West Africa and the adjacent oceanic regions are very important locations for studying dust properties and their influence on weather and climate. The SHADOW (study of SaHAran Dust Over West Africa) campaign is performing a multiscale and multilaboratory study of aerosol properties and dynamics using a set of in situ and remote sensing instruments at an observation site located at the IRD (Institute for Research and Development) in Mbour, Senegal (14° N, 17° W). In this paper, we present the results of lidar measurements performed during the first phase of SHADOW (study of SaHAran Dust Over West Africa) which occurred in March–April 2015. The multiwavelength Mie–Raman lidar acquired 3β + 2α + 1δ measurements during this period. This set of measurements has permitted particle-intensive properties, such as extinction and backscattering Ångström exponents (BAE) for 355/532 nm wavelengths' corresponding lidar ratios and depolarization ratio at 532 nm, to be determined. The mean values of dust lidar ratios during the observation period were about 53 sr at both 532 and 355 nm, which agrees with the values observed during the SAMUM-1 and SAMUM-2 campaigns held in Morocco and Cabo Verde in 2006 and 2008. The mean value of the particle depolarization ratio at 532 nm was 30 ± 4.5 %; however, during strong dust episodes this ratio increased to 35 ± 5 %, which is also in agreement with the results of the SAMUM campaigns. The backscattering Ångström exponent during the dust episodes decreased to ∼ −0.7, while the extinction Ångström exponent, though negative, was greater than −0.2. Low values of BAE can likely be explained by an increase in the imaginary part of the dust refractive index at 355 nm compared to 532 nm. The dust extinction and backscattering coefficients at multiple wavelengths were inverted to the particle microphysics using the regularization algorithm and the model of randomly oriented spheroids. The analysis performed has demonstrated that the spectral dependence of the imaginary part of the dust refractive index may significantly influence the inversion results and should be taken into account.

To study the feasibility of a fluorescence lidar for aerosol characterization, the fluorescence channel is added to the LILAS multiwavelength Mie–Raman lidar of Lille University, France. A part of the fluorescence spectrum induced by 355 nm laser radiation is selected by the interference filter of 44 nm bandwidth centered at 466 nm. Such an approach has proved to have high sensitivity, allowing fluorescence signals from weak aerosol layers to be detected and the fluorescence backscattering coefficient from the ratio of fluorescence and nitrogen Raman backscatters to be calculated. Observations were performed during the November 2019–February 2020 period. The fluorescence capacity (ratio of fluorescence to elastic backscattering coefficients), measured under conditions of low relative humidity, varied in a wide range, being the highest for the smoke and the lowest for the dust particles. The results presented also demonstrate that the fluorescence measurements can be used for monitoring the aerosol inside the cloud layers.

An algorithm for linear estimation of aerosol bulk properties such as particle volume, effective radius and complex refractive index from multiwavelength lidar measurements is presented. The approach uses the fact that the total aerosol concentration can well be approximated as a linear combination of aerosol characteristics measured by multi-wavelength lidar. Therefore, the aerosol concentration can be estimated from lidar measurements without the need to derive the size distribution, which entails more sophisticated procedures. The definition of the coefficients required for the linear estimates is based on an expansion of the particle size distribution in terms of the measurement kernels. Once the coefficients are established, the approach permits fast retrieval of aerosol bulk properties when compared with the full regularization technique. In addition, the straightforward estimation of bulk properties stabilizes the inversion making it more resistant to noise in the optical data. Numerical tests demonstrate that for data sets containing three aerosol backscattering and two extinction coefficients (so called 3β + 2α) the uncertainties in the retrieval of particle volume and surface area are below 45% when input data random uncertainties are below 20%. Moreover, using linear estimates allows reliable retrievals even when the number of input data is reduced. To evaluate the approach, the results obtained using this technique are compared with those based on the previously developed full inversion scheme that relies on the regularization procedure. Both techniques were applied to the data measured by multiwavelength lidar at NASA/GSFC. The results obtained with both methods using the same observations are in good agreement. At the same time, the high speed of the retrieval using linear estimates makes the method preferable for generating aerosol information from extended lidar observations. To demonstrate the efficiency of the method, an extended time series of observations acquired in Turkey in May 2010 was processed using the linear estimates technique permitting, for what we believe to be the first time, temporal-height distributions of particle parameters.

The paper presents an approach to revealing the variability in aerosol type, at high spatiotemporal resolution, by combining fluorescence and Mie–Raman lidar observations. The multiwavelength Mie–Raman lidar system in operation at the ATOLL (ATmospheric Observation at liLLe) platform, Laboratoire d'Optique Atmosphérique, University of Lille, has included, since 2019, a wideband fluorescence channel allowing the derivation of the fluorescence backscattering coefficient β_F. The fluorescence capacity G_F, which is the ratio of β_F to the aerosol backscattering coefficient, is an intensive particle property, strongly changing with aerosol type, thus providing a relevant basis for aerosol classification. In this first stage of research, only two intensive properties are used for classification, namely the particle depolarization ratio at 532 nm, δ₅₃₂, and the fluorescence capacity, G_F. These properties are considered because they can be derived at high spatiotemporal resolution and are quite specific to each aerosol type. In particular, in this study, we use a δ₅₃₂–G_F diagram to identify smoke, dust, pollen, and urban aerosol particles. We applied our new classification approach to lidar data obtained during the 2020–2021 period, which includes strong smoke, dust, and pollen episodes. The particle classification was performed with a height resolution of about 60 m and temporal resolution better than 8 min.

The multiwavelength Mie–Raman–fluorescence lidar of the University of Lille has the capability to measure three aerosol backscattering coefficients, two extinction coefficients and three linear depolarization ratios, together with fluorescence backscattering at 466 nm. It was used to characterize aerosols during the pollen season in the north of France for the period March–June 2020. The results of observations demonstrate that the presence of pollen grains in aerosol mixture leads to an increase in the depolarization ratio. Moreover, the depolarization ratio exhibits a strong spectral dependence increasing with wavelength, which is expected for the mixture containing fine background aerosols with low depolarization and strongly depolarizing pollen grains. A high depolarization ratio correlates with the enhancement of the fluorescence backscattering, corroborating the presence of pollen grains. Obtained results demonstrate that simultaneous measurements of particle depolarization and fluorescence allows for the separation of dust, smoke particles and aerosol mixtures containing the pollen grains.

Measurements performed in western Africa (Senegal) during the SHADOW field campaign are analyzed to show that spectral dependence of the imaginary part of the complex refractive index (CRI) of dust can be revealed by lidar-measured particle parameters. Observations in April 2015 provide good opportunity for such study, because, due to high optical depth of the dust, exceeding 0.5, the extinction coefficient could be derived from lidar measurements with high accuracy and the contribution of other aerosol types, such as biomass burning, was negligible. For instance, in the second half of April 2015, AERONET observations demonstrated a temporal decrease in the imaginary part of the CRI at 440 nm from approximately 0.0045 to 0.0025. This decrease is in line with a change in the relationship between the lidar ratios (the extinction-to-backscattering ratio) at 355 and 532 nm (S355 and S532). For instance in the first half of April, S355∕S532 is as high as 1.5 and the backscatter Ångström exponent, Aβ, is as low as −0.75, while after 15 April S355/S532=1.0 and Aβ is close to zero. The aerosol depolarization ratio δ532 for the whole of April exceeded 30 % in the height range considered, implying that no other aerosol, except dust, occurred. The performed modeling confirmed that the observed S355∕S532 and Aβ values match the spectrally dependent imaginary part of the refractive index as can be expected for mineral dust containing iron oxides. The second phase of the SHADOW campaign was focused on evaluation of the lidar ratio of smoke and estimates of its dependence on relative humidity (RH). For five studied smoke episodes the lidar ratio increases from 44±5 to 66±7 sr at 532 nm and from 62±6 to 80±8 sr at 355 nm, when RH varied from 25 % to 85 %. Performed numerical simulations demonstrate that observed ratio S355∕S532, exceeding 1.0 in the smoke plumes, can indicate an increase in the imaginary part of the smoke particles in the ultraviolet (UV) range.