generalized Retrieval of 20 Aerosol and Surface Properties) to retrieve continuous day-to-night aerosol properties, both column-integrated and vertically-resolved. The study is focused on the evaluation of GRASP retrievals during an intense Saharan dust event that occurred during the Sierra Nevada Lidar aerOsol Profiling Experiment I (SLOPE I) field campaign. For daytime aerosol retrievals, we combined the measurements of the lidar ground-based from EARLINET (European Aerosol 25 Research Lidar Network) station and sun/sky photometer from AERONET (Aerosol Robotic Network), both instruments co-located in Granada (Spain). However, for night-time retrievals three different combinations of active and passive remote sensing measurements are proposed. The first scheme (N0) uses lidar night-time measurements in combination with the interpolation of sun/sky daytime measurements. The other two schemes combine lidar night30 time measurements with night-time aerosol optical depth obtained by lunar photometry either using intensive properties of the aerosol retrieved during sun/sky daytime measurements (N1) or using the moon aureole radiance obtained by sky camera images (N2). Evaluations of the columnar aerosol properties retrieved by GRASP are done versus standard AERONET retrievals. The coherence of day-to-night evolutions of the different 35 aerosol properties retrieved by GRASP is also studied. The extinction coefficient vertical profiles retrieved by GRASP are compared with the profiles calculated by Raman technique at https://doi.org/10.5194/acp-2019-681 Preprint. Discussion started: 5 September 2019 c © Author(s) 2019. CC BY 4.0 License.

column-integrated and vertically-resolved. The study is focused on the evaluation of GRASP retrievals during an intense Saharan dust event that occurred during the Sierra Nevada Lidar aerOsol Profiling Experiment I (SLOPE I) field campaign. For daytime aerosol retrievals, we combined the measurements of the lidar ground-based from EARLINET (European Aerosol 25 Research Lidar Network) station and sun/sky photometer from AERONET (Aerosol Robotic Network), both instruments co-located in Granada (Spain). However, for night-time retrievals three different combinations of active and passive remote sensing measurements are proposed.
The first scheme (N0) uses lidar night-time measurements in combination with the interpolation of sun/sky daytime measurements. The other two schemes combine lidar night- 30 time measurements with night-time aerosol optical depth obtained by lunar photometry either using intensive properties of the aerosol retrieved during sun/sky daytime measurements (N1) or using the moon aureole radiance obtained by sky camera images (N2).
Evaluations of the columnar aerosol properties retrieved by GRASP are done versus standard AERONET retrievals. The coherence of day-to-night evolutions of the different 35 aerosol properties retrieved by GRASP is also studied. The extinction coefficient vertical profiles retrieved by GRASP are compared with the profiles calculated by Raman technique at

Introduction
Knowledge of the atmospheric aerosol optical and microphysical properties is important due to their different effects on the Earth-atmosphere radiative budget (IPCC, 2013). The aerosol particles can scatter and absorb solar and terrestrial radiation. Earth-Atmosphere radiative forcing sign (warming or cooling) is sensitive to aerosol optical and microphysical properties 15 and their vertical distribution (e.g. Boucher et al., 2013). In addition, aerosol particles can act as cloud condensation and ice nuclei and, thus, can modify the development, microphysical properties and lifetime of clouds (e.g. Andreae et al., 2004;Boucher et al., 2013). Recent developments in remote sensing have allowed advancing in understanding aerosol globally, but the characteristic of each system do not allow either a complete characterization of day-to- 20 night, especially in aerosol microphysical properties (e.g. Perez-Ramirez et al., 2012).
Understanding day-to-night aerosol properties from remote sensing measurements is essential to advances in aerosol dynamics and changes, which eventually will serve for advancing in aerosol impact on air-quality and climate. Therefore, current efforts are in integrating different measurements that require advancing in the development of retrieval techniques. 25 During the last two decades, global and regional networks have been established to get a comprehensive, quantitative, and statistically significant database of atmospheric aerosols.
The Aerosol Robotic Network (AERONET; Holben et al., 1998) and East Asian SKYNET (Nakajima et al., 2007) use sun/sky photometer to provide aerosol column-integrated properties with high temporal resolution. These networks use retrieval techniques that allow the 30 characterization of aerosol microphysical properties (e.g. Nakajima et al., 1996;Dubovik and King, 2000). These networks were focused on daytime measurements but nowadays they are trying to add night-time aerosol measurements derived from lunar photometry. The developments in moon (Berkoff et al., 2011;Barreto et al., 2013Barreto et al., , 2016 and star photometry (e.g. Pérez-Ramírez et al., 2011Baibakov et al., 2015) allow the acquisition of nighttime measurements, however, these measurements are limited in the inversion algorithms to retrieve the aerosol microphysical properties (Pérez-Ramírez et al., 2015;Torres et al., 2017). 5 Lidar networks such as EARLINET (European Aerosol Research LIdar NETwork; Pappalardo et al., 2014), LALINET (Latin American LIdar NETwork;Guerrero-Rascado et al., 2016;Antuña-Marrero et al., 2017) and MPLNET (Welton et al., 2002) provide information about aerosol vertical distribution. However, many of the lidar systems operating in these networks are basic lidar systems which only have information on the backscatter elastic signals 10 and only allows the retrieval of the vertical profiles of the aerosol backscatter coefficient ( ) by the Klett-Fernald method (Fernald et al., 1972;Fernald, 1984;Klett, 1981Klett, , 1985 and corresponding aerosol extinction ( ) coefficient by assuming constant aerosol extinction-tobackscattering ratio, which is so-called lidar ratio (LR). On the other hand, more advanced lidar systems implement either Raman (e.g. Ansmann et al., 1992;Whiteman et al., 1992) technique 15 for independent retrievals of aerosol backscatter and extinction measurements. These multiwavelength lidar measurements allow use different inversion algorithms based on the regularization technique to retrieve vertical profiles of aerosol microphysical properties using 3 +2 configuration; that is multiwavelength lidar measurements of three backscatter and two extinction coefficients (e.g. Müller et al., 1999;Böckmann et al., 2001;Veselovskii et al., 20 2002). Nevertheless, the amount advanced lidar systems is considerably lower when compared with basic lidar systems, therefore the independent and measurements are sparse and mostly limited to night-time. In this context there is a lot of passive and active remote sensing measurements that alone do not allow enough information to retrieving advanced aerosol microphysical properties. However, integrating all these measurements in an appropriate 25 inversion scheme allows such retrievals and can even complete the number of unknown aerosol optical properties. Such integration is critical for retrieving vertical profiles where the information content for the retrievals is considerable low when comparing with classical sun photometer inversion (e.g. Veselovskii 2005). In the framework of EARLINET different inversion algorithm were developed, such as the LIdar-Radiometer Inversion Code (LIRIC; 30 Chaikovsky et al., 200830 Chaikovsky et al., , 2016) that uses as input AERONET retrievals and backscatter elastic signals, and the Generalized Aerosol Retrieval from Radiometer and Lidar Combined (GARRLiC; Lopatin et al., 2013) code which uses as inputs sun/sky radiance and backscatter lidar measurements that make the inversion more consistent (Lopatin et al., 2013).
Among these algorithms, we use in this study the recently developed Generalized Retrieval of Aerosol and Surface Properties algorithm (GRASP; Dubovik et al., 2014) which includes GARRLiC code. GRASP is a versatile and open-source algorithm (www.grasp-5 open.com) based in the concept of Dubovik and King (2000) algorithm which has been successfully used by AERONET during the last decades. GRASP algorithm is divided in two main independent modules: forward model and numerical inversion modules. The forward model is based on radiative transfer and aerosol models and it is a convenient tool for sensitivity and tuning studies (Dubovik et al., 2014;Torres et al., 2017). The numerical inversion module 10 is the main part of the core program, which includes general mathematical operations based on multi-term least square method (LSM) concept (Dubovik and King, 2000;Dubovik, 2004). The GRASP versatility allows the retrieval of aerosol properties through the combination of measurements from different instruments both column-integrated and vertically resolved. In fact, GRASP was successfully utilized for the retrieval of the aerosol properties using different 15 configurations and measurements, such as: polar nephelometer data (Espinosa et al., 2017); satellite images (Kokhanovsky et al., 2015); aerosol optical depth (AOD) and sky radiances (including polarization) (Fedarenka et al., 2016); spectral AOD and sky camera images (Román et al., 2017a); only spectral AOD (Torres et al., 2017); and the combination of aerosol optical depth (AOD), sky radiances and elastic lidar (Lopatin et al., 2013;Benavent-Oltra et., 2017) 20 or ceilometer profiles (Román et al., 2018). The aerosol properties retrieved by GRASP aerosol profiles have been used as input to radiative transfer models , to evaluate dust forecast models (Tsekeri et al., 2017) or to be assimilated in global models (Chen et al., 2018).
In this framework, the main objective of this paper is to propose and explore different 25 and novel strategies for the retrieval of vertically-resolved aerosol properties at night-time using GRASP algorithm combining remote sensing measurements as input data. Another goal is to quantify the accuracy of the retrieved night-time aerosol properties obtained by these strategies, classified in three schemes, using as reference independent aerosol measurements and products. To that end, the recent developments on lunar photometry which allows to derive 30 the night-time AOD from lunar photometer (Barreto et al., 2013(Barreto et al., , 2016 and the new studies with sky camera images that allow to obtain the normalized sky radiance from lunar aureole (Román et al., 2017a), open the possibility to explore the use GRASP algorithm combining these night-time measurements with elastic lidar data to study night-time microphysical and optical aerosol properties.
The paper structure is as follows: Section 2 and 3 give a brief description of the experimental site, instrumentation used and the dust event occurred during Sierra Nevada Lidar aerOsol Profiling Experiment I (SLOPE I) campaign. The different schemes used in GRASP 5 to retrieve the aerosol properties both day and night-time are described in Section 4. In section 5, the assessment of the aerosol column-integrated and vertically-resolved properties both day and night-time retrieved by GRASP is discussed. Finally, the conclusions are given in Section 6.

AGORA Observatory
The paper is mainly focus on the city of Granada (Spain). Granada is located in the Western Mediterranean basin and it is frequently affected by long-range transport of Saharan dust (Lyamani et al., 2005;Fernández et al., 2019;Soupiona et al., 2019) and biomass-burning, both from near sources  and large distances (e.g. Ortiz-Amezcua et 15 al., 2017;Sicard et al., 2019). Main local sources of anthropogenic aerosols are road-traffic and heating systems during the winter season (Lyamani et al., 2010). Under strong anticyclone conditions, the orographic situation with the city situated in a basin surrounded by mountains makes ventilation processes difficult and favors aerosol stagnation (Patrón et al., 2017).
The experimental measurements used in this study were collected in the AGORA 20 observatory (Andalusian Global ObseRvatory of the Atmosphere) in Granada. AGORA deployed instrumentation at three different stations at different altitudes. The principal station (UGR station) is located in the Andalusian Institute for Earth System Research/IISTA-CEAMA in Granada city where active and passive remote sensing instrumentation operated.
The other two stations are in Sierra Nevada Mountain range: Cerro Poyos station (37.11º N, 25 3.49º W, 1820 m a.s.l.) and Sierra Nevada station (SNS; 37.10º N, 3.39º W, 2500 m a.s.l.). In this study, we used the in-situ measurements from SNS station which is located about 25 km away (horizontally) from UGR station. The measurements of SNS station can allow for characterization of regional and long-range transport episodes and the validation of inversion algorithms used to retrieve aerosol optical and microphysical properties. The altitude difference 30 between UGR and SNS stations (~1.8 km) and the short horizontal distance make that the correlative measurements between both sites ideal in our objective of evaluating different GRASP scheme retrievals.
The measurements used in this work were acquired in the framework of the Sierra Nevada Lidar aerOsol Profiling Experiment I (SLOPE I) campaign. SLOPE I took place at AGORA from May to September 2016 with the objective to validate the vertically-resolved  (Román et al., 2018) and the characterization of the angular scattering of the Sahara dust aerosol by means of polar nephelometry (Horvath et al., 2018).
Thus, SLOPE I is ideal for our purposes of studying day-to-night aerosol microphysical properties retrievals.

Remote sensing measurements
The measurements of the remote sensing instrumentation of UGR station are used as input data in the different GRASP schemes (see Section 4). One of these instruments is a multiwavelength Raman lidar (LR331D400, Raymetrics S.A.) which is included in EARLINET network since 2005 and contributes to the ACTRIS research infrastructure. It is composed of a pulsed 20 Nd:YAG laser that emits at 1064 nm (110 mJ/pulse), 532 nm (65 mJ/pulse) and 355 nm (60 mJ/pulse) by means of the 2 nd and 3 rd harmonic generators. The receiving system has seven channels: three to measure the backscatter signal at emission wavelengths plus one additional channel to measure the cross-polarized signal at 532 nm; two channels at 387 and 607 nm for the detection of Raman scattering from N2 and an additional channel to detect the Raman 25 scattering from water vapour at 408 nm. Due to incomplete overlap, atmospheric information up to 500 m above the system is limited (Navas-Guzmán et al., 2011). A detailed description of this multiwavelength Raman lidar system can be found in Guerrero-Rascado et al. (2008, 2009).
Co-located with the lidar system, a sun/sky/lunar photometer Cimel CE318-T (Cimel 30 Electronique), included in the AERONET network, makes day and night-time measurements since March 2016. This photometer is equipped with a filter wheel (9 narrow filters) covering the spectral range between 340 and 1640 nm. During daytime, the sun/sky/lunar photometer performs measurements of sky radiance but also direct solar irradiance, which is used to derive the AOD; both kind of measurements can be used to retrieve detailed aerosol properties such as particle size distribution, complex refractive index (CRI) and single-scattering albedo (SSA) (Nakajima et al., 1996;Dubovik et al., 2006). This photometer is annually calibrated following 5 the AERONET methodology by ACTRIS/AERONET-Europe, the European branch of AERONET. Furthermore, this photometer has the capacity to measure the solar radiation reflected by the Moon during night-time, providing valuable information of atmospheric aerosols during whole day. Therefore, the sun/sky/lunar photometer provides the AOD at nighttime between first and third Moon quarters (Barreto et al., 2013(Barreto et al., , 2019. The calibration of the 10 CE318-T for AOD calculation at night-time has been done by the Lunar-Langley calibration method explained by Barreto et al. (2019). More details of the sun/sky/lunar photometer Cimel CE318-T and its operational functionalities are described by Barreto et al. (2016).
Furthermore, we used a sky camera SONA ('Sistema de Observación de Nubosidad Automático': Automatic Cloud Observation System) which provides hemispherical sky images 15 at day and night (González et al., 2012). This system is composed of a CCD camera with a fisheye lens providing RGB images which effective wavelengths at night scenarios correspond to 469, 533 and 608 nm (Román et al., 2017a). It was configured to take multi-exposure sequences of sky images. These sequences are used to obtain a high dynamic range (HDR) image (one each 5-minutes) which allows, after some correction processes, to obtain the 20 normalized radiances at lunar almucantar points (up to 20° in azimuth from the Moon) at the three effective wavelengths as showed by Román et al. (2017a). Sky cameras usually present a low signal to noise ratio. Thus, to calculate the moon radiances from sky camera images we need cases with high values of AOD (to enhance the scattered moon signal in the aureole) and high Moon extraterrestrial irradiance that restrict data availability to the period between the 25 first and last Moon quarters (Román et al., 2017a). In addition, in this work we applied some threshold to use the Moon radiance calculated from sky camera images: 1) The Moon zenith angle must be lower than 70º, 2) a minimum of 18 sky radiances with azimuth angles between 3º and 20º must be available for each effective wavelengths of the sky camera. A detailed explanation about the configuration, corrections and products obtained by this camera is 30 presented in Román et al. (2017a, b).

In-situ measurements
The in-situ measurements collected at SNS station are used to assess the aerosol properties, such as scattering coefficient ( ) and volume concentration (VC) retrieved by GRASP algorithm. The integrating nephelometer (model TSI 3563) measures the particle light scattering coefficients at three wavelengths (450, 550 and 700 nm) with 5-min temporal 5 resolution. A quartz-halogen lamp equipped with a built-in elliptical reflector illuminates over an angle of 7 to 170° the air sample (particle + gas) extracted by a small turbine blower at a constant flow of 30 L min −1 . The nephelometer measurements underestimate the scattering and backscattering coefficients due to the limits of the angular integration of the scattered light since a part of forward (0º-7º) and backward (170º-180º) signals are not measured. 10 Nephelometer data have been corrected for truncation and non-Lambertian illumination errors using the method described by Anderson and Ogren (1998). The APS measures number aerosol concentrations up to 1000 particles·cm -3 with coincidence errors inferior to 5% and 10% at 0.5 and 10 μm diameters, respectively. From these measurements, aerosol volume concentrations were obtained in the 0.05-10 μm radius range 25 with the 5-min time resolution. For that, Q-value=1 has been assumed for conversion from aerodynamic to mobility size distribution (Sorribas et al., 2015).

Dust event during SLOPE I campaign
This work focuses on an intense dust event that reached the south-eastern of the Iberian Peninsula during SLOPE I field campaign from 18 th to 21 st July 2016. The analysis of five-day  Given coherence among all measurements, we can affirm that the Saharan dust affected a wide area and measurements in UGR and SNS are both representative of such event. Thus, the conditions of this dust event allow the evaluation of vertical and columnar aerosol optical 20 and microphysical properties retrieved by GRASP algorithm both day and night-time.

GRASP retrieval schemes
In this section, we present in four schemes the different strategies used in GRASP algorithm for retrieving continuous day-and night-time atmospheric aerosol properties, both columnintegrated and vertical profiles. For daytime retrievals, (denoted as D), the scheme used in 25 GRASP is the proposed by Lopatin et al. (2013) which used as input data both lidar and sun/sky photometer measurements. On the other hand, we have proposed three different schemes to retrieve the aerosol properties during night-time, each scheme can be used depending on the available instrumentation and the conditions of the event.
The lidar data use in each retrieval (both for day-and night-time retrieval) corresponds 30 to preprocessed 30-minute averages of the raw signals for each wavelength. This preprocessing includes background noise subtraction and altitude correction, but other corrections also are applied as overlap correction, analog and photon-counting signals gluing and depolarization correction. To reduce the number of retrieved parameters and to remove the noise in lidar signals at higher altitudes, a logarithmical altitude/range scale with 60 points between a minimum and maximum altitudes is used as in Lopatin et al. (2013). More details of lidar data 5 preprocessing are described in Lopatin et al. (2013). In addition to the lidar signal measurements, each scheme uses different input data from different instrumentation, and hence the retrieval strategies and configurations differ between schemes. These configurations are summarized in Table 1 and described in the following subsections.
[Insert Table 1  available, but it only should be applied when the aerosol load and type is similar along nighttime.

N1
Currently, night-time AOD measurements, taken with the recently developed sun/sky/lunar

20
The third and last night-time scheme (N2 scheme) avoids any assumption as the previous schemes, assuming that intensive and extensive (as N0 scheme) aerosol properties do not change between day and night, or using a fixed CRI and spherical particle fraction (as N1 scheme). The N2 scheme uses as input data the elastic lidar, lunar aureole normalized sky radiances at 469, 533 and 608 nm derived by the SONA sky camera and the night-time AOD 25 at 440 (which is interpolated to 469 nm by Angström Exponent law using 440 and 675 nm), 675, 870 and 1020 nm. This scheme needs the elastic lidar, lunar photometer and sky camera measurements but it has the advantage that it is not dependent of daytime measurements and can retrieve extensive and intensive aerosol properties and hence is useful to detect changes on aerosol load or type along the night.

Columnar aerosol properties
For studying the coherence of daytime columnar-integrated aerosol properties retrieved by GRASP (using D scheme), such retrievals are compared with those provided by AERONET operational algorithm. Generally, the retrievals of Level 2.0 from AERONET Version 2 are used for this comparison, but for specific cases (i.e. AOD440<0.4) the SSA and CRI values of 5 Level 1.5 are used instead (Holben et al., 2006). For evaluating columnar aerosol properties retrieved by GRASP at night-time, we evaluate the smoothness and temporal coherence of the variation of the aerosol retrievals along the night and having as benchmarks the daytime retrievals both AERONET and GRASP D scheme.
The P1 and P2 periods present a situation with an apparent smooth variation of the 10 aerosol load but with the remaining of some intensive properties, identifying the type of aerosol, along the whole studied period (see Fig. 2). In this sense, the selected cases offer an appropriate situation for testing the proposed schemes for night-time aerosol retrievals, having in mind the smoothness of the aerosol evolution in spite of the ample change in the aerosol load. Hereafter, evaluations of aerosol parameters retrieved by GRASP using different input 15 data set (different schemes) are presented.

Columnar particle size distribution parameters
The columnar particle size distribution can be approximated as bimodal log-normals instead of binned size distributions. The bimodal log-normals can be described using six parameters: volume concentration (VCi [µm 3 /µm 2 ]), volume median radius ( [µm]) and standard 20 deviation ( ) for fine and coarse mode. Table 2 shows the average values (±standard deviation), for all available retrievals, of the size distribution parameters retrieved by GRASP using different configuration schemes and those provided by AERONET. Figure 4 shows the aerosol size distributions calculated from the parameters given in Table 2. Due to the drastic change in aerosol load (as indicated by AOD) between P1 and P2 periods, the results of GRASP 25 and AERONET retrievals are provided separately for these two periods.
[Insert Table 2 here] [Insert Figure 4 here] The aerosol size distribution parameters obtained using scheme D are consistent with AERONET products, with mean relative differences between GRASP and AERONET around 30 8% (26%), 12% (35%) and 8% (10%) for VCc (VCf), r v c (r v f ) and σ v c (σ v f ), respectively, being the agreement better for the coarse mode. In general, the coarse mode parameters obtained during the Saharan dust event analysed here are the typical values obtained at Granada during dust events originating from Western Sahara (Valenzuela et al., 2012). It is noted that the coarse modal radius retrieved by GRASP D scheme is slightly larger than that provided by AERONET during both periods. This shift towards larger radii for GRASP retrievals was also observed by Lopatin et al. (2013) during dust and biomass burning events over Minsk (Belarus) and by 5 Bovchaliuk et al. (2016) during dust events over Dakar (Senegal) and it is attributed to the use of additional lidar data.
Columnar aerosol size distribution parameters at night-time retrieved by GRASP using different schemes (see Tab. 2) show a good coherence and smooth variation when they are compared against daytime AERONET and GRASP retrievals (scheme D). In fact, the GRASP 10 night-time retrievals using the N0 scheme present average values similar to those provided by GRASP daytime retrievals with discrepancies around 10% for both modes in the two analysed periods. The aerosol size distribution parameters of coarse mode retrieved by GRASP using N1 scheme are slightly higher systematically than those obtained during day time (by both D scheme and AERONET) with differences around 15% and 10% for VCc and r v c , respectively. 15 These differences are inside the uncertainties observed by Torres et al. (2017) in the cases in which the coarse mode is predominant. The use of night-time AOD measurements in N1 scheme, which reveals a change in AOD values (aerosol load) between day and night, can also be behind these changes in the aerosol size distribution parameters retrieved by N1 scheme.
Finally, the values of aerosol parameters retrieved by GRASP using the N2 scheme are 20 almost similar to the values retrieved by AERONET the day before and after, especially for coarse mode where the discrepancies are around 12%, 3% and 20% for VCc, and , respectively, showing the potential of such retrievals. However, for fine mode properties (VCf, and ) there are considerable differences between GRASP and AERONET retrievals mainly due to the low concentration of fine particles. 25

Columnar complex refractive indices
The real (RRI) and imaginary (IRI) refractive indices obtained by GRASP and AERONET are not directly comparable because the GRASP configurations used here provide RRI and IRI separately for fine and coarse modes while AERONET provides only RRI and IRI equivalent values for the whole size distribution. Nevertheless, the RRI and IRI values provided by 30 AERONET are again used to study the consistency of the proposed schemes for GRASP retrievals. In this case, the mean RRI and IRI values (see Tab. 3) and their corresponding standard deviations correspond to the whole analysed period. This is done because, in contrast to VC retrievals that showed a large change between P1 and P2 periods, RRI and IRI retrieved by GRASP (using different schemes) and AERONET were almost stable and showed a very small variation along the whole analysed period, as indicated by the corresponding standard deviations. As can be seen in this table, standard deviations were within and even below the 5 uncertainties associated with the AERONET retrievals, i. e. ± 0.03 for RRI and ±50% for IRI (Dubovik et al., 2000). On the other hand, it is important to remember that complex refractive indices values for the N1 scheme are not reported in  (Shettle and Fenn, 1979;WMO, 1983;Koepke et al., 1997). However, the differences between RRI values obtained here for desert dust event and those reported in the 25 literature can be explained by the differences in the chemical composition of dust (e.g., Patterson et al. 1977;Carlson and Benjamin, 1980;Sokolik et al., 1993;Sokolik and Toon, 1999 to the AERONET retrievals. The observed spectral dependence in IRI is the typical observed for desert dust with higher IRI in the UV region (Patterson et al., 1977;Dubovik et al., 2002;Wagner et al., 2012). The mean IRI values retrieved using D and N0 schemes for coarse mode are almost similar to AERONET retrievals being the differences within the uncertainties (about 50%) associated with IRI provided by AERONET (Dubovik et al., 2000) and similar to those 5 obtained in previous works as Benavent-Oltra et al. (2017) and Tsekeri et al. (2017). Although the discrepancy between IRI values retrieved using N2 scheme for coarse mode and those provided by AERONET is high, the IRI values of N2 scheme are consistent with IRI values around 0.008 at 675 nm obtained at night-time during a dust event in Dakar (Senegal) by Bovchaliuk et al. (2016). Considering the success in this issue for daytime IRI retrievals, it can 10 be concluded that accurate AOD and sky measurements combined with lidar measurements are useful for accurately characterizing CRI, and particularly for separating the features of fine and coarse modes as discussed by Dubovik et al. (2000). The approach proposed using additional relative radiance in the lunar aureole is also promising for the retrievals of CRI values.
Nevertheless, studying the accuracy of the IRI retrieved using night-time sky cameras require 15 further studies. Table 4 shows the averaged values of SSA and their corresponding standard deviations obtained by GRASP (using different schemes) and AERONET during the whole dust event.

Columnar single-scattering albedo
As for IRI and RRI retrievals, SSA values retrieved by both GRASP and AERONET show 20 very small temporal variation during the whole analysed period, as confirmed by the low standard deviations of the SSA values.
[Insert Table 4 here] SSA retrieved by GRASP and AERONET show a smooth variability between day and night for the total period. Actually, mean differences in SSA values retrieved by GRASP and 25 AERONET are below 0.03, which it is within uncertainty associated to AERONET retrieval for dust aerosol (Dubovik et al., 2000) and similar to those obtained in previous works as  5b), where the N2 scheme shows large differences with Raman at 355 and 532 nm. For this case, the N2 GRASP retrieval fits worse with Raman likely since the obtained residual error was higher than the residuals of the other retrievals which presented higher convergence.
In order to quantify the agreement between the retrieved extinction with GRASP and   (Herreras et al., 2019). Also, the rather broad assumption of constant lidar ratio used in the estimation of the extinction at 1064 nm, derived from the backscatter coefficient retrieved by Klett-Fernald retrieval, could explain a part of the observed discrepancies at 1064 nm. In general, the differences between GRASP and Raman retrievals 25 present in this work are similar to the differences obtained in previous studies (e.g. Bovchaliuk et al., 2016;Tsekeri et al., 2017).

GRASP retrievals versus in-situ measurements
Hereafter, ( ) and VC retrieved by GRASP are compared versus in-situ measurements 30 obtained at SNS station (2.5 km a.s.l). In Figure 7, the averaged profiles of scattering coefficient at 532 nm (Fig. 7a) and volume concentration profiles (Fig. 7b)  [Insert Figure 7 here] 10 For a direct comparison between GRASP and in-situ measurements we used the averaged values of GRASP retrievals at an altitude of 2.5 ± 0.2 km a.s.l. and in-situ measurements averaged ± 15 min around the GRASP retrieval time. Comparisons of are made at 450, 550 and 700 nm and the AE computed from GRASP retrievals is used to get the equivalent at these wavelengths. Figure 8a shows the temporal evolutions of at 550 15 nm obtained by GRASP (D, N0, N1, and N2 schemes) and by the integrating nephelometer at SNS station for the analysed dust event. Generally, both GRASP and in-situ measurements follow the same pattern and are sensitive to the arrival of Saharan dust particles. Furthermore, differences between GRASP (using different schemes) and in-situ measurements are very small, being the differences less than 25 Mm -1 in 90% of the cases. Generally, the differences 20 are negligible for daytime. For night-time, the best agreement is found for the N1 scheme and the worst accordance is obtained for the N2 scheme. The worst accordance for N2 scheme could be due to the smaller scattering angle range of the almucantar radiance retrieved from the moon aureole. In addition, the number of available retrievals for each scheme can be also appreciated in Figure 7; when a retrieval does not appear in the figure is because this retrieval 25 did not pass the imposed convergence criteria. [Insert Figure 9 here] An overview of the statistical analysis of the differences between GRASP retrievals and in-situ scattering coefficient measurements is given in Table 6 that shows the mean of the 5 differences expressed as (∆ = − ) and also the mean of the relative differences ∆ = 100 · | − |/ for each scheme. Due to the drastic change in the scattering coefficient between P1 and P2 periods, this statistical analysis is provided separately for these two periods. For the P1 period, GRASP algorithm underestimates the insitu scattering coefficient measurements both at day and night, especially for N0 and N2 10 schemes, and at all wavelengths. The highest differences are found for N2 scheme with differences between 30% (at 700 nm) and 35% (at 550 nm). However, for the other schemes (D, N0, and N1) the differences are less than 20%. Again, the uncertainties associated with IRI and with the incomplete overlap assumption as well as the particles losses in sampling inlet can be behind these differences. However, for P2 period, the differences are considerably small 15 and even in some cases they go down to the half of the differences observed in P1 period. On contrary to P1, GRASP overestimates in-situ scattering coefficient in P2 for all schemes except N2. N1, followed by N0, presents the scattering values fitting best with in-situ measurements during P2 period, while D scheme shows the highest differences. The uncertainties associated with IRI and with the incomplete overlap assumption as well as the particles losses in sampling 20 inlet and also uncertainties in the measurements (used as input in GRASP but also from nephelometer) could be behind at least part of the observed differences.
[Insert Table 6 here] Figure 8b shows the temporal evolutions of the VC retrieved by GRASP at 2500 m a.s.l. and those measured at SNS station. As for the scattering coefficient, the VC retrieved by 25 GRASP and the measured at SNS follow the same pattern both being sensitive to the increase of dust event intensity. Differences at daytime are negligible, while at night-time the differences depend on the GRASP scheme used, being the differences in the P1 period much smaller than in P2 period indicating that the differences increase with increasing aerosol load. Figure 9d shows the VC values retrieved by GRASP (using different schemes) versus those 30 measured at SNS station. The correlation between the measured and the retrieved values is very high with r 2 between 0.75 and 0.98. As in the case of the scattering coefficient, linear fits indicate an underestimation by GRASP for low values and overestimates for high values. Table 7 presents an overview of the statistical analysis of VC comparisons. This table shows the mean of ∆ = − and the mean of the absolute relative differences described by ∆ (%) = 100 · | − |⁄ . It is clearly observed that GRASP 5 fits the measured values within 15% for D, N0 and N1 schemes during P1 period, while for N2 scheme is observed an underestimation around 30%. However, for P2 period, VC from GRASP overestimates the in-situ measurements with differences around 20% for D, N0 and N1 schemes; while for N2 scheme, GRASP still underestimates the in-situ measurements again but with lower differences, around 10%, than P1 period. The differences between GRASP (all 10 schemes) and in-situ data are within the differences obtained in previous studies that compared GRASP retrievals with in-situ airborne measurements and LIRIC algorithm (e.g. Bovchaliuk et al., 2016;Tsekeri et al., 2017). The different assumption in GRASP algorithm and the particles losses in sampling inlet (which increase with increasing aerosol load) can be behind the observed differences between GRASP retrievals and in-situ 15 measurements.

Summary and conclusions
The main goal of this work has been to explore the capacity and possibilities of GRASP algorithm to retrieve vertical profiles and column-integrated optical and microphysical aerosol 20 properties at night-time. To this end, we proposed three different schemes combining the measurements of different remote sensing instruments such as elastic lidar, sun/sky/lunar photometer and/or sky camera. The experimental measurements used in this wok were acquired during a Saharan dust event that took place during SLOPE I campaign at Granada (Spain) from 18 th to 21 st July 2016. This event has been selected because intensive aerosol properties such 25 as Angström Exponent did not vary too much, with a value around 0.2, and was very intense with aerosol optical depth (AOD) reaching twice the typical values for Saharan dust outbreaks at Granada.
The three schemes proposed to run GRASP for night-time retrievals have different assumptions, as: no day/night variation of aerosol intensive neither extensive (except vertical 30 distribution) properties (N0 scheme); no day/night variation of aerosol intensive properties but considering changes on extensive aerosol properties (N1 scheme); day/night variation in both intensive and extensive aerosol properties (N2 scheme).
AERONET inversion products have been used to study the coherence of GRASP nighttime retrievals and of the continuous day-to-night aerosol evolution. For the parameters derived from columnar aerosol size distributions, all GRASP schemes show coherent values with 5 AERONET. Similarly happens for complex refractive index (CRI) and single-scattering albedo (SSA), although more variability is observed, particularly for the N2 scheme due likely to the large uncertainties in relative sky radiance measurements at lunar aureole and the higher freedom degrees assumed than in N1 scheme. Nevertheless, we were not able to go further in the evaluation of the accuracy of the GRASP retrieved parameters. Doing so would require a 10 large synthetic database that out of the scope of the manuscript. Also, it is needed to study the sensitivity of retrievals to errors in the input optical data, which is the objective of future works.
In general, the aerosol extinction from GRASP and Raman measurements agrees quite well, with differences below 30% at 355, 532 and 1064 nm. The scattering coefficient ( ) and aerosol volume concentration (VC) retrieved by GRASP (using different schemes) at 2500 m a.s.l. have been evaluated against in-situ measurements acquired at Sierra Nevada station during a dust event classified in two periods: moderate (P1) and high (P2) aerosol load. Usually, both GRASP retrievals and in-situ measurements follow the same patterns and are sensitive to the arrival of Saharan dust particles. GRASP N0 and N1 schemes underestimate the in-situ and VC measurements for P1 period (except for VC from N1 scheme) and overestimate for P2 20 period with differences between 4% and 23%. On the other hand, GRASP N2 scheme underestimates the in-situ measurement both and VC, with differences around 30% and 10% for P1 and P2 periods, respectively. In general, N2 show slightly high differences than other schemes, however the best results for VC in P2 are for N2 scheme.
The obtained differences could be likely caused by different factors like: the 25 approximation used to fill the incomplete overlap area; the uncertainties in data used as input (large differences shown in N2 scheme could be due the uncertainties associated with the measurements of relative lunar sky radiance); the self-uncertainties of GRASP algorithm under the followed configurations; but also the uncertainty in the values used as reference (like uncertainties in the in-situ measurements); the lack of overlap between night-time retrievals 30 and AERONET daytime retrievals used as reference; and possible inhomogeneity in the atmosphere and local aerosol sources when the GRASP retrievals are compared with in-situ measurements carried out in the mountain.
The analysis presented here is useful to present three configurations of GRASP algorithm to retrieve night-time column-integrated and vertically-resolved of aerosol properties by combination of different remote sensing instruments. In fact, the proposed N2 scheme allows a stand-alone way to retrieve intensive and extensive aerosol properties at night in the cases with high values of AOD and high Moon irradiance (at least between the first and last 5 Moon quarters) independent on daytime information, even when this scheme usually present higher differences with the reference values. However, this study is only focus in one aerosol episode which is representative of Saharan dust transport and hence, it is necessary to use a more complete dataset that includes at least different aerosol types. Additional studies are needed in this sense to investigate the accuracy and uncertainty of the retrieved GRASP 10 products obtained with the proposed schemes; in this sense sensitivity tests could be done using synthetic data as reference. Therefore, in future studies, it is planned to developed different sensitivity studies with the proposed schemes. In addition, we could try to study the capabilities of GRASP to work with Raman lidar signals and implement the multi-pixel scenario proposed by Dubovik et al. (2011) to retrieve the aerosol properties at night. Barreto, Á., Román, R., Cuevas, E., Pérez-Ramírez, D., J. Berjón, A., Kouremeti, N., Kazadzis, S., Gröbner, J., Mazzola, M., Toledano, C., Benavent-Oltra, J. A., Doppler, L., Juryšek, J., Almansa, A. F., Victori, S., Maupin, F., Guirado-Fuentes, C., González, R., Vitale, V., Goloub, P., Blarel, L., Alados-Arboledas, L., Woolliams, E., Greenwell, C., Taylor, S., Antuña, J. C., and Yela, M.: Evaluation of night-time aerosols measurements and lunar irradiance models in the frame of the first multi-instrument nocturnal Bedoya-Velásquez, A. E., Navas-Guzmán, F., Granados-Muñoz, M. J., Titos, G., Román, R., Casquero-Vera, J.
A., Ortiz-Amezcua, P., Benavent-Oltra, J. A.,     [µm]) and standard deviation ( and ) for fine and coarse modes retrieved by GRASP using different configuration schemes and those provided by AERONET. The retrievals are provided for the first period (P1) and the second period (P2). The subscript 'f' denotes fine mode and 'c' denotes coarse mode.