The typical multiwavelength aerosol lidar data set for inversion of optical to microphysical parameters is composed of three backscatter coefficients (

We find that depolarization information of at least one wavelength already provides useful information for the inversion of optical data that have been collected in the presence of non-spherical mineral dust particles. However, any choice of

Over the past 2 decades, the inversion of multiwavelength aerosol lidar measurements for the retrieval of aerosol microphysical properties

The presence of such non-spherical particles in lidar measurements is identified by non-zero values of the particle linear depolarization ratio (

A database for light scattering by spheroids (Dubovik model,

On the one hand, the answer to the question of what inversion input provides the most accurate estimate of dust microphysical parameters requires independent measurements of these parameters. An example for such a study is presented by

In this study, we investigate the effect of using

What is the optimum choice of

We address this question by using

The particle linear depolarization ratio

Light scattering by non-spherical particles such as mineral dust poses a great challenge for applications in atmospheric science as it cannot be described by Mie scattering. The problem has been addressed by introducing a variety of non-spherical model particles

Overview of particle linear depolarization ratios for pure mineral dust from field measurements

Figure

Mineral dust and volcanic ash were generally considered to be the aerosol types that show the highest values of

This section provides an overview of the lidar data used in this study, different methodologies for estimating the contribution of non-spherical particles to the measured optical data and a brief description of the inversion procedure.

To date, few lidar instruments have the capability of measuring particle linear depolarization ratios at three wavelengths simultaneously, and we refer to

HSRL-2 is the second-generation airborne HSRL developed at NASA Langley Research Center. It builds on the heritage of the HSRL-1 system

Overview of the

DISCOVER-AQ measurements with HSRL-2 were screened for observations that showed elevated levels of

While BERTHA had been used to characterize the optical properties of pure dust during the Saharan Mineral Dust Experiment (SAMUM,

The particle linear depolarization ratio is an intensive aerosol property that can be applied for aerosol classification

BERTHA measurement from 23:10 to 02:10 UTC on 20–21 June 2014 during SALTRACE in terms of

In principle, these aerosol-type separation techniques can be used to obtain input data sets for the inversion of lidar data that represent the spherical and non-spherical particles in a mixed aerosol plume, respectively. The inversion could then be run with the conventional

The inversion of multiwavelength lidar data is based on using light-scattering kernels that were computed on the basis of Mie theory

Because depolarization-ratio measurements at 532 nm are most common

Inversion calculations have been performed with eight base functions and by varying the minimum and maximum particle radius of the inversion window between 0.075 and 0.450

Combinations of

For the measurements listed in Table

Standard inversion outputs are particle number, surface-area, and volume concentration and effective radius derived from these parameters, complex refractive index, and single-scattering albedo (SSA). The inversion with spheroid kernels also provides us with an estimate of the contribution of spheroids to the values we obtain for each of the parameters. In the inversion algorithm, the obtained microphysical properties are used to recalculate the input parameter and to assess the discrepancy between the original input data and the optical data set, which are both obtained from the retrieved microphysical properties. In the analysis of the inversion calculations, we have averaged those 140–200 solutions (median value of 160 for the different input data sets) that revealed the smallest discrepancy to the optical input data. The mean and median discrepancies found from this approach for the different input data sets are shown in Table

We present selected measurement cases that illustrate the effect of the choice of inversion input data sets on the retrieved aerosol microphysical properties. These case studies describe scenarios of varying concentration of non-spherical particles. We then discuss the results for the entire data set outlined in Table

A

Figure

Inversion results of

Figure

The

Same as Fig.

Figure

In contrast, the use of a different number of depolarization information results in a much stronger spread of the spheroid fraction. If we use no depolarization information we obtain spheroid fractions that vary between 20 % and 30 % and change erratically from height bin to height bin. The sets III, IV, and VII (i.e. those with

Same as Fig.

The separation of the results for different input data can also be seen in the profiles of the 532 nm SSA and the refractive index in Fig.

Figure

Profiles of

A more complete picture of the effect of the choice of

Connection between the retrieved spheroid fraction (from inversion) and the ratio of non-spherical particles to the 532 nm backscatter coefficient (from lidar measurements of

Correlation of

In the following, we are hence contrasting the results for the volume concentration, the effective radius, and the SSA according to the two subsets shown in Fig.

The dependence of the retrieved real and imaginary parts of the refractive index on the dust ratio is shown in Fig.

We would like to start the discussion by emphasizing that the results presented here are specific to the application of the spheroid model of

The results we obtain from our study are somewhat contradictory to the findings of

We stress again that the conclusions of this study are valid only for the inversion of lidar data, which resorts to describing the light-scattering properties of non-spherical dust particles by means of the spheroid model of

Following on the initial work of

The retrieved

Our findings provide insights that go beyond previous studies that investigated the effect of adding depolarization information to the inversion of multiwavelength lidar data.

Previous studies that also resort to using the Dubovik model for lidar applications focused exclusively on pure-dust situations, i.e. values of

We present the first results of applying the inversion with the Dubovik model to lidar observations of mixtures of spherical and non-spherical particles of varying degree and spectral behaviour of the particle linear depolarization ratio. Considering such conditions rather than only pure-dust cases allows for using the retrieved spheroid particle fraction as an additional indicator for the quality of the inversion results.

We present the first systematic (though relational) study of the effect of the choice of depolarization input based on actual atmospheric triple-depolarization-ratio measurements. Previous investigations of the effect of depolarization input on the inversion results for which Dubovik's model was used have been restricted to using either

Following the footsteps of AERONET's data processing, microphysical particle properties mark the next logical data product level in the analysis of multiwavelength aerosol lidar data. It is therefore of vital importance to define the minimum information needed for this purpose (i.e. the best choice of input data) as this decision relates directly to the optimum setup for lidar instruments whose measurements can provide this data product. This study represents an important step for determining that information, though it is restricted to a very specific model that is used for describing the light-scattering properties of non-spherical particles. The main issue in that regard is weighting the benefits of using instrument setups, which are already highly challenging, over the added information provided by these measurements. This decision-making is of particular importance in light of future spaceborne lidar missions that will focus on aerosol profiling as well as their airborne demonstrators.

The inversion assumes a spectrally independent complex refractive index. In contrast, mineral dust is known to show a strong increase in the imaginary part of the refractive index with smaller wavelengths. This issue has been explored by

We have performed a first systematic relational investigation of the effect of exploiting different combinations of depolarization information as input to the inversion of optical lidar data into aerosol microphysical properties. The inversion is run with spheroid kernels based on the Dubovik model for the description of light scattering by non-spherical particles. In this work, we use

We have selected 11 observations. Increased values of

We find that inversion without depolarization information (i.e. the traditional

The choice of depolarization input wavelength was found to have little effect on the retrieval of extensive parameters such as the volume concentration and the effective radius that can be derived from this extensive parameter. The use of depolarization input at any wavelength, i.e. 355 or 532 or 1064 nm, generally increases the retrieved values of the 532 nm SSA compared to the

We investigated the connection between output from different sets of input data and inversions in which the light-scattering properties of non-spherical particles are described by the spheroid model of

DISCOVER-AQ data are publicly available from the science team at the NASA Atmospheric Science Data Center (ASDC) via

MT, DM, and AK had the idea for this study and performed the inversion runs. SPB, RAF, and CAH collected the HSRL-2 data during DISCOVER-AQ. MH collected the BERTHA data during SALTRACE. MT performed the analysis and interpretation of the inversion data and prepared the figures. All authors contributed to the discussion of the findings and the preparation of the manuscript.

The authors declare that they have no conflict of interest.

This activity has been supported by ACTRIS research infrastructure (EU H2020-R&I) under grant agreement no. 654109.

This paper was edited by Vassilis Amiridis and reviewed by three anonymous referees.