Measurements of particle extinction coefficients at 1064†nm with lidar: temperature dependence of rotational Raman channels.

Aerosol intensive optical properties, including lidar ratio and particle depolarization ratio, are of vital importance for aerosol typing. However, aerosol intensive optical properties at near-infrared wavelength are less exploited by atmospheric lidar measurements, because of the comparably small backscatter cross section of Raman-scattering and a low efficiency of signal detection compared to what is commonly available at 355 nm and 532 nm. To obtain accurate optical properties of aerosols at near-infrared wavelength, we considered three factors: Raman-spectra selection, detector selection, and interference-filter optimization. Rotational Raman scattering has been chosen for Raman signal detection, because of the higher cross-section compared to vibrational Raman scattering. The optimization of the properties of the interference filter are based on a comprehensive consideration of both signal-to-noise ratio and temperature dependence of the simulated lidar signals. The interference filter that has eventually been chosen uses the central wavelength at 1056 nm and a filter bandwidth (full-width-at-half-maximum) of 6 nm. We built a 3-channel 1064-nm rotational Raman lidar. In this paper two methods are proposed to test the temperature dependence of the signal-detection unit and to evaluate the quality of the Raman signals. We performed two measurements to test the quality of the detection channel: cirrus clouds in the free troposphere and aerosols in the planetary boundary layer. Our analysis of the measured Raman signals shows a negligible temperature dependence of the Raman signals in our system. For cirrus measurements, the Raman signal profile did not show crosstalk even for the case of strong elastic backscatter from clouds, which was about 100 times larger than Rayleigh scattering in the case considered here. The cirrus-mean extinction-to-backscatter ratio (lidar ratio) was 27.8 ± 10.0 sr (1064 nm) at a height of 10.5-11.5 km above ground. For the aerosols in the planetary boundary layer, we found the mean lidar ratio of 38.9 ± 7.0 sr at a height of 1.0-3.0 km above ground.

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

We analyze the solution space of 3ß+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 3ß+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 3ß+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.

Improved algorithm for retrieving aerosol optical properties based on multi-wavelength Raman lidar.

Multi-wavelength Raman lidar has been widely used in profiling aerosol optical properties. The accuracy of measured aerosol optical properties largely depends on sophisticated lidar data retrieval algorithms. Commonly to retrieve aerosol optical properties of Raman lidar, the extinction-related Ångström exponent (EAE) is assumed (to be 1). This value usually generally differs from the true value (called EAE deviation) and adds uncertainty to the retrieved aerosol optical properties. Lidar-signal noise and EAE-deviation are two important error sources for retrieving aerosol optical properties. As the measurement accuracy of Raman lidar has been greatly improved in recent years, the influence of signal noise on retrieval results becomes relatively small, and the uncertainty of retrieved aerosol optical properties caused by an EAE-deviation becomes nonnegligible, especially in scenes that EAE deviation is large. In this study, an iteration retrieval algorithm is proposed to obtain more reliable EAE based on multi-wavelength Raman lidar. Results from this iteration are more precise values of aerosol optical properties. Three atmospheric scenarios where aerosol distribution and the values of EAE vary widely were simulated with a Monte Carlo method to analyze the characteristics and robustness of the iterative algorithm. The results show that the proposed iterative algorithm can eliminate the systematic errors of aerosol optical properties retrieved by traditional retrieval method. The EAEs after iteration does converge to the true value, and the accuracy of aerosol optical properties can be greatly improved, especially for the particle backscatter coefficient and lidar ratio, which has been improved by more than 10% in most cases, and even more than 30%. In addition, field observations data of a three-wavelength Raman lidar are analyzed to illustrate the necessity and reliability of the proposed iterative retrieval algorithm.

Simultaneous profiling of dust aerosol mass concentration and optical properties with polarized high-spectral-resolution lidar.

Dust particles originating from arid desert regions can be transported over long distances, presenting severe risks to climate, environment, social economics, and human health at the source and downwind regions. However, there has been a dearth of continuous diurnal observations of vertically resolved mass concentration and optical properties of dust aerosols, which hinders our understanding of aerosol mixing, stratification, aerosol-cloud interactions, and their impacts on the environment. To fill the gap of the insufficient observations, to the best of our knowledge, this work presents the first high-spectral-resolution lidar (HSRL) observation providing days of continuous profiles of the mass concentration, along with particle linear depolarization ratio (PLDR), backscattering coefficient, extinction coefficient and lidar ratio (LR), simultaneously. We present the results of two strong dust events observed by HSRL over Beijing in 2021. The maximum particle mass concentrations reached (1.52 ± 3.5) x103 µg/m3 and (19.48 ± 0.36) x103 µg/m3 for the two dust events, respectively. The retrieved particle mass concentrations and aerosol optical depth (AOD) agree well with the observation from the surface PM10 concentrations and sun photometer with correlation coefficients of 0.90 and 0.95, respectively. The intensive properties of PLDR and LR of the dust aerosols are 0.31 ± 0.02 and 39 ± 7 sr at 532 nm, respectively, which are generally close to those obtained from observations in the downwind areas. Moreover, inspired by the observations from HSRL, a universal analytical relationship is discovered to evaluate the proportion of dust aerosol backscattering, extinction, AOD, and mass concentration using PLDR. The universal analytical relationship reveals that PLDR can directly quantify dust aerosol contribution, which is expected to further expand the application of polarization technology in dust detection. These valuable observations and findings further our understanding of the contribution of dust aerosol to the environment and help supplement dust aerosol databases.

LITES: rotational Raman spectra of air molecules measured by high-resolution-spectroscopy lidar.

We present the first, to the best of our knowledge, measurements from a new lidar facility that was designed and built at the University of Hertforshire since 2012. LITES (Lidar Innovations for Technologies and Environmental Sciences) allows testing, developing, and measuring of a multitude of climate-change relevant parameters of atmospheric particulate pollution and photochemically reactive trace gases. The core of LITES consists of a lidar spectroscopy instrument. In this first contribution, for example, we present the design and specifications of this instrument, its performance, and potential applications. First, we show examples of the measurements of range-resolved pure rotational Raman spectra and rotational-vibrational Raman spectra of air molecules with a spectral resolution better than 5cm-1. We also present day-time temperature profiles obtained from pure rotational spectroscopic lidar signals. In future work, we aim to explore the potential of our multi-channel high-resolution spectrometric lidar to obtain vertically resolved chemical characterization of aerosols and trace gases.

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.

Improved identification of the solution space of aerosol microphysical properties derived from the inversion of profiles of lidar optical data, part 3: case studies.

We conclude our series of publications on the development of the gradient correlation method (GCM), which can be used for an improved stabilization of the solution space of particle microphysical parameters derived from measurements with multiwavelength Raman and high-spectral-resolution lidar (3 backscatter +2 extinction coefficients). We show results of three cases studies. The data were taken with a ground-based multiwavelength Raman lidar during the Saharan Mineral Dust Experiment in the Cape Verde Islands (North Atlantic). These cases describe mixtures of dust with smoke. For our data analysis we separated the contribution of smoke to the total signal and only used these optical profiles for the test of GCM. The results show a significant stabilization of the solution space of the particle microphysical parameter retrieval on the particle radius domain from 0.03 to 10 µm, the real part of the complex refractive index domain from 1.3 to 1.8, and the imaginary part from 0 to 0.1. This new method will be included in the Tikhonov Advanced Regularization Algorithm, which is a fully automated, unsupervised algorithm that is used for the analysis of data collected with the worldwide first airborne 3 backscatter +2 extinction high-spectral-resolution lidar developed by NASA Langley Research Center.

Improved identification of the solution space of aerosol microphysical properties derived from the inversion of profiles of lidar optical data, part 1: theory.

Multiwavelength Raman/high spectral resolution lidars that measure backscatter coefficients at 355, 532, and 1064 nm and extinction coefficients at 355 and 532 nm can be used for the retrieval of particle microphysical parameters, such as effective and mean radius, number, surface-area and volume concentrations, and complex refractive index, from inversion algorithms. In this study, we carry out a correlation analysis in order to investigate the degree of dependence that may exist between the optical data taken with lidar and the underlying microphysical parameters. We also investigate if the correlation properties identified in our study can be used as a priori or a posteriori constraints for our inversion scheme so that the inversion results can be improved. We made the simplifying assumption of error-free optical data in order to find out what correlations exist in the best case situation. Clearly, for practical applications, erroneous data need to be considered too. On the basis of simulations with synthetic optical data, we find the following results, which hold true for arbitrary particle size distributions, i.e., regardless of the modality or the shape of the size distribution function: surface-area concentrations and extinction coefficients are linearly correlated with a correlation coefficient above 0.99. We also find a correlation coefficient above 0.99 for the extinction coefficient versus (1) the ratio of the volume concentration to effective radius and (2) the product of the number concentration times the sum of the squares of the mean radius and standard deviation of the investigated particle size distributions. Besides that, we find that for particles of any mode fraction of the particle size distribution, the complex refractive index is uniquely defined by extinction- and backscatter-related Ångström exponents, lidar ratios at two wavelengths, and an effective radius.

Improved identification of the solution space of aerosol microphysical properties derived from the inversion of profiles of lidar optical data, part 2: simulations with synthetic optical data.

We developed a mathematical scheme that allows us to improve retrieval products obtained from the inversion of multiwavelength Raman/HSRL lidar data, commonly dubbed "3 backscatter+2 extinction" (3ß+2α) lidar. This scheme works independently of the automated inversion method that is currently being developed in the framework of the Aerosol-Cloud-Ecosystem (ACE) mission and which is successfully applied since 2012 [Atmos. Meas. Tech.7, 3487 (2014)10.5194/amt-7-3487-2014; "Comparison of aerosol optical and microphysical retrievals from HSRL-2 and in-situ measurements during DISCOVER-AQ 2013 (California and Texas)," in International Laser Radar Conference, July 2015, paper PS-C1-14] to data collected with the first airborne multiwavelength 3ß+2α high spectral resolution lidar (HSRL) developed at NASA Langley Research Center. The mathematical scheme uses gradient correlation relationships we presented in part 1 of our study [Appl. Opt.55, 9839 (2016)APOPAI0003-693510.1364/AO.55.009839] in which we investigated lidar data products and particle microphysical parameters from one and the same set of optical lidar profiles. For an accurate assessment of regression coefficients that are used in the correlation relationships we specially designed the proximate analysis method that allows us to search for a first-estimate solution space of particle microphysical parameters on the basis of a look-up table. The scheme works for any shape of particle size distribution. Simulation studies demonstrate a significant stabilization of the various solution spaces of the investigated aerosol microphysical data products if we apply this gradient correlation method in our traditional regularization technique. Surface-area concentration can be estimated with an uncertainty that is not worse than the measurement error of the underlying extinction coefficients. The retrieval uncertainty of the effective radius is as large as ±0.07 µm for fine mode particles and approximately 100% for particle size distributions composed of fine (submicron) and coarse (supermicron) mode particles. The volume concentration uncertainty is defined by the sum of the uncertainty of surface-area concentration and the uncertainty of the effective radius. The uncertainty of number concentration is better than 100% for any radius domain between 0.03 and 10 µm. For monomodal PSDs, the uncertainties of the real and imaginary parts of the CRI can be restricted to ±0.1 and ±0.01 on the domains [1.3; 1.8] and [0; 0.1], respectively.

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.

Vertically-resolved profiles of mass concentrations and particle backscatter coefficients of Asian dust plumes derived from lidar observations of silicon dioxide.

This study presents a method to retrieve vertically-resolved profiles of dust mass concentrations by analyzing Raman lidar signals of silicon dioxide (quartz) at 546nm. The observed particle plumes consisted of mixtures of East Asian dust with anthropogenic pollution. Our method for the first time allows for extracting the contribution of the aerosol component "pure dust" contained in the aerosol type "polluted dust". We also propose a method that uses OPAC (Optical Properties of Aerosols and Clouds) and the mass concentrations profiles of dust in order to derive profiles of backscatter coefficients of pure dust in mixed dust/pollution plumes. The mass concentration of silicon dioxide (quartz) in the atmosphere can be estimated from the backscatter coefficient of quartz. The mass concentration of dust is estimated by the weight percentage (38-77%) of mineral quartz in Asian dust. The retrieved dust mass concentrations are classified into water soluble, nucleation, accumulation, mineral-transported and coarse mode according to OPAC. The mass mixing ratio of 0.018, 0.033, 0.747, 0.130 and 0.072, respectively, is used. Dust extinction coefficients at 550nm were calculated by using OPAC and prescribed number concentrations for each of the 5 components. Dust backscatter coefficients were calculated from the dust extinction coefficients on the basis of a lidar ratio of 45±3sr at 532nm. We present results of quartz-Raman measurements carried out on the campus of the Gwangju Institute of Science and Technology (35.10°N, 126.53°E) on 15, 16, and 21 March 2010.

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 (ß)+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 "3ß+1α," "2ß+1α," and "3ß" 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 "3ß+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 "3ß+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 "3ß" configuration performs significantly poorer. The performance of the "3ß+1α" and "2ß+1α" configurations is intermediate between the "3ß+2α" and the "3ß."

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.

Columnar aerosol optical and radiative properties according to season and air mass transport pattern over East Asia.

The column-integrated optical and radiative properties of aerosols in the downwind area of East Asia were investigated based on sun/sky radiometer measurements performed from February 2004 to June 2005 at Gwangju (35.23° N, 126.84° E) and Anmyeon (36.54° N, 126.33° E), Korea. The observed aerosol data were analyzed for differences among three seasons: spring (March-May), summer (June-August), and autumn/winter (September-February). The data were also categorized into five types depending on the air mass origin in arriving in the measurement sites: (a) from a northerly direction in spring (S(N)), (b) from a westerly direction in spring (S(W)), (c) cases with a low Ångström exponent (<0.8) in spring (dust), (d) from a northerly direction in autumn/winter (AW(N)), and (e) from a westerly direction during other seasons (AW(W)). The highest Ångström exponents (α) at Gwangju and Anmyeon were 1.43 ± 0.30 and 1.49 ± 0.20, respectively, observed in summer. The lowest column-mean single-scattering albedo (ω) at 440 nm observed at Gwangju and Anmyeon were 0.89 ± 0.02 and 0.88 ± 0.02, respectively, during a period marked by the advection of dust from the Asian continent. The highest ω values at Gwangju and Anmyeon were 0.95 ± 0.02 and 0.96 ± 0.02, respectively, observed in summer. Variations in the aerosol radiative-forcing efficiency (ß) were related to the conditions of the air mass origin. The forcing efficiency in summer was -131.7 and -125.6 W m(-2) at the surface in Gwangju and Anmyeon, respectively. These values are lower than those under the atmospheric conditions of spring and autumn/winter. The highest forcing efficiencies in autumn/winter were -214.3 and -255.9 W m(-2) at the surface in Gwangju and Anmyeon, respectively, when the air mass was transported from westerly directions.

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.

Lidar measurements of Raman scattering at ultraviolet wavelength from mineral dust over East Asia.

We developed a novel measurement channel that utilizes Raman scattering from silicon dioxide (SiO2) quartz at an ultraviolet wavelength (361 nm). The excitation of the Raman signals is done at the primary wavelength of 355 nm emitted from a lidar instrument. In combination with Raman signals from scattering from nitrogen molecules, we may infer the mineral-quartz-related backscatter coefficient. This technique thus allows us to identify in a comparably direct way the mineral quartz content in mixed pollution plumes that consist, e.g., of a mix of desert dust and urban pollution. We tested the channel for the complex situation of East Asian pollution. We find good agreement of the inferred mineral-quartz-related backscatter coefficient to results obtained with another mineral quartz channel which was operated at 546 nm (primary emission wavelength at 532 nm), the functionality of which has already been shown for a lidar system in Tsukuba (Japan). The advantage of the novel channel is that it provides a better signal-to-noise ratio because of the shorter measurement wavelength.

Systematic error of lidar profiles caused by a polarization-dependent receiver transmission: quantification and error correction scheme.

Signals of many types of aerosol lidars can be affected with a significant systematic error, if depolarizing scatterers are present in the atmosphere. That error is caused by a polarization-dependent receiver transmission. In this contribution we present an estimation of the magnitude of this systematic error. We show that lidar signals can be biased by more than 20%, if linearly polarized laser light is emitted, if both polarization components of the backscattered light are measured with a single detection channel, and if the receiver transmissions for these two polarization components differ by more than 50%. This signal bias increases with increasing ratio between the two transmission values (transmission ratio) or with the volume depolarization ratio of the scatterers. The resulting error of the particle backscatter coefficient increases with decreasing backscatter ratio. If the particle backscatter coefficients are to have an accuracy better than 5%, the transmission ratio has to be in the range between 0.85 and 1.15. We present a method to correct the measured signals for this bias. We demonstrate an experimental method for the determination of the transmission ratio. We use collocated measurements of a lidar system strongly affected by this signal bias and an unbiased reference system to verify the applicability of the correction scheme. The errors in the case of no correction are illustrated with example measurements of fresh Saharan dust.

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.

Particle backscatter, extinction, and lidar ratio profiling with Raman lidar in south and north China.

Aerosol Raman lidar observations of profiles of the particle extinction and backscatter coefficients and the respective extinction-to-backscatter ratio (lidar ratio) were performed under highly polluted conditions in the Pearl River Delta (PRD) in southern China in October 2004 and at Beijing during a clear period with moderately polluted to background aerosol conditions in January 2005. The anthropogenic haze in the PRD is characterized by volume light-extinction coefficients of particles ranging from approximately 200 to 800 Mm(-1) and lidar ratios mostly between 40 and 55 sr (average of 47+/-6 sr). Almost clean air masses were observed throughout the measurements of the Beijing campaign. These air masses originated from arid desert-steppe-like regions (greater Gobi area). Extinction values usually varied between 100 and 300 Mm(-1), and the lidar ratios were considerably lower (compared with PRD values) with values mostly from 30 to 45 sr (average of 38+/-7 sr). Gobi dust partly influenced the observations. Unexpectedly low lidar ratios of approximately 25 sr were found for a case of background aerosol with a low optical depth of 0.05. The low lidar ratios are consistent with Mie-scattering calculations applied to ground-based observations of particle size distributions.