Inversion of multiwavelength Raman lidar data for retrieval of bimodal aerosol size distribution

1064 nm rotational Raman polarization lidar for profiling aerosol and cloud characteristics

The vertical profiles of aerosol or mixed-phase cloud optical properties (e.g. extinction coefficient) at 1064 nm are difficult to obtain from lidar observations. Based on the techniques of rotational Raman signal at 1058 nm described by Haarig et al. [Atmos. Meas. Tech.9, 4269 (2016)10.5194/amt-9-4269-2016], we have developed a novel rotational Raman polarization lidar at 1064 nm at Wuhan University. In this design, we optimized the central wavelength of the rotational Raman channel to 1056 nm with a bandwidth of 6 nm to increase the signal-to-noise ratio and minimize the temperature dependence of the extracted rotational Raman spectrum. And then separated elastic polarization channels (1064 nm Parallel, P and 1064 nm Cross, S) into near range (low 1064 nm P and 1064 nm S) and far range detection channels (high 1064 nm P and 1064 nm S) to extend the dynamic range of lidar observation. Silicon single photon avalanche diodes (SPAD) working at photon counting mode were applied to improve the quantum efficiency and reduce the electronic noise, which resulted in quantum efficiency of 2.5%. With a power of 3 W diode pumped pulsed Nd:YAG laser and aperture of 250 mm Cassegrain telescope, the detectable range can cover the atmosphere from 0.3 km to the top troposphere (about 12-15 km). To the best of our knowledge, the design of this novel lidar system is described and the mixed-phase cloud and aerosol optical properties observations of backscatter coefficients, extinction coefficients, lidar ratio and depolarization ratio at 1064 nm were performed as demonstrations of the system capabilities.

Optimized method for simultaneous detection of fog/aerosol particle size distribution

We propose forward/lateral scattering of dual-wavelength (ultraviolet and short-wave near-infrared bands) radiation to simultaneously detect aerosol particles and fog droplet size distribution in an open atmosphere. The size distributions were described using a gamma distribution. A light-scattering detection system was optimized and designed, and the final wavelengths and scattering angles of ∼ 350 nm and ∼ 1100 nm, and 1°, 2°, 12°, and 35°, respectively, were selected. Numerical simulation analyses and measurements were performed for the proposed detection scheme. The results confirmed that the proposed method is feasible and can rapidly acquire the fog droplet spectrum and aerosol particle size spectrum distribution in an open environment. The system structure of the method is simple and easy to implement, with high detection results and accuracy.

Pre-filter analysis for retrieval of microphysical particle parameters: a quality-assurance method applied to 3 backscatter (β) +2 extinction (α) optical data taken with HSRL/Raman lidar

We analyze the solution space of 3β+2α optical data inferred from lidar measurements, i.e., backscatter coefficients at three wavelengths and extinction coefficients at two wavelengths. These optical data are governed by microphysical parameters that can be expressed in terms of particle size distribution, effective radius, and complex refractive index (CRI). In our analysis, we consider two scenarios of the solution space. First, it can be expressed in terms of monomodal particle size distributions represented either by fine modes or by coarse modes. Secondly, the particle size distributions contain a fine mode as well as a coarse mode. Consideration of both scenarios and different values of the effective radius and CRI allows us to find synthetic 3β+2α optical data and corresponding intensive parameters (IPs) such as lidar ratios, backscatter- and extinction-related Ångström exponents at the available measurement wavelengths. Based on interdependencies between synthetic IPs and various microphysical properties, the qualitative and quantitative criteria for the optical data quality-assurance tool are developed. We derive the conditions of smoothness, closeness, convergence, and stability of the solution space for the quantitative criteria to test the quality of the 3β+2α optical data. Our novel methodology, to the best of our knowledge, can be used not only for particles of spherical shape, but also for cases in which particles are irregularly shaped. Another strength of our methodology is that it also works for the case of a size-dependent and wavelength-dependent CRI. We show the potential of this methodology for a measurement case from the ORACLES campaign. Data were taken with NASA Langley's airborne HSRL-2 instrument on September 24, 2016.

Classification of atmospheric aerosols and clouds by use of dual-polarization lidar measurements

Accurate identification of aerosols and cloud from remote sensing observations is of importance for quantitatively evaluating their radiative forcing and related impacts. Even though polarization lidar has exhibited a unique advantage of classifying atmospheric aerosols and clouds over the past several decades, polarization measurements are often achieved at one wavelength (UV or VIS) using laser remote sensing. To better identify the types of aerosols and clouds, we developed a ground-based dual-polarization lidar system that can simultaneously detect polarization measurements at wavelengths of 355 nm and 532 nm. Our results show that the volume depolarization ratios (VDRs) at 355 nm and 532 nm markedly differ for typical types of aerosols and clouds in the atmosphere. For non-spherical particles, the ratio of VDRs at 532 nm and 355 nm are 2.87 ± 1.35 for ice cloud and 1.51 ± 0.29 for dust-dominated aerosols, respectively. However, for spherical particles, the ratios are 0.43 ± 0.26 for water cloud and 0.56 ± 0.05 for air pollutants. Consequently, we proposed a simple reliable method for classifying atmospheric aerosols and clouds from polarization measurements observed by the developed lidar system. The proposed method first distinguishes clouds from aerosols using a combination of the color ratio (CR, 532 nm/355 nm) and attenuated backscattering coefficients (ABC) at 532 nm. Then, subtypes of clouds and aerosols are identified based on the ratio of VDRs at 532 nm and 355 nm. The results showed that dual-polarization lidar measurements can remarkably improve the classification of atmospheric aerosols and clouds, compared with results using a traditional method. This study illustrates that more information on atmospheric aerosols and clouds can be obtained from polarization measurements at multiple wavelengths by active remote sensing.

Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops

In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.

Multi-Sensor Analysis of a Weak and Long-Lasting Volcanic Plume Emission

Volcanic emissions are a well-known hazard that can have serious impacts on local populations and aviation operations. Whereas several remote sensing observations detect high-intensity explosive eruptions, few studies focus on low intensity and long-lasting volcanic emissions. In this work, we have managed to fully characterize those events by analyzing the volcanic plume produced on the last day of the 2018 Christmas eruption at Mt. Etna, in Italy. We combined data from a visible calibrated camera, a multi-wavelength elastic/Raman Lidar system, from SEVIRI (EUMETSAT-MSG) and MODIS (NASA-Terra/Aqua) satellites and, for the first time, data from an automatic sun-photometer of the aerosol robotic network (AERONET). Results show that the volcanic plume height, ranging between 4.5 and 6 km at the source, decreased by about 0.5 km after 25 km. Moreover, the volcanic plume was detectable by the satellites up to a distance of about 400 km and contained very fine particles with a mean effective radius of about 7 µm. In some time intervals, volcanic ash mass concentration values were around the aviation safety thresholds of 2 × 10−3 g m−3. Of note, Lidar observations show two main stratifications of about 0.25 km, which were not observed at the volcanic source. The presence of the double stratification could have important implications on satellite retrievals, which usually consider only one plume layer. This work gives new details on the main features of volcanic plumes produced during low intensity and long-lasting volcanic plume emissions.

Application of Regularization Algorithm to HSRL-2 Observations During Oracles Campaign: Comparison of Retrieved and In Situ Particle Size Distributions and Single Scattering Albedo

Data obtained from HSRL-2 observations carried out on 20 September 2016 during the ORACLES campaign are publicly accessible. In our presentation we invert 3β+2α data into (1) particle size distributions with a regularization algorithm, and subsequently compute (2) single scattering albedo. We carry out a first comparison to the same particle characteristics measured with airborne in-situ instruments. We find good agreement of the data products. However, a more detailed study is needed as correction factors and sources of retrieval and measurement uncertainties need to be tested.

Improved algorithm of aerosol particle size distribution based on remote sensing data

The optical parameters (extinction or backscatter coefficients) of multi-wavelength beams can be used for the retrieval of the aerosol particle size distribution (APSD). An improved algorithm for APSD and aerosol microphysical parameters (AMPs) is studied and discussed by using only multi-wavelength extinction coefficients data. The regularized algorithm and prior value are combined for the retrieval of APSD and AMPs. The regularization algorithm, based on minimum discrepancy principle and averaging procedure, is used for the retrieval of fine-mode APSD and an averaging procedure that can achieve stable outputs is proposed. The 1% averaging result near the minimum of the discrepancy is selected and verified. Based on the inversion results of fine mode from the regularization algorithm, the lognormal distribution with a prior value (model radius) is applied to reconstruct the coarse mode of APSDs through fitting the data. The comprehensive application of the regularization algorithm and averaging process improves the stability of the inversion in the fine mode, and the use of the prior value broadens the inversion radius range of APSD. The complex refractive index need not be assumed for this method. The inversion error for different types of aerosols is analyzed and studied. The reliability of the algorithm is tested and verified by many typical APSDs and the measured APSDs by particle size spectrometer in different pollution days. The algorithm sensitivity analysis is also provided and discussed. The algorithm can obtain reliable inversion of APSD and AMPs with large radius range.

Comparison of Columnar, Surface, and UAS Profiles of Absorbing Aerosol Optical Depth and Single-Scattering Albedo in South-East Poland

The impact of absorbing aerosols on climate is complex, with their potential positive or negative forcing, depending on many factors, including their height distribution and reflective properties of the underlying background. Measurement data is very limited, due to insufficient remote sensing methods dedicated to the retrieval of their vertical distribution. Columnar values of absorbing aerosol optical depth (AAOD) and single scattering albedo (SSA) are retrieved by the Aerosol Robotic Network (AERONET). However, the number of available results is low due to sky condition and aerosol optical depth (AOD) limitation. Presented research describes results of field campaigns in Strzyżów (South-East Poland, Eastern Europe) dedicated to the comparison of the absorption coefficient and SSA measurements performed with on-ground in-situ devices (aethalomter, nephelometer), small unmanned aerial system (UAS) carrying micro-aethalometer, as well as with lidar/ceilometer. An important aspect is the comparison of measurement results with those delivered by AERONET. Correlation of absorption to scattering coefficients measured on ground (0.79) and correlation of extinction on ground to AOD measured by AERONET (0.77) was visibly higher than correlation between AOD and AAOD retrieved by AERONET (0.56). Columnar SSA was weakly correlated with ground SSA (higher values of columnar SSA), which were mainly explained by hygroscopic effects, increasing scattering coefficient in ambient (wet conditions), and partly high uncertainty of SSA retrieval. AAOD derived with the use of profiles from UAS up to PBL height, was estimated to contribute in average to 37% of the total AAOD. A method of AAOD estimation, in the whole troposphere, with use of measured vertical profiles of absorption coefficient and extinction coefficient profiles from lidars was proposed. AAOD measured with this method has poor correlation with AERONET data, however for some measurements, within PBL, AAOD was higher than reported by AERONET, suggesting potential underestimation in photometric measurement under particular conditions. Correlation of absorption coefficient in profile to on ground measurements decrease with altitude. Measurements of SSA from drones agree well with ground measurements and are lower than results from AERONET, which suggests a larger contribution of absorbing aerosols. As an alternative for AAOD estimation in case of lack of AERONET AAOD data simple models are proposed, which base on AOD scaling with SSA measured with different methods. Proposed solution increase potential of absorption coefficient measurements in vertical profiles and columns of the atmosphere. Presented solutions make measurements of absorption coefficients in vertical profiles more affordable and allow rough estimation of columnar values for the whole atmosphere.

Automated, unsupervised inversion of multiwavelength lidar data with TiARA: assessment of retrieval performance of microphysical parameters using simulated data

We evaluate the retrieval performance of the automated, unsupervised inversion algorithm, Tikhonov Advanced Regularization Algorithm (TiARA), which is used for the autonomous retrieval of microphysical parameters of anthropogenic and natural pollution particles. TiARA (version 1.0) has been developed in the past 10 years and builds on the legacy of a data-operator-controlled inversion algorithm used since 1998 for the analysis of data from multiwavelength Raman lidar. The development of TiARA has been driven by the need to analyze in (near) real time large volumes of data collected with NASA Langley Research Center's high-spectral-resolution lidar (HSRL-2). HSRL-2 was envisioned as part of the NASA Aerosols-Clouds-Ecosystems mission in response to the National Academy of Sciences (NAS) Decadal Study mission recommendations 2007. TiARA could thus also serve as an inversion algorithm in the context of a future space-borne lidar. We summarize key properties of TiARA on the basis of simulations with monomodal logarithmic-normal particle size distributions that cover particle radii from approximately 0.05 μm to 10 μm. The real and imaginary parts of the complex refractive index cover the range from non-absorbing to highly light-absorbing pollutants. Our simulations include up to 25% measurement uncertainty. The goal of our study is to provide guidance with respect to technical features of future space-borne lidars, if such lidars will be used for retrievals of microphysical data products, absorption coefficients, and single-scattering albedo. We investigate the impact of two different measurement-error models on the quality of the data products. We also obtain for the first time, to the best of our knowledge, a statistical view on systematic and statistical uncertainties, if a large volume of data is processed. Effective radius is retrieved to 50% accuracy for 58% of cases with an imaginary part up to 0.01i and up to 100% of cases with an imaginary part of 0.05i. Similarly, volume concentration, surface-area concentration, and number concentrations are retrieved to 50% accuracy in 56%-100% of cases, 99%-100% of cases, and 54%-87% of cases, respectively, depending on the imaginary part. The numbers represent measurement uncertainties of up to 15%. If we target 20% retrieval accuracy, the numbers of cases that fall within that threshold are 36%-76% for effective radius, 36%-73% for volume concentration, 98%-100% for surface-area concentration, and 37%-61% for number concentration. That range of numbers again represents a spread in results for different values of the imaginary part. At present, we obtain an accuracy of (on average) 0.1 for the real part. A case study from the ORCALES field campaign is used to illustrate data products obtained with TiARA.

Precise size distribution measurement of aerosol particles and fog droplets in the open atmosphere

A precise measurement for the aerosol particle size distribution and fog droplet size distribution simultaneously in the open atmosphere is proposed. The extinction coefficient and small-angle forward scattering measurement are integrated into the detection for particle size distribution in the open atmosphere, and can achieve the fine detection of the particles in the atmosphere with radius between 0.1 to 30 μm. The key technology including optimal scattering angle in small-angle forward scattering measurement and optimal wavelengths selection are discussed and solved in detail. The fourteen different particle size distributions including aerosol size distributions and fog droplet size distributions are used for the determination of optimal forward scattering angle and wavelengths. The optimal forward scattering angle is calculated to be 1.1°. Seven wavelengths for extinction coefficients and five wavelengths for forward scattering coefficients are chosen for the retrieval of particle size distribution in the measurement. The regularization inversion of optical parameters for the retrieval of particle size distribution is described. The aerosol particle size distributions measured by particle spectrometer and actual fog particle size distributions are used for the method test and the reconstructions of particle size distributions. The inversion results show that the method can achieve the precise measurements of aerosol particle size distribution and fog droplet size distribution. The error influence on the inversion results of distributions is discussed. Based on the sensitivity analysis of inversion results, the feasibility of measurement in the real atmosphere is analyzed and discussed, and the scheme of detection system is provided.

Characterization of smoke and dust episode over West Africa: comparison of MERRA-2 modeling with multiwavelength Mie-Raman lidar observations

Observations of multiwavelength Mie-Raman lidar taken during the SHADOW field campaign are used to analyze a smoke-dust episode over West Africa on 24-27 December 2015. For the case considered, the dust layer extended from the ground up to approximately 2000 m while the elevated smoke layer occurred in the 2500-4000 m range. The profiles of lidar measured backscattering, extinction coefficients, and depolarization ratios are compared with the vertical distribution of aerosol parameters provided by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The MERRA-2 model simulated the correct location of the near-surface dust and elevated smoke layers. The values of modeled and observed aerosol extinction coefficients at both 355 and 532 nm are also rather close. In particular, for the episode reported, the mean value of difference between the measured and modeled extinction coefficients at 355 nm is 0.01 km^-1 with SD of 0.042 km^-1. The model predicts significant concentration of dust particles inside the elevated smoke layer, which is supported by an increased depolarization ratio of 15 % observed in the center of this layer. The modeled at 355 nm the lidar ratio of 65 sr in the near-surface dust layer is close to the observed value (70 ± 10) sr. At 532 nm, however, the simulated lidar ratio (about 40 sr) is lower than measurements (55 ± 8 sr). The results presented demonstrate that the lidar and model data are complimentary and the synergy of observations and models is a key to improve the aerosols characterization.

Retrieval of the particle size distribution function from the data of lidar sensing under the assumption of known refractive index

This paper presents a method to retrieve the particle size distribution function from the data of vertical lidar sensing. We have used 462 data models obtained at the Zvenigorod AERONET site obtained in 2011-2012. For each laser shot, we considered both fine (with particle sizes in a range from 0.05 to 0.6 μm) and coarse aerosol fractions (from 0.6 to 10 μm), with emphasize on the coarse fraction. Our suggested method is a modification of the Tikhonov method. The Tikhonov method is not optimal for coarse particles because its stabilizer does not and cannot account for the presence of the coarse mode, i.e., existence of more than one maximum of the size distribution function. The components of the matrix Wu-1 located in quadrants II and IV are sensitive to the change of these parameters. Neglecting this fact will lead again to arbitrary estimates of the contribution of the coarse particles even for exact values on the main diagonal and the two diagonals adjacent to it. Our method allows the coarse fraction up to 2.5 μm to be determined unambiguously. For larger particles (>2.5  μm) we recommend using the available sets of the coefficients, but with the level of values to be determined.

Retrieval of aerosol parameters from multiwavelength lidar: investigation of the underlying inverse mathematical problem

We present an investigation of some important mathematical and numerical features related to the retrieval of microphysical parameters [complex refractive index, single-scattering albedo, effective radius, total number, surface area, and volume concentrations] of ambient aerosol particles using multiwavelength Raman or high-spectral-resolution lidar. Using simple examples, we prove the non-uniqueness of an inverse solution to be the major source of the retrieval difficulties. Some theoretically possible ways of partially compensating for these difficulties are offered. For instance, an increase in the variety of input data via combination of lidar and certain passive remote sensing instruments will be helpful to reduce the error of estimation of the complex refractive index. We also demonstrate a significant interference between Aitken and accumulation aerosol modes in our inversion algorithm, and confirm that the solutions can be better constrained by limiting the particle radii. Applying a combination of an analytical approach and numerical simulations, we explain the statistical behavior of the microphysical size parameters. We reveal and clarify why the total surface area concentration is consistent even in the presence of non-unique solution sets and is on average the most stable parameter to be estimated, as long as at least one extinction optical coefficient is employed. We find that for selected particle size distributions, the total surface area and volume concentrations can be quickly retrieved with fair precision using only single extinction coefficients in a simple arithmetical relationship.

Arrange and average algorithm for the retrieval of aerosol parameters from multiwavelength high-spectral-resolution lidar/Raman lidar data

We present the results of a feasibility study in which a simple, automated, and unsupervised algorithm, which we call the arrange and average algorithm, is used to infer microphysical parameters (complex refractive index, effective radius, total number, surface area, and volume concentrations) of atmospheric aerosol particles. The algorithm uses backscatter coefficients at 355, 532, and 1064 nm and extinction coefficients at 355 and 532 nm as input information. Testing of the algorithm is based on synthetic optical data that are computed from prescribed monomodal particle size distributions and complex refractive indices that describe spherical, primarily fine mode pollution particles. We tested the performance of the algorithm for the "3 backscatter (β)+2 extinction (α)" configuration of a multiwavelength aerosol high-spectral-resolution lidar (HSRL) or Raman lidar. We investigated the degree to which the microphysical results retrieved by this algorithm depends on the number of input backscatter and extinction coefficients. For example, we tested "3β+1α," "2β+1α," and "3β" lidar configurations. This arrange and average algorithm can be used in two ways. First, it can be applied for quick data processing of experimental data acquired with lidar. Fast automated retrievals of microphysical particle properties are needed in view of the enormous amount of data that can be acquired by the NASA Langley Research Center's airborne "3β+2α" High-Spectral-Resolution Lidar (HSRL-2). It would prove useful for the growing number of ground-based multiwavelength lidar networks, and it would provide an option for analyzing the vast amount of optical data acquired with a future spaceborne multiwavelength lidar. The second potential application is to improve the microphysical particle characterization with our existing inversion algorithm that uses Tikhonov's inversion with regularization. This advanced algorithm has recently undergone development to allow automated and unsupervised processing; the arrange and average algorithm can be used as a preclassifier to further improve its speed and precision. First tests of the performance of arrange and average algorithm are encouraging. We used a set of 48 different monomodal particle size distributions, 4 real parts and 15 imaginary parts of the complex refractive index. All in all we tested 2880 different optical data sets for 0%, 10%, and 20% Gaussian measurement noise (one-standard deviation). In the case of the "3β+2α" configuration with 10% measurement noise, we retrieve the particle effective radius to within 27% for 1964 (68.2%) of the test optical data sets. The number concentration is obtained to 76%, the surface area concentration to 16%, and the volume concentration to 30% precision. The "3β" configuration performs significantly poorer. The performance of the "3β+1α" and "2β+1α" configurations is intermediate between the "3β+2α" and the "3β."

Forest fire smoke layers observed in the free troposphere over Portugal with a multiwavelength Raman lidar: optical and microphysical properties

Vertically resolved optical and microphysical properties of biomass burning aerosols, measured in 2011 with a multiwavelength Raman lidar, are presented. The transportation time, within 1-2 days (or less), pointed towards the presence of relatively fresh smoke particles over the site. Some strong layers aloft were observed with particle backscatter and extinction coefficients (at 355 nm) greater than 5 Mm⁻¹ sr⁻¹ and close to 300 Mm⁻¹, respectively. The particle intensive optical properties showed features different from the ones reported for aged smoke, but rather consistent with fresh smoke. The Ångström exponents were generally high, mainly above 1.4, indicating a dominating accumulation mode. Weak depolarization values, as shown by the small depolarization ratio of 5% or lower, were measured. Furthermore, the lidar ratio presented no clear wavelength dependency. The inversion of the lidar signals provided a set of microphysical properties including particle effective radius below 0.2 μm, which is less than values previously observed for aged smoke particles. Real and imaginary parts of refractive index of about 1.5-1.6 and 0.02i, respectively, were derived. The single scattering albedo was in the range between 0.85 and 0.93; these last two quantities indicate the nonnegligible absorbing characteristics of the observed particles.

Vertical profiles of pure dust and mixed smoke-dust plumes inferred from inversion of multiwavelength Raman/polarization lidar data and comparison to AERONET retrievals and in situ observations

We present for the first time vertical profiles of microphysical properties of pure mineral dust (largely unaffected by any other aerosol types) on the basis of the inversion of optical data collected with multiwavelength polarization Raman lidar. The data were taken during the Saharan Mineral Dust Experiment (SAMUM) in Morocco in 2006. We also investigated two cases of mixed dust-smoke plumes on the basis of data collected during the second SAMUM field campaign that took place in the Republic of Cape Verde in 2008. Following the experience of the Aerosol Robotic Network (AERONET), the dust is modeled as a mixture of spherical particles and randomly oriented spheroids. The retrieval is performed from the full set of lidar input data (three backscatter coefficients, two extinction coefficients, and one depolarization ratio) and from a reduced set of data in which we exclude the depolarization ratio. We find differences of the microphysical properties depending on what kind of optical data combination we use. For the case of pure mineral dust, the results from these two sets of optical data are consistent and confirm the validity of the spheroid particle model for data inversion. Our results indicate that in the case of pure mineral dust we do not need depolarization information in the inversion. For the mixture of dust and biomass burning, there seem to be more limitations in the retrieval accuracy of the various data products. The evaluation of the quality of our data products is done by comparing our lidar-derived data products (vertically resolved) to results from AERONET Sun photometer observations (column-averaged) carried out at the lidar field site. Our results for dust effective radius show agreement with the AERONET observations within the retrieval uncertainties. Regarding the complex refractive index a comparison is difficult, as AERONET provides this parameter as wavelength-dependent quantity. In contrast, our inversion algorithm provides this parameter as a wavelength-independent quantity. We also show some comparison to results from airborne in situobservation. A detailed comparison to in situ results will be left for a future contribution.

Feasibility study of integral property retrieval for tropospheric aerosol from Raman lidar data using principal component analysis

A method is introduced to derive integral properties of the aerosol size distribution, e.g., aerosol mass, from tropospheric multiwavelength Raman lidar aerosol extinction and backscatter data, using an adapted form of the principal component analysis (PCA) technique. Since the refractive index of general tropospheric aerosols is variable and aerosol types can vary within one profile, an inversion technique applied in the troposphere should account for varying aerosol refractive indices. Using PCA, if a sufficiently complete set of appropriate refractive index dependent kernels is used, no a priori information about the aerosol type is necessary for the inversion of integral properties. In principle, the refractive index itself can be retrieved, but this quantity is more sensitive to measurement errors than the various integral properties of the aerosol size distribution. Here, the PCA technique adapted for use in the troposphere is introduced, the refractive index information content of the kernel sets is investigated, and error analyses are presented. The technique is then applied to actual tropospheric Raman lidar measurements.

Polarization impacts on the water-leaving radiance retrieval from above-water radiometric measurements

Above-water measurements of water-leaving radiance are widely used for water-quality monitoring and ocean-color satellite data validation. Reflected skylight in above-water radiometry needs to be accurately estimated prior to derivation of water-leaving radiance. Up-to-date methods to estimate reflection of diffuse skylight on rough sea surfaces are based on radiative transfer simulations and sky radiance measurements. But these methods neglect the polarization state of the incident skylight, which is generally highly polarized. In this paper, the effects of polarization on the sea surface reflectance and the subsequent water-leaving radiance estimation are investigated. We show that knowledge of the polarization field of the diffuse skylight significantly improves above-water radiometry estimates, in particular in the blue part of the spectrum where the reflected skylight is dominant. A newly developed algorithm based on radiative transfer simulations including polarization is described. Its application to the standard Aerosol Robotic Network-Ocean Color and hyperspectral radiometric measurements of the 1.5-year dataset acquired at the Long Island Sound site demonstrates the noticeable importance of considering polarization for water-leaving radiance estimation. In particular it is shown, based on time series of collocated data acquired in coastal waters, that the azimuth range of measurements leading to good-quality data is significantly increased, and that these estimates are improved by more than 12% at 413 nm. Full consideration of polarization effects is expected to significantly improve the quality of the field data utilized for satellite data validation or potential vicarious calibration purposes.

Generalized eikonal approximation for fast retrieval of particle size distribution in spectral extinction technique

In retrieving particle size distribution from spectral extinction data, a critical issue is the calculation of extinction efficiency, which affects the accuracy and rapidity of the whole retrieval. The generalized eikonal approximation (GEA) method, used as an alternative to the rigorous Mie theory, is introduced for retrieval of the unparameterized shape-independent particle size distribution (PSD). To compute the extinction efficiency more efficiently, the combination of GEA method and Mie theory is adopted in this paper, which not only extends the applicable range of the approximation method but also improves the speed of the whole retrieval. Within the framework of the combined approximation method, the accuracy and limitations of the retrieval are investigated. Moreover, the retrieval time and memory requirement are also discussed. Both simulations and experimental results show that the combined approximation method can be successfully applied to retrieval of PSD when the refractive index is within the validity range. The retrieval results we present demonstrate the high reliability and stability of the method. By using this method, we find the complexity and computation time of the retrieval are significantly reduced and the memory resources can also be saved effectively, thus making this method more suitable for online particle sizing.

Influence of polarimetric satellite data measured in the visible region on aerosol detection and on the performance of atmospheric correction procedure over open ocean waters

An original atmospheric correction algorithm, so-called multi-directionality and POLarization-based Atmospheric Correction (POLAC), is described. This algorithm is based on the characteristics of the multidirectional and polarimetric data of the satellite PARASOL (CNES). POLAC algorithm is used to assess the influence of the polarimetric information in the visible bands on the retrieval of the aerosol properties and the water-leaving radiance over open ocean waters. This study points out that the use of the polarized signal significantly improves the aerosol type determination. The use of the polarized information at one visible wavelength only, namely 490 nm, allows providing estimates of the Angstrom exponent of aerosol optical depth with an uncertainty lower than 4%. Based on PARASOL observations, it is shown that the detection of the fine aerosols is improved when exploiting polarization data. The atmospheric component of the satellite signal is then better modeled, thus improving de facto the water-leaving radiance estimation.

Vertical profiles of microphysical particle properties derived from inversion with two-dimensional regularization of multiwavelength Raman lidar data: experiment

Inversion with two-dimensional (2-D) regularization is a new methodology that can be used for the retrieval of profiles of microphysical properties, e.g., effective radius and complex refractive index of atmospheric particles from complete (or sections) of profiles of optical particle properties. The optical profiles are acquired with multiwavelength Raman lidar. Previous simulations with synthetic data have shown advantages in terms of retrieval accuracy compared to our so-called classical one-dimensional (1-D) regularization, which is a method mostly used in the European Aerosol Research Lidar Network (EARLINET). The 1-D regularization suffers from flaws such as retrieval accuracy, speed, and ability for error analysis. In this contribution, we test for the first time the performance of the new 2-D regularization algorithm on the basis of experimental data. We measured with lidar an aged biomass-burning plume over West/Central Europe. For comparison, we use particle in situ data taken in the smoke plume during research aircraft flights upwind of the lidar. We find good agreement for effective radius and volume, surface-area, and number concentrations. The retrieved complex refractive index on average is lower than what we find from the in situ observations. Accordingly, the single-scattering albedo that we obtain from the inversion is higher than what we obtain from the aircraft data. In view of the difficult measurement situation, i.e., the large spatial and temporal distances between aircraft and lidar measurements, this test of our new inversion methodology is satisfactory.

Aerosols and environmental pollution

The number of publications on atmospheric aerosols has dramatically increased in recent years. This review, predominantly from a European perspective, summarizes the current state of knowledge of the role played by aerosols in environmental pollution and, in addition, highlights gaps in our current knowledge. Aerosol particles are ubiquitous in the Earth's atmosphere and are central to many environmental issues; ranging from the Earth's radiative budget to human health. Aerosol size distribution and chemical composition are crucial parameters that determine their dynamics in the atmosphere. Sources of aerosols are both anthropogenic and natural ranging from vehicular emissions to dust resuspension. Ambient concentrations of aerosols are elevated in urban areas with lower values at rural sites. A comprehensive understanding of aerosol ambient characteristics requires a combination of measurements and modeling tools. Legislation for ambient aerosols has been introduced at national and international levels aiming to protect human health and the environment.

Particle size distribution retrieval from multiwavelength lidar signals for droplet aerosol

A method of retrieval of the aerosol particle size distribution (APSD) from multiwavelength lidar signals is presented. Assumed distribution (usually a bimodal combination of lognormal functions) with a few free parameters is directly substituted into the lidar equations. The minimization technique allows one to find the parameters that provide the best fit of the assumed APSD by comparison of theoretically generated and experimental signals. Prior knowledge of the lidar ratio is not required. The approach was tested on a typical synthetic APSD consisting of spherical droplets. Comparison of lidar measurements with results from a condensation particle counter was also performed. For signals registered at 3-5 wavelengths from the UV to the near IR a satisfactory retrieval of synthetic APSD is possible for the particles within the 100-3000 nm range.

Reconstruction of the aerosol optical parameters from the data of sensing with a multifrequency Raman lidar

A method of interpreting data of multifrequency Raman lidar sensing is developed. An algorithm for separating aerosol layers with different scattering properties and subsequently estimating the average value of the lidar ratio and Angström parameter within individual layers is suggested. The algorithm allows the error of reconstructing the backscattering coefficient from daytime observations to be at least halved. A well-posed numerical differentiation algorithm for determining the extinction coefficient is suggested for the interpretation of nighttime measurements based on the transformation of the range of allowable values that requires a solution of nonlinear equations. An iterative procedure envisaged for linearization improves the spatial resolution compared with the conventional methods. The methods can be successfully used to process routine lidar measurements under conditions of a priori uncertainty.

Theory of inversion with two-dimensional regularization: profiles of microphysical particle properties derived from multiwavelength lidar measurements

We present the theory of inversion with two-dimensional regularization. We use this novel method to retrieve profiles of microphysical properties of atmospheric particles from profiles of optical properties acquired with multiwavelength Raman lidar. This technique is the first attempt to the best of our knowledge, toward an operational inversion algorithm, which is strongly needed in view of multiwavelength Raman lidar networks. The new algorithm has several advantages over the inversion with so-called classical one-dimensional regularization. Extensive data postprocessing procedures, which are needed to obtain a sensible physical solution space with the classical approach, are reduced. Data analysis, which strongly depends on the experience of the operator, is put on a more objective basis. Thus, we strongly increase unsupervised data analysis. First results from simulation studies show that the new methodology in many cases outperforms our old methodology regarding accuracy of retrieved particle effective radius, and number, surface-area, and volume concentration. The real and the imaginary parts of the complex refractive index can be estimated with at least as equal accuracy as with our old method of inversion with one-dimensional regularization. However, our results on retrieval accuracy still have to be verified in a much larger simulation study.

Bayesian assessment of uncertainty in aerosol size distributions and index of refraction retrieved from multiwavelength lidar measurements

We investigate the assessment of uncertainty in the inference of aerosol size distributions from backscatter and extinction measurements that can be obtained from a modern elastic/Raman lidar system with a Nd:YAG laser transmitter. To calculate the uncertainty, an analytic formula for the correlated probability density function (PDF) describing the error for an optical coefficient ratio is derived based on a normally distributed fractional error in the optical coefficients. Assuming a monomodal lognormal particle size distribution of spherical, homogeneous particles with a known index of refraction, we compare the assessment of uncertainty using a more conventional forward Monte Carlo method with that obtained from a Bayesian posterior PDF assuming a uniform prior PDF and show that substantial differences between the two methods exist. In addition, we use the posterior PDF formalism, which was extended to include an unknown refractive index, to find credible sets for a variety of optical measurement scenarios. We find the uncertainty is greatly reduced with the addition of suitable extinction measurements in contrast to the inclusion of extra backscatter coefficients, which we show to have a minimal effect and strengthens similar observations based on numerical regularization methods.

Retrieval of aerosol properties from combined multiwavelength lidar and sunphotometer measurements

Simulation studies were carried out with regard to the feasibility of using combined observations from sunphotometer (SPM) and lidar for microphysical characterization of aerosol particles, i.e., the retrieval of effective radius, volume, and surface-area concentrations. It was shown that for single, homogeneous aerosol layers, the aerosol parameters can be retrieved with an average accuracy of 30% for a wide range of particle size distributions. Based on the simulations, an instrument combination consisting of a lidar that measures particle backscattering at 355 and 1574 nm, and a SPM that measures at three to four channels in the range from 340 to 1020 nm is a promising tool for aerosol characterization. The inversion algorithm has been tested for a set of experimental data. The comparison with the particle size distribution parameters, measured with in situ instrumentation at the lidar site, showed good agreement.

Extension of the graphical technique for estimation of particle size distribution parameters for the consistent intercomparison of diverse sets of multiwavelength lidar derived optical coefficients

In applying the graphical technique to the estimation of the particle size distribution (PSD) parameters, determination of proper bounds surrounding the solution space for a particular confidence level is essential to the consistent intercomparison of diverse multiwavelength lidar optical data sets. The graphical technique utilizes ratios of backscatter and/or extinction coefficients, and it is shown that if the correlation between ratios is not taken into account in calculating the error bounds, the solution space will be overestimated, resulting in relatively larger discrepancies for a larger number of optical coefficients. A method for correcting the bounds, to account for the correlation is developed for various numbers of wavelengths. These improved bounds are then applied, for the case of a monomodal lognormal PSD, with an assumed refractive index, to assess the role additional Raman extinction channels play in improving retrieval capability of a typical three-channel backscatter lidar (1064, 532, and 355 nm) under varying noise levels. Applying the same formalism to underlying bimodal distributions of coarse and fine particles can result in false monomodal solutions. However, when both Raman optical extinction channels are available, no solution is obtained. This can potentially serve as a quick and simple method, prior to a more complex regularization analysis, to differentiate between cases in which the fine mode is dominant versus the cases in which the contribution from the coarse mode is significant.

Information content of multiwavelength lidar data with respect to microphysical particle properties derived from eigenvalue analysis

The multiwavelength Raman lidar technique in combination with sophisticated inversion algorithms has been recognized as a new tool for deriving information about the microphysical properties of atmospheric aerosols. The input optical parameter sets, provided by respective aerosol Raman lidars, are at the theoretical lower limit at which these inversion algorithms work properly. For that reason there is ongoing intense discussion of the accuracy of these inversion methods and the possibility of simultaneous retrieval of the particle size distribution and the complex refractive index. We present results of the eigenvalue analysis, used to study the information content of multiwavelength lidar data with respect to microphysical particle properties. Such an analysis provides, on a rather mathematical basis, more insight into the limitations of these inversion algorithms regarding the accuracy of the retrieved parameters. We show that the effective radius may be retrieved to 50% accuracy and the real and imaginary part of the complex refractive index to +/- 0.05 and +/- 0.005i, if the imaginary part is < 0.02i. These results are in accordance with the classic approach of simulation studies with synthetic particle size distributions. Major difficulties are found with a particle effective radius of < 0.15 microm. In that case the complex refractive index may not be derived with sufficient accuracy. The eigenvalue analysis also shows that the accuracy of the derived parameters degrades if the imaginary part is > 0.02i. Furthermore it shows the importance of the simultaneous use of backscatter and extinction coefficients for the retrieval of microphysical parameters.

Particle extinction measured at ambient conditions with differential optical absorption spectroscopy. 1. system setup and characterization

We describe an instrument for measuring the particle extinction coefficient at ambient conditions in the spectral range from 270 to 1000 nm. It is based on a differential optical absorption spectroscopy (DOAS) system, which was originally used for measuring trace-gas concentrations of atmospheric absorbers in the ultraviolet-visible wavelength range. One obtains the particle extinction spectrum by measuring the total atmospheric extinction and subtracting trace-gas absorption and Rayleigh scattering. The instrument consists of two nested Newton-type telescopes, which are simultaneously used for emitting and detecting light, and two arrays of retroreflectors at the ends of the two light paths. The design of this new instrument solves crucial problems usually encountered in the design of such instruments. The telescope is actively repositioned during the measurement cycle. Particle extinction is simultaneously measured at several wavelengths by the use of two grating spectrometers. Optical turbulence causes lateral movement of the spot of light in the receiver telescope. Monitoring of the return signals with a diode permits correction for this effect. Phase-sensitive detection efficiently suppresses background signals from the atmosphere as well as from the instrument itself. The performance of the instrument was tested during a measurement period of 3 months from January to March 2000. The instrument ran without significant interruption during that period. A mean accuracy of 0.032 km(-1) was found for the extinction coefficient for an 11-day period in March.