Lidar Inversion Research Articles

Lidar AOD inversion and aerosol extinction profile correction method based on GA-BP neural network

Lidar is an effective remote sensing method to obtain the vertical distribution of aerosols, and how to select the aerosol extinction-backscattering ratio (AE-BR) during the inversion process is a key step to guarantee the accuracy of the lidar inversion of aerosol optical thickness (AOD) and aerosol extinction coefficient profile (AECP). In this paper, an inversion algorithm for AOD and AECP based on a genetic BP (GA-BP) neural network is proposed. Simultaneous measurements are carried out using CE318 sun photometer and lidar, and the mapping relationship between the lidar echo signal and AOD is established based on the genetic BP (GA-BP) neural network method, which achieves the accurate inversion of AOD with an absolute error mean value of 0.0156. Based on the AOD output from the GA-BP neural network, the real-time best AE- BR to improve the inversion accuracy of AECP. Finally, practical tests show that the method achieves accurate inversion of AOD, determines the range of AE-BR from 20-50sr, realizes real-time dynamic correction of AECP, and has strong generalization ability and applicability in practical situations.

Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements

Abstract. In this paper a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. First, a review of the literature on the two-way transmittance method is presented. This method is a well-known lidar inversion method used to retrieve the optical properties of an aerosol–cloud layer between two molecular (i.e. aerosol and cloud-free) regions below and above, without the need to make any a priori assumptions about their optical and/or microphysical properties. Second, a simple mathematical expression of the two-way transmittance method is proposed for both ground-based and spaceborne lidar systems. This approach of the method allows the retrieval of the cloud optical depth, the cloud column lidar ratio and the vertical profile of the cloud backscatter coefficient. The method is illustrated for a cirrus cloud using measurements from the ground-based MPL and from the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Third, the database is then filtered with a cirrus identification criterion based on (and compared to) the literature using only lidar and radiosonde data. During the period from November 2018 to September 2022, 367 high-altitude cirrus clouds were identified at 00:00 and 12:00 UTC, of which 203 were successfully inverted with the two-way transmittance method. The statistical results of these 203 high-altitude cirrus clouds show that the cloud thickness is 1.8 ± 1.1 km, the mid-cloud temperature is −51 ± 8 ∘C and the linear cloud depolarization ratio is 0.32 ± 0.13. The application of the transmittance method yields an average cloud optical depth (COD) of 0.36 ± 0.45 and a mean effective column lidar ratio of 30 ± 19 sr. Statistical results of the errors associated with the two-way transmittance method retrievals are also provided. The highest occurrence of cirrus is observed in spring and the majority of cirrus clouds (48 %) are visible (0.03 < COD < 0.3), followed by opaque (COD > 0.3) with a percentage of 38 %. Together with results from other sites, possible latitudinal dependencies have been analysed together with correlations between cirrus cloud properties. For example, we noted that in Barcelona the COD correlates positively with the cloud base temperature, effective column lidar ratio and linear cloud depolarization ratio and negatively with the cloud base height.

Combined sun-photometer–lidar inversion: lessons learned during the EARLINET/ACTRIS COVID-19 campaign

Abstract. The European Aerosol Research Lidar Network (EARLINET), part of the Aerosols, Clouds and Trace gases Research Infrastructure (ACTRIS), organized an intensive observational campaign in May 2020, with the objective of monitoring the atmospheric state over Europe during the COVID-19 lockdown and relaxation period. Besides the standard operational processing of the lidar data in EARLINET, for seven EARLINET sites having collocated sun-photometric observations in the Aerosol Robotic Network (AERONET), a network exercise was held in order to derive profiles of the concentration and effective column size distributions of the aerosols in the atmosphere, by applying the GRASP/GARRLiC (from Generalized Aerosol Retrieval from Radiometer and Lidar Combined data – GARRLiC – part of the Generalized Retrieval of Atmosphere and Surface Properties – GRASP) inversion algorithm. The objective of this network exercise was to explore the possibility of identifying the anthropogenic component and of monitoring its spatial and temporal characteristics in the COVID-19 lockdown and relaxation period. While the number of cases is far from being statistically significant so as to provide a conclusive description of the atmospheric aerosols over Europe during this period, this network exercise was fundamental to deriving a common methodology for applying GRASP/GARRLiC to a network of instruments with different characteristics. The limits of the approach are discussed, in particular the missing information close to the ground in the lidar measurements due to the instrument geometry and the sensitivity of the GRASP/GARRLiC retrieval to the settings used, especially for cases with low aerosol optical depth (AOD) like the ones we show here. We found that this sensitivity is well-characterized in the GRASP/GARRLiC products, since it is included in their retrieval uncertainties.

Investigation of the Vertical Distribution Characteristics and Microphysical Properties of Summer Mineral Dust Masses over the Taklimakan Desert Using an Unmanned Aerial Vehicle

Investigating the vertical distribution of mineral dust masses and their microphysical properties is crucial for accurately assessing the climate effects of dust. However, there are limited studies related to relevant in situ observations over dust source areas. In this study, the near-surface vertical characteristics (within 500 m a.g.l) of dust mass concentrations in five size fractions (PMs: TSP, PM10, PM4, PM2.5, and PM1) were investigated using an unmanned aerial vehicle (UAV) in Tazhong (TZ) in the Taklimakan Desert (TD) in July 2021. To the best of our knowledge, the vertical profiles of particle number concentration (PNC), effective radius (Reff), and volume concentration (Cv) were obtained for the first time by UAV over the TD. Four scenarios of clear sky, floating dust, blowing sand, and dust storm were selected based on the classification criteria for PMs. The PMs, PNC, Reff, and Cv decreased with height for all scenarios. From clear-sky to dust-storm scenarios PMs, PNC, Reff, and Cv in the column gradually increased. Reff (Cv) increased from 1.15 μm (0.08 μm3/μm2) to 4.53 μm (0.74 μm3/μm2). The diurnal variations of PMs, PNC, and Reff (Cv) revealed a unimodal pattern, with the peak occurring between 13:00 and 16:00, due to the evolution of wind speed and the atmospheric boundary layer in TZ. Unexpectedly, among the three postprecipitation scenarios (P1, P2, and P3), the PNC of P2 was smaller than those of P1 and P3. The Reff (Cv) for P2 was similar to or greater than that for dust storms, which may be associated with moist dust particles on the ground surface being carried into the air by wind. These investigations add to our understanding of the mineral dust vertical characteristics over the source area, and provide a meaningful reference for colocated lidar inversion and dust simulations.

Performance Evaluation of an A Band Differential Absorption LIDAR Model and Inversion for the Ocean Surface Pressure from Low-Earth Orbit

Remote sensing of ocean surface pressure from space is very important, and differential absorption LIDAR and differential absorption radar are only two kinds of remote sensing instruments with this potential. The differential absorption LIDAR works with the integral path mode from the spacecraft in the 400 km low-Earth orbit. The differential optical depth of the oxygen A-band is measured, and then the ocean surface pressure is obtained using a circle-iterative calculation. Performance evaluation of the differential absorption LIDAR model was based on feasibility to the advanced system parameters of the space instrument, whilst weak echo pulse energy at ocean surface yielded random errors in the surface pressure measurement. On the other hand, uncertain atmospheric temperature profiles and water vapor mixture profiles resulted in a primary systematic error in the surface pressure. The error of the surface pressure is sensitive to the jitter of the central frequency of laser emission. Under a strict implementation of the error budget, the time resolution is 6.25 s and the along-orbit distance resolution is 44 km, 625 echoes from ocean surface was cumulatively averaged. Consequently, if the jitter of the central frequency of laser emission exceeded 10 MHz, controlling the error of the surface pressure below 0.1% proved almost hopeless; further, the error could be expected to within 0.1–0.2%; however, the error limited within 0.2–0.3% is an achievable indicator.

Algorithm for vertical distribution of boundary layer aerosol components in remote-sensing data

Abstract. The vertical distribution of atmospheric aerosol components is vital to the estimation of radiative forcing and the catalysis of atmospheric photochemical processes. Based on the synergy of ground-based lidar and sun-photometer in Generalized Aerosol Retrieval from Radiometer and Lidar Combined data (GARRLiC), this paper developed a new algorithm to get the vertical mass concentration profiles of fine-mode aerosol components for the first time. Retrieval of aerosol properties was achieved based on the sky radiance at multiple scatter angles, total optical depth (TOD) at 440, 675, 870, and 1020 nm, and lidar signals at 532 and 1064 nm. In addition, the internal mixing model and normalized volume size distribution (VSD) model were established according to the absorption and water solubility of the aerosol components, to separate the profiles of black carbon (BC), water-insoluble organic matter (WIOM), water-soluble organic matter (WSOM), ammonium nitrate-like (AN), and fine aerosol water (AW) content. Results showed a reasonable vertical distribution of aerosol components compared with in situ observations and reanalysis data. The estimated and observed BC concentrations matched well with a correlation coefficient up to 0.91, while there was an evident overestimation of organic matter (OM = WIOM + WSOM, NMB = 0.98). Moreover, the retrieved AN concentrations were closer to the simulated results (R = 0.85), especially in polluted conditions. The BC and OM correlations were relatively weaker, with a correlation coefficient of ∼ 0.5. Besides, the uncertainties caused by the input parameters (i.e., relative humidity (RH), volume concentration, and extinction coefficients) were assessed using the Monte Carlo method. The AN and AW had smaller uncertainties at higher RH. Herein, the proposed algorithm was also applied to remote-sensing measurements in Beijing with two typical cases. In the clean condition with low RH, there were comparable AN and WIOM, but peaking at different altitudes. On the other hand, in the polluted case, AN was dominant and the maximum mass concentration occurred near the surface. We expected that the algorithm could provide a new idea for lidar inversion and promote the development of aerosol component profiles.

Numerical Weather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products

The atmospheric molecular number density can be obtained from atmospheric temperature and pressure profiles and is a significant input parameter for the inversion of lidar measurements. When measurements of vertical profiles of temperature and pressure are not available, atmospheric models are typically considered a valid alternative option. This paper investigates the influence of different atmospheric models (forecast and reanalysis) on the retrieval of aerosol optical properties (extinction and backscatter coefficients) by applying Raman and elastic-only methods to lidar measurements, to assess their use in lidar data processing. In general, reanalyzes are more accurate than forecasts, but, typically, they are not delivered in time for allowing near-real-time lidar data analysis. However, near-real-time observation is crucial for real-time monitoring of the environment and meteorological studies. The forecast models used in the paper are provided by the Integrated Forecasting System operated by the European Centre for Medium-Range Weather Forecasts (IFS_ECMWF) and the Global Data Assimilation System (GDAS), whereas the reanalysis model is obtained from the fifth-generation European Centre for Medium-Range Weather Forecasts ReAnalysis v5 (ERA5). The lidar dataset consists of measurements collected from four European Aerosol Research Lidar Network (EARLINET) stations during two intensive measurement campaigns and includes more than 200 cases at wavelengths of 355 nm, 532 nm, and 1064 nm. We present and discuss the results and influence of the forecast and reanalysis models in terms of deviations of the derived aerosol optical properties. The results show that the mean relative deviation in molecular number density is always below ±3%, while larger deviations are shown in the derived aerosol optical properties, and the size of the deviation depends on the retrieval method together with the different wavelengths. In general, the aerosol extinction coefficient retrieval is more dependent on the model used than the aerosol backscatter retrievals are. The larger influence on the extinction retrieval is mainly related to the deviation in the gradient of the temperature profile provided by forecast and reanalysis models rather than the absolute deviation of the molecular number density. We found that deviations in extinction were within ±5%, with a probability of 83% at 355 nm and 60% at 532 nm. Moreover, for aerosol backscatter coefficient retrievals, different models can have a larger impact when the backscatter coefficient is retrieved with the elastic method than when the backscatter coefficient is calculated using the Raman method at both 355 nm and 532 nm. In addition, the atmospheric aerosol load can also influence the deviations in the aerosol extinction and backscatter coefficients, showing a larger impact under low aerosol loading scenarios.

Optimal estimation method applied on ceilometer aerosol retrievals

The solution of the lidar equation is an ill-posed problem that requires nonlinear methods to retrieve the atmospheric aerosol optical and microphysical properties. Particularly, in the last decades, the most applied solution for the elastic lidars is through the well known Klett-Fernald-Sasano algorithm for retrieving the backscatter coefficient. To solve this inversion problem, we propose to apply the optimal estimation method to a Vaisala CL51 ceilometer range corrected signals for retrieving under two different frameworks, the particle backscatter coefficient or the ratio and the lidar constant. The optimal estimation is a Bayesian inversion fed by a set of a priori information. In this work, to obtain the suitable prior, we have tested two approaches that involved measurements and synthetic data. The first data set was obtained from previous inversions using the classical Klett-Fernald-Sasano method, and the second one by using Mie simulations fed by aerosol properties from OPAC database.The optimal estimation method used for elastic lidar inversion presents two main advantages compared to the classic approaches. On one hand, there is no need for Rayleigh zone determination and on the other hand, the uncertainty of the retrieved products is directly estimated, therefore the quality of the results is highly dependant on the prior selection. To evaluate the performance of the model, low and high aerosol accumulations scenarios were considered, finding that the backscatter coefficient was oscillating between 5 and 7 (kmsr)−1 in the first 3 km agl with uncertainties lower than 27 %at degraded spatial resolutions. Additionally, constant and height-dependent priors were tested reaching relative errors in percentage up to 5% between them. Besides, relative errors were also analyzed for the prior covariance matrices estimated either from synthetic lidar data and Klett's retrievals, where the errors are lower than 2% by using one instead of the another. However, scale factors were applied to the synthetic prior covariance matrices to reach the convergence.The results at retrieving the particle backscattering were compared to those ones estimated from Klett's inversion, considering Klett inversion as the reference. For the extreme scenario of inversions, considering aerosol accumulations at different layers, the bias between the optimized profiles was lower than −0.5 (kmsr)−1 in the first 0.5 km and 0.5 (kmsr)−1 above 1.5 km. Here, we also shown a two-parameter optimization for the lidar constant and lidar ratio, applied to 39 aerosol inversions, finding relative errors lower than 1 % and 2.3 %, respectively, considering those ones from Klett inversion as the reference.

A new lidar inversion method using a surface reference target applied to the backscattering coefficient and lidar ratio retrievals of a fog-oil plume at short range

Abstract. In this paper, a new elastic lidar inversion equation is presented. It is based on the backscattering signal from a surface reference target (SRT) rather than that from a volumetric layer of reference (Rayleigh molecular scatterer) as is usually done. The method presented can be used when the optical properties of such a layer are not available, e.g., in the case of airborne elastic lidar measurements or when the lidar–target line is horizontal Also, a new algorithm is described to retrieve the lidar ratio and the backscattering coefficient of an aerosol plume without any a priori assumptions about the plume. In addition, our algorithm allows a determination of the instrumental constant. This algorithm is theoretically tested, viz. by means of simulated lidar profiles and then using real measurements. Good agreement with available data in the literature has been found.

Optimized profile retrievals of aerosol microphysical properties from simulated spaceborne multiwavelength Lidar

This work is an expanded study of one previously published on retrievals of aerosol microphysical properties from space-borne multiwavelength lidar measurements. The earlier studies and this one were done in the framework of the NASA Aerosol-Clouds-Ecosystems (now the Aerosol Clouds Convection and Precipitation) NASA mission. The focus here is on the capabilities of a simulated spaceborne multiwavelength lidar system for retrieving aerosol complex refractive index (m = mr + imi) and spectral single scattering albedo (SSA(λ)), although other bulk parameters such as effective (reff) radius and particle volume (V) and surface (S) concentrations are also studied. The novelty presented here is the use of recently published, case-dependent optimized-constraints on the microphysical retrievals using three backscattering coefficients (β) at 355, 532 and 1064 nm and two extinction coefficients (α) at 355 and 532 nm, typically known as the stand-alone 3β + 2α lidar inversion. Case-dependent optimized-constraints (CDOC) limit the ranges of refractive index, both real (mr) and imaginary (mi) parts, and of radii that are permitted in the retrievals. Such constraints are selected directly from the 3β + 2α measurements through an analysis of the relationship between spectral dependence of aerosol extinction-to-backscatter ratios (LR) and the Ångström exponent of extinction. The analyses presented here for different sets of size distributions and refractive indices reveal that the direct determination of CDOC are only feasible for cases where the uncertainties in the input optical data are less than 15%. For the same simulated spaceborne system and yield than in Whiteman et al., (2018), we demonstrated that the use of CDOC as essential for the retrievals of refractive index and also largely improved retrieval of bulk parameters. A discussion of the global representativeness of CDOC is presented using simulated lidar data from a 24 h satellite track using GEOS model output to initialize the lidar simulator. We found that CDOC are representative of many aerosol mixtures in spite of some outliers (e.g. highly hydrated particles) associated with the assumptions of bimodal size distributions and of the same refractive index for fine and coarse modes. Moreover, sensitivity tests performed using synthetic data reveal that retrievals of imaginary refractive index (mi) and SSA are extremely sensitive to β(355).

Preprocessing of Raman Lidar Signal Over a High Altitude Station in India: Practical Considerations

Lidar can provide the range-resolved information about the vertical profiles of optical properties of aerosol content and clouds present in the atmosphere. However, before obtaining the information from received lidar signal we ought to use a few pre-processing techniques such as range-correction, temporal and spatial averaging, dead time correction of the photon counts (PC) signal, correction due to overlap effect, Simulation of molecular Rayleigh signal and gluing analog & PC signals. In this work, we have discussed some of the initial pre-processing techniques ought to be performed before lidar inversion.

AN AEROSOL LIDAR DATA VISUALIZATION METHOD BASED ON ARCGIS

Abstract. The geographical information, temporal and spatial distribution of pollution sources cannot be accurately described by the traditional aerosol lidar inversion data. In order to accurately locate the pollution sources and monitor the spatial and temporal distribution of pollutants, an aerosol lidar data visualization method based on ArcGIS is proposed in this paper. A natural neighborhood method is used to perform group interpolation on ArcGIS software, and the range range-extinction coefficient relationship diagram is plotted using the aerosol optical data obtained from Klett inversion algorithm. The geographic information is added to the lidar inversion data in this diagram. The aerosol spatial and temporal distribution range and the direction of aerosol diffusion are accurately displayed directly. The change of aerosol concentration is plotted in different colors, the visually displaying the concentration of pollutants in the detection area is realized. The aerosol detection data in the sunny and haze days in Yinchuan are visualized in this paper. The result shows that this method can quickly determine the geographical location of pollution sources and visualize the spatial and temporal distribution of aerosols. This method can be used to display aerosol data with geographic information for meteorological and environmental protection departments. This method can provide data support for meteorological and environmental protection departments to quickly determine the location and concentration of pollution sources.

Retrievals of aerosol single scattering albedo by multiwavelength lidar measurements: Evaluations with NASA Langley HSRL-2 during discover-AQ field campaigns

This work focuses on the study and evaluation of the retrievals of aerosol complex refractive index (m = mr + imi) and single scattering albedo (SSA) from the inversion of multi-wavelength lidar measurements, particularly of three backscattering coefficients (β) at 355, 532 and 1064 nm and two extinction coefficients (α) at 355 and 532 nm, typically known as the stand-alone 3β + 2α lidar inversion. The focus is on the well-known regularization technique for spherical particles. It is well known that constraints in the range of refractive indices allowed in the inversion are essential, both for the real (mr) and imaginary (mi) parts, due to the under-determined nature of the problem. Usually these constraints are fixed for a given set of inversions. Using a large database of AERONET retrievals, correlations between retrieved mr and mi are observed and those correlations together with results from the GOCART model are used to define optimized, case-dependent, constraints in the stand-alone 3β + 2α lidar inversion. For each inversion performed, the optimized constraints are computed from the 3β + 2α data using a-priori information of extinction-to-backscattered ratio (LR) and the Angstrom exponent computed with α at 355 and 532 nm. The stand-alone 3β + 2α lidar inversion with optimized, case-dependent, constraints is applied to airborne NASA LaRC HSRL-2 experimental measurements during DISCOVER-AQ. The optimized constraints selected from the measured 3β + 2α are compared with the typing classification based on additional multiwavelength depolarization measurements, showing consistency between aerosol size and absorption range and aerosol typing. Evaluations of the SSA retrieved by the stand-alone 3β + 2α lidar inversion with optimized constraints are done by comparisons with correlative airborne in-situ measured SSA. The agreement between both methodologies is satisfactory for most aerosol types as differences are within the uncertainties of each methodology.

Noise reduction and retrieval by modified lidar inversion method combines joint retrieval method and machine learning

To address the problem in which the signal-to-noise ratio of a raw atmospheric lidar signal decreases rapidly as the range increases, which has a tremendous effect on the accuracy and the effective range of lidar retrieval, many de-noising algorithms have been proposed. Among these methods, those based on the ensemble Kalman Filter (EnKF) exhibit good performance. EnKF-based methods can simultaneously denoise lidar signals and yield accurate retrieval results. However, due to poor forecasting in the EnKF step, biases exist in the results of these methods. In this study, a modified lidar inversion method was proposed for horizontal aerosol characteristic retrieval, which combines the joint retrieval method and Gaussian processing machine learning. This method compensates for the poor forecasting in the EnKF step in the joint retrieval method through the Gaussian processing machine learning algorithm, which can reduce the biases in the retrieval results. The modified lidar inversion method was applied to both simulated and real lidar signals, and the results show that the modified lidar inversion method is effective and practical in aerosol extinction characteristics’ analysis.

Optical properties of an industrial fire observed with a ground based N2-Raman lidar over the Paris area

This paper presents the first, to our knowledge, lidar measurement of an industrial fire plume, which covered the north of the Paris area on 17th April 2015. The fire started in a textile warehouse and rapidly spread by emitting large quantities of aerosols into the low troposphere. A ground based N2-Raman lidar performed continuous measurements during this event. Vertical profiles of the aerosol extinction coefficient, depolarization and lidar ratio are derived. A Monte Carlo algorithm was used to assess the uncertainties on the optical parameters, and to evaluate lidar inversion methods.

Mini N2-Raman Lidar Onboard Ultra-Light Aircraft for Aerosol Measurements: Demonstration and Extrapolation

Few airborne aerosol research experiments have deployed N2-Raman Lidar despite its capability to retrieve aerosol optical properties without ambiguity. Here, we show the high scientific potential of this instrument when used with specific flight plans. Our demonstration is based on (i) a field-experiment conducted in June 2015 in southern France, involving a N2-Raman Lidar embedded on an ultra-light aircraft (ULA); and (ii) an appropriate algorithmic approach using two-level flight levels, aiming to solve the notorious instability of the airborne Lidar inversion for the retrieval of aerosol optical properties. The Lidar measurements include the determination of the aerosol extinction coefficient along ~500 m horizontal line of sight, and this value is used as a reference to validate the proposed algorithm. The Lidar-derived vertical profiles obtained during the flights are used as an input in a Monte Carlo simulation in order to compute the error budget in terms of biases and standard deviations on the retrieved aerosol extinction coefficient profile, as well as the subsequent optical thickness. The influence of the Lidar ratio (i.e., between aerosol extinction and backscatter) on the error budget is further discussed. Finally, from this end-to-end modeling, an optimal N2-Raman Lidar is proposed for airborne experiments, adapted to both small and large carriers.

Two-wavelength Lidar inversion algorithm for determining planetary boundary layer height

This study proposes a two-wavelength Lidar inversion algorithm to determine the boundary layer height (BLH) based on the particles clustering. Color ratio and depolarization ratio are used to analyze the particle distribution, based on which the proposed algorithm can overcome the effects of complex aerosol layers to calculate the BLH. The algorithm is used to determine the top of the boundary layer under different mixing state. Experimental results demonstrate that the proposed algorithm can determine the top of the boundary layer even in a complex case. Moreover, it can better deal with the weak convection conditions. Finally, experimental data from June 2015 to December 2015 were used to verify the reliability of the proposed algorithm. The correlation between the results of the proposed algorithm and the manual method is R2 = 0.89 with a RMSE of 131 m and mean bias of 49 m; the correlation between the results of the ideal profile fitting method and the manual method is R2 = 0.64 with a RMSE of 270 m and a mean bias of 165 m; and the correlation between the results of the wavelet covariance transform method and manual method is R2 = 0.76, with a RMSE of 196 m and mean bias of 23 m. These findings indicate that the proposed algorithm has better reliability and stability than traditional algorithms.

A sensitivity study of the LIdar-Radiometer Inversion Code (LIRIC) using selected cases from Thessaloniki, Greece database

ABSTRACTWe investigate the uncertainty introduced to the optical and microphysical properties estimated with the lidar radiometer inversion code (LIRIC) by user-defined input parameters based on measurements carried out with a multi-wavelength Raman lidar and a sun photometer located at Thessaloniki, Greece (40.6° N, 22.9° E, 60 m above sea level). The sensitivity study involves three tests. We first evaluate the selection of the regularization parameters needed for the algorithm to initialize the iteration process. The latter two tests consider the impact of the boundary limits at the top/bottom (upper/lower limit) of the signal to the derived concentration profiles. The aforementioned tests were applied to two different cases, a Saharan dust event and a continental pollution case. We concluded that the largest uncertainties are introduced when varying the lower limit (more than 35%) regardless of the aerosol type or mode (fine/coarse). Varying the regularization parameters resulted in an uncertainty of 20%, and the selection of upper limit led to discrepancies of less than 3%. In conclusion, this sensitivity study indicates that future LIRIC users should apply an overlap function to the lidar signals before applying the methodology for minimizing the uncertainties in the near range.

Retrievals of aerosol microphysics from simulations of spaceborne multiwavelength lidar measurements

In support of the Aerosol, Clouds, Ecosystems mission, simulations of a spaceborne multiwavelength lidar are performed based on global model simulations of the atmosphere along a satellite orbit track. The yield for aerosol microphysical inversions is quantified and comparisons are made between the aerosol microphysics inherent in the global model and those inverted from both the model's optical data and the simulated three backscatter and two extinction lidar measurements, which are based on the model's optical data. We find that yield can be significantly increased if inversions based on a reduced optical dataset of three backscatter and one extinction are acceptable. In general, retrieval performance is better for cases where the aerosol fine mode dominates although a lack of sensitivity to particles with sizes less than 0.1 µm is found. Lack of sensitivity to coarse mode cases is also found, in agreement with earlier studies. Surface area is generally the most robustly retrieved quantity. The work here points toward the need for ancillary data to aid in the constraints of the lidar inversions and also for joint inversions involving lidar and polarimeter measurements.

Lidar equation inversion methods and uncertainties in measuring fugitive particulate matter emission factors.

Measurements from two field campaigns that employed a micropulse lidar are used to compare the near-end and the far-end lidar equation inversion methods for estimating emission factors (EFs) of particulate matter (PM) from three types of anthropogenic fugitive sources: vehicles moving on unpaved roads, open burning, and open detonation. As optical depth increased from 0 to 2, relative EF uncertainty increased from 54% to 300% using the near-end method and decreased from 69% to 42% using the far-end method. To the best of our knowledge, this research is the first to use field measurements to compare results from these methods for anthropogenic PM plumes and quantify their uncertainties.