Aerosol Extinction Coefficient Profiles Research Articles

Capabilities of an Automatic Lidar Ceilometer to Retrieve Aerosol Characteristics within the Planetary Boundary Layer

Continuous observation and quantitative retrieval of aerosol backscatter coefficients are important in the study of air quality and climate in metropolitan areas such as New York City. Ceilometers are ideal for this application, but aerosol backscatter coefficient retrievals from ceilometers are challenging and require proper calibration. In this study, we calibrate the ceilometer (Lufft CHM15k, 1064 nm) system constant with the molecular backscatter coefficient and evaluate the calibrated profiles with other independent methods, including the water-phase cloud method and comparison with the NASA Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) attenuated backscatter coefficient profile. Multiple-day calibration results show a stable system constant with a relative uncertainty of about 7%. We also evaluate the overlap correction for the CHM15k ceilometer (provided by Lufft) with a Vaisala CL-31 ceilometer, and the results show good consistency between two ceilometers’ range-corrected signal (RCS) profiles above 200 m. Next, we implement a forward iterative method to retrieve aerosol backscatter coefficients from continuous ceilometer measurements. In the retrieval, the lidar ratio is constrained by the co-located NASA AERONET radiometer aerosol optical depth (AOD) retrieval and agrees with the AERONET lidar-ratio products, derived from aerosol microphysical parameters. The aerosol backscatter coefficient retrievals are validated with co-located elastic-Raman lidar retrievals and indicate a good correlation (R2≥0.95) in the planetary boundary layer (PBL). Furthermore, a case study shows that the ceilometer retrieved aerosol extinction coefficient profiles can be used to estimate the AOD of the PBL and the aloft plumes. Finally, simulations of the uncertainty of aerosol backscatter coefficient retrieval show that a calibration error of 10% results in 10–20% of relative error in the aerosol backscatter coefficient retrievals, while relative error caused by a lidar-ratio error of 10% is less than 4% in the PBL.

Real-time tracing VOCs, O3 and PM2.5 emission sources with vehicle-mounted proton transfer reaction mass spectrometry combined differential absorption lidar

Owing to rapid economic development and urbanization, environmental problems caused by PM2.5 and O3 have been occurring frequently. Volatile organic compounds (VOCs) are significant precursors of O3 and secondary organic aerosols that contribute to PM2.5. The study of VOC pollution characteristics is of great significance for providing a reference to guide its reduction policy formulation. Proton transfer reaction mass spectrometry has been widely used to study the emissions, distributions, and chemical evolution of VOCs in the atmosphere. A differential absorption lidar is often used to detect O3 concentration profiles and aerosol extinction coefficient profiles in low troposphere. In this study, a novel vehicle-mounted differential absorption lidar combined with a PTR-MS system is used to track environmental VOCs, particulate matter and O3 emission sources in real time. Plane scanning results can determine the contaminated areas on a large scale. The results of particulate matter navigation and fixed vertical detection both showed that local pollution and high-altitude input pollution together lead to poor air quality. Navigation experimental results show that the pollution sources are mainly concentrated in the vicinity of traffic intersections, polluting enterprises and residential areas. O3, particulate matter and VOCs have the same high value region.

Intercomparison of MAX-DOAS vertical profile retrieval algorithms: studies on field data from the CINDI-2 campaign

Abstract. The second Cabauw Intercomparison of Nitrogen Dioxide measuring Instruments (CINDI-2) took place in Cabauw (the Netherlands) in September 2016 with the aim of assessing the consistency of multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements of tropospheric species (NO2, HCHO, O3, HONO, CHOCHO and O4). This was achieved through the coordinated operation of 36 spectrometers operated by 24 groups from all over the world, together with a wide range of supporting reference observations (in situ analysers, balloon sondes, lidars, long-path DOAS, direct-sun DOAS, Sun photometer and meteorological instruments). In the presented study, the retrieved CINDI-2 MAX-DOAS trace gas (NO2, HCHO) and aerosol vertical profiles of 15 participating groups using different inversion algorithms are compared and validated against the colocated supporting observations, with the focus on aerosol optical thicknesses (AOTs), trace gas vertical column densities (VCDs) and trace gas surface concentrations. The algorithms are based on three different techniques: six use the optimal estimation method, two use a parameterized approach and one algorithm relies on simplified radiative transport assumptions and analytical calculations. To assess the agreement among the inversion algorithms independent of inconsistencies in the trace gas slant column density acquisition, participants applied their inversion to a common set of slant columns. Further, important settings like the retrieval grid, profiles of O3, temperature and pressure as well as aerosol optical properties and a priori assumptions (for optimal estimation algorithms) have been prescribed to reduce possible sources of discrepancies. The profiling results were found to be in good qualitative agreement: most participants obtained the same features in the retrieved vertical trace gas and aerosol distributions; however, these are sometimes at different altitudes and of different magnitudes. Under clear-sky conditions, the root-mean-square differences (RMSDs) among the results of individual participants are in the range of 0.01–0.1 for AOTs, (1.5–15) ×1014molec.cm-2 for trace gas (NO2, HCHO) VCDs and (0.3–8)×1010molec.cm-3 for trace gas surface concentrations. These values compare to approximate average optical thicknesses of 0.3, trace gas vertical columns of 90×1014molec.cm-2 and trace gas surface concentrations of 11×1010molec.cm-3 observed over the campaign period. The discrepancies originate from differences in the applied techniques, the exact implementation of the algorithms and the user-defined settings that were not prescribed. For the comparison against supporting observations, the RMSDs increase to a range of 0.02–0.2 against AOTs from the Sun photometer, (11–55)×1014molec.cm-2 against trace gas VCDs from direct-sun DOAS observations and (0.8–9)×1010molec.cm-3 against surface concentrations from the long-path DOAS instrument. This increase in RMSDs is most likely caused by uncertainties in the supporting data, spatiotemporal mismatch among the observations and simplified assumptions particularly on aerosol optical properties made for the MAX-DOAS retrieval. As a side investigation, the comparison was repeated with the participants retrieving profiles from their own differential slant column densities (dSCDs) acquired during the campaign. In this case, the consistency among the participants degrades by about 30 % for AOTs, by 180 % (40 %) for HCHO (NO2) VCDs and by 90 % (20 %) for HCHO (NO2) surface concentrations. In former publications and also during this comparison study, it was found that MAX-DOAS vertically integrated aerosol extinction coefficient profiles systematically underestimate the AOT observed by the Sun photometer. For the first time, it is quantitatively shown that for optimal estimation algorithms this can be largely explained and compensated by considering biases arising from the reduced sensitivity of MAX-DOAS observations to higher altitudes and associated a priori assumptions.

Development of a three-dimensional variational assimilation system for lidar profile data based on a size-resolved aerosol model in WRF–Chem model v3.9.1 and its application in PM<sub>2.5</sub> forecasts across China

Abstract. The authors developed a three-dimensional variational (3-DVAR) aerosol extinction coefficient (AEC) and aerosol mass concentration (AMC) data assimilation (DA) system for aerosol variables in the Weather Research and Forecasting–Chemistry (WRF–Chem) model with the WRF–Chem using the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) scheme. They establish an AEC observation operator and its corresponding adjoint based on the Interagency Monitoring of Protected Visual Environments (IMPROVE) equation and investigate the use of lidar AEC and surface AMC DA to forecast mass concentration (MC) profiles of PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 µm) across China. Two sets of data were assimilated: AEC profiles captured by five conventional Mie scattering lidars (positioned in Beijing, Shijiazhuang, Taiyuan, Xuzhou, and Wuhu) and PM2.5 and PM10 MC data obtained from over 1500 ground environmental monitoring stations across China. Three DA experiments (i.e., a PM2.5 (PM10) DA experiment, a lidar AEC DA experiment, and a simultaneous PM2.5 (PM10) and lidar AEC DA experiment) with a 12 h assimilation period and a 24 h forecast period were conducted. The PM2.5 (PM10) DA reduced the root mean square error (RMSE) of the surface PM2.5 MC in the initial field of the model by 38.6 µg m−3 (64.8 %). When lidar AEC data were assimilated, this reduction was 10.5 µg m−3 (17.6 %), and a 38.4 µg m−3 (64.4 %) reduction occurred when the two data sets were assimilated simultaneously, although only five lidars were available within the simulation region (approximately 2.33 million km2 in size). The RMSEs of the forecasted surface PM2.5 MC 24 h after the DA period in the three DA experiments were reduced by 6.1 µg m−3 (11.8 %), 1.5 µg m−3 (2.9 %), and 6.5 µg m−3 (12.6 %), respectively, indicating that the assimilation and hence the optimization of the initial field have a positive effect on the PM2.5 MC forecast performance over a period of 24 h after the DA period.

Triple charge-coupled device cameras combined backscatter lidar for retrieving PM2.5 from aerosol extinction coefficient.

The vertical aerosol extinction coefficient profile is the key to evaluating the aerosol radiation effect. In addition, it is also the premise of indirect inversion of PM2.5 concentration from the lidar signal. A novel lidar system combining tripe charge-coupled device (CCD) cameras and backscatter lidar is proposed to retrieve accurately the aerosol extinction coefficient. The relative humidity segmentation method is used to convert the aerosol extinction coefficient into PM2.5 concentrations. The results show that the combination of triple CCDs and backscatter lidar can obtain a wider-range extinction coefficient profile. The variation trend of fitted PM2.5 concentrations and PM2.5 concentrations measured by the in situ instrument show a high correlation. The correlation coefficient reaches 0.850.

A feasibility study to use machine learning as an inversion algorithm for aerosol profile and property retrieval from multi-axis differential absorption spectroscopy measurements

Abstract. In this study, we explore a new approach based on machine learning (ML) for deriving aerosol extinction coefficient profiles, single-scattering albedo and asymmetry parameter at 360 nm from a single multi-axis differential optical absorption spectroscopy (MAX-DOAS) sky scan. Our method relies on a multi-output sequence-to-sequence model combining convolutional neural networks (CNNs) for feature extraction and long short-term memory networks (LSTMs) for profile prediction. The model was trained and evaluated using data simulated by Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) v2.7, which contains 1 459 200 unique mappings. From the simulations, 75 % were randomly selected for training and the remaining 25 % for validation. The overall error of estimated aerosol properties (1) for total aerosol optical depth (AOD) is -1.4±10.1 %, (2) for the single-scattering albedo is 0.1±3.6 %, and (3) for the asymmetry factor is -0.1±2.1 %. The resulting model is capable of retrieving aerosol extinction coefficient profiles with degrading accuracy as a function of height. The uncertainty due to the randomness in ML training is also discussed.

Retrieval of the aerosol extinction coefficient of 1064nm based on high-spectral-resolution lidar

It is difficult to independently obtain aerosol optical parameters at the near-infrared wavelength using lidar. A new high-spectral-resolution technology is proposed to obtain the aerosol extinction coefficient profile at 1064 nm. The high-spectral-resolution detection method by extracting Mie scattering and suppressing Cabannes–Brillouin scattering is applied in the lidar at 1064 nm to improve the detection signal-to-noise ratio. The system parameters of near-infrared high-spectral-resolution LiDAR (NHSRL) must be calibrated in this method; then, the optical parameters are accurately deduced. The effect of the system parameters (transmittances of the spectral filter for molecule and aerosol) on the detection results in this method is discussed and analysed. The transmittance of the spectral filter for aerosol scattering is a key parameter in the retrieval of the extinction coefficient, and the sensitivity of the inversion results to the transmittance is analysed and discussed. The method to distinguish the large deviation data of the extinction coefficient is discussed and provided, and the third derivative of the optical thickness profile is adopted to determine the data. According to the characteristics of the inversion signal, the method to correct the extinction coefficient profile is also presented. The correction algorithm can obviously increase the inversion accuracy of the extinction coefficient. The 1064-nm NHSRL Lidar was developed at Xi'an University of Technology. A dual-channel Fabry-Perot Etalon was designed and used to extract the aerosol Mie scattering signal and suppress Cabannes–Brillouin scattering in this NHSRL. The NHSRL lidar observations were performed, and extinction coefficient profiles at 1064 nm were obtained. The results demonstrate that the method can obtain an accurate profile of the extinction coefficients.

Determination of Lidar Ratio for Major Aerosol Types over Western North Pacific Based on Long-Term MPLNET Data

East Asia is the most complex region in the world for aerosol studies, as it encounters a lot of varieties of aerosols, and aerosol classification can be a challenge in this region. In the present study, we focused on the relationship between aerosol types and aerosol optical properties. We analyzed the long-term (2005–2012) data of vertical profiles of aerosol extinction coefficients, lidar ratio (Sp), and other aerosol optical properties obtained from a NASA Micro-Pulse Lidar Network and Aerosol Robotic Network site in northern Taiwan, which frequently receives Asian continental outflows. Based on aerosol extinction vertical profiles, the profiles were classified into two types: type 1 (single-layer structure) and type 2 (two-layer structure). Fall season (October–November) was the prevailing season for the Type 1, whereas type 2 mainly happened in spring (March–April). In type 1, air masses normally originated from three regional sectors, i.e., Asia continental (AC), Pacific Ocean (PO), and Southeast Asia (SA). The mean Sp values were 39 ± 17 sr, 30 ± 12 sr, and 38 ± 18 sr for the AC, PO, and SA sectors, respectively. The Sp results suggested that aerosols from the AC sector contained dust and anthropogenic particles, and aerosols from the PO sector were most likely sea salts. We further combined the EPA dust event database and backward trajectory analysis for type 2. Results showed that Sp was 41 ± 14 sr and 53 ± 21 sr for dust storm and biomass-burning events, respectively. The Sp for biomass-burning events in type 2 showed two peaks patterns. The first peak occurred within range of 30–50 sr corresponding to urban pollutant, and the second peak occurred within range of 60–80 sr in relation to biomass burning. Finally, our study summarized the Sp values for four major aerosol types over northern Taiwan, viz., urban (42 ± 18 sr), dust (34 ± 6 sr), biomass-burning (69 ± 12 sr), and oceanic (30 ± 12 sr). Our findings provide useful references for aerosol classification and air pollution identification over the western North Pacific.

MAX-DOAS measurements of tropospheric NO<sub>2</sub> and HCHO in Munich and the comparison to OMI and TROPOMI satellite observations

Abstract. We present two-dimensional scanning Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations of nitrogen dioxide (NO2) and formaldehyde (HCHO) in Munich. Vertical columns and vertical distribution profiles of aerosol extinction coefficient, NO2 and HCHO are retrieved from the 2D MAX-DOAS observations. The measured surface aerosol extinction coefficients and NO2 mixing ratios derived from the retrieved profiles are compared to in situ monitoring data, and the surface NO2 mixing ratios show a good agreement with in situ monitoring data with a Pearson correlation coefficient (R) of 0.91. The aerosol optical depths (AODs) show good agreement as well (R = 0.80) when compared to sun photometer measurements. Tropospheric vertical column densities (VCDs) of NO2 and HCHO derived from the MAX-DOAS measurements are also used to validate Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) satellite observations. Monthly averaged data show a good correlation; however, satellite observations are on average 30 % lower than the MAX-DOAS measurements. Furthermore, the MAX-DOAS observations are used to investigate the spatiotemporal characteristic of NO2 and HCHO in Munich. Analysis of the relations between aerosol, NO2 and HCHO shows higher aerosol-to-HCHO ratios in winter, which reflects a longer atmospheric lifetime of secondary aerosol and HCHO during winter. The analysis also suggests that secondary aerosol formation is the major source of these aerosols in Munich.

A Method for Retrieving Stratospheric Aerosol Extinction and Particle Size from Ground-Based Rayleigh-Mie-Raman Lidar Observations

We report on the retrieval of stratospheric aerosol particle size and extinction coefficient profiles from multi-color backscatter measurements with the Rayleigh–Mie–Raman lidar operated at the Arctic Lidar Observatory for Middle Atmosphere Research (ALOMAR) in northern Norway. The retrievals are based on a two-step approach. In a first step, the median radius of an assumed monomodal log-normal particle size distribution with fixed width is retrieved based on a color index formed from the measured backscatter ratios at the wavelengths of 1064 nm and 532 nm. An intrinsic ambiguity of the retrieved aerosol size information is discussed. In a second step, this particle size information is used to convert the measured lidar backscatter ratio to aerosol extinction coefficients. The retrieval is currently based on monthly-averaged lidar measurements and the results for March 2013 are discussed. A sensitivity study is presented that allows for establishing an error budget for the aerosol retrievals. Assuming a monomodal log-normal aerosol particle size distribution with a geometric width of S = 1.5, median radii on the order of below 100 nm are retrieved. The median radii are found to generally decrease with increasing altitude. The retrieved aerosol extinction profiles are compared to observations with the OSIRIS (Optical Spectrograph and InfraRed Imager System) and the OMPS-LP (Ozone Mapping Profiling Suite Limb Profiler) satellite instruments in the 60∘ N to 80∘ N latitude band. The extinction profiles that were retrieved from the lidar measurements show good agreement with the observations of the two satellite instruments when taking the different wavelengths of the instruments into account.

Method to retrieve aerosol extinction profiles and aerosol scattering phase functions with a modified CCD laser atmospheric detection system.

Vertical distributions of ambient aerosols and their corresponding optical properties are crucial to the assessment of aerosol radiative effects. Traditionally, ambient aerosol phase function is assumed as a constant of input parameter in the retrieval of the vertical distribution of aerosol optical characteristics from remote sensing measurements (e.g. lidar or camera-laser based instruments). In this work, sensitivity studies revealed that using constant aerosol phase function assumptions in the algorithm would cause large uncertainties. Therefore, an improved retrieval method was established to simultaneously measure ambient aerosol scattering phase functions and aerosol scattering function profiles with a modified charge-coupled device-laser aerosol detection system (CLADS), which are then combined to yield vertical profiles of aerosol extinction coefficients. This method was applied and evaluated in a comprehensive field campaign in the North China Plain during January 2016. The algorithm showed robust performance and was able to capture temporal variations in ambient aerosol scattering phase functions and aerosol scattering function profiles. Aerosol extinction coefficients derived with simultaneously measured aerosol phase functions agreed well with in-situ measurements, indicating that uncertainties in the retrieval of aerosol extinction vertical profiles have been significantly reduced by using the proposed method with the modified CLADS. The advantage of this modified CLADS is that it can accomplish these aerosol measurements independent of other supplementary instruments. Benefiting from its low cost and high spatial resolution (∼1 m on average) in the boundary layer, this measurement system can play an important role in the research of aerosol vertical distributions and its impacts on environmental and climatic studies.

Importance of Bias Correction in Data Assimilation of Multiple Observations Over Eastern China Using WRF‐Chem/DART

AbstractThree types of observations, aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer, surface particulate matter with diameters less than 2.5 (PM2.5) and 10 μm (PM10), and aerosol extinction coefficient (AEXT) profiles from ground‐based lidars, were separately and simultaneously assimilated using the Weather Research and Forecasting Model with the Chemistry/Data Assimilation Research Testbed (WRF‐Chem/DART). Two cases in June and November 2018 were selected over middle and eastern China. Experiments assimilating single‐type and multiple observations were evaluated by cross validating their analysis and forecast against the three observation types. Compared to the experiment without data assimilation (DA), DA of single‐type observations is always closer to the type of observations assimilated. However, DA of aerosol optical depth or AEXT sometimes significantly degraded the error performance for PM2.5. This problem is caused by the inconsistency of bias tendencies when modeling aerosol optical properties and surface aerosol mass. It is found that WRF‐Chem tends to predict dryer air within the boundary layer over eastern China, which may have played a role in the underestimation of AEXT even when PM2.5 was overestimated. After applying a simple bias correction (BC), the problem was alleviated. DA of multiple observations with BC gives the best overall error performance when validated against all types of observations and even performs better than any DA of single‐type observations experiments in reproducing AEXT profiles. The results illustrate that BC is important in DA of multiple observations and that the simultaneous DA of aerosol observations with different vertical information can work synergistically to improve aerosol forecasts.

Multi-sensor comparison of aerosol optical properties profiles observed during a pollution episode in a mega city of the North China Plain

A fine aerosol particle pollution episode happened in mega city Beijing during February 25th – March 5th. Aerosol extinction coefficient profiles were observed at 7 L idar s ta ti ons. Different aerosol optical property instruments were used to verify Lidar retrievals. Ground visibility observations were used to verify near ground Lidar observations. High correlation coefficient was found (0.8) between visiometer and Lidar observations. Lidar’s Column integrated aerosol extinction coefficient was also verified by Himawari-8 aerosol optical depth (AOD) data. The correlation coefficient between two AOD retrievals was 0.4. And the mean bias was -0.17. CALIPSO aerosol property profiles was also used to verify Lidar retrievals. Lidar retrievals was inconsistent with CALIPSO in the boundary layer. Overall, the Lidar retrievals are consistent with other optical property sensors. And the retrials could be used for observation research and model validation.

Inversion of aerosol extinction coefficient by Raman-Mie scattering lidar

An observation experiment of atmospheric aerosol based on Raman-Mie scattering lidar was introduced. Raman scattering lidar signals were smoothed by generalized regression neural network(GRNN) combined with wavelet denoising. Based on Raman-Mie scattering lidar, several groups of aerosol extinction coefficient profiles were retrieved by GRNN method. And the variation characteristics of aerosol extinction coefficient were analyzed by combining with meteorological data.

CALIPSO level 3 stratospheric aerosol profile product: version 1.00 algorithm description and initial assessment

Abstract. In August 2018, the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) project released a new level 3 stratospheric aerosol profile data product derived from nearly 12 years of measurements acquired by the spaceborne Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). This monthly averaged, gridded level 3 product is based on version 4 of the CALIOP level 1B and level 2 data products, which feature significantly improved calibration that now makes it possible to reliably retrieve profiles of stratospheric aerosol extinction and backscatter coefficients at 532 nm. This paper describes the science algorithm and data handling techniques that were developed to generate the CALIPSO version 1.00 level 3 stratospheric aerosol profile product. Further, we show that the extinction profiles (retrieved using a constant lidar ratio of 50 sr) capture the major stratospheric perturbations in both hemispheres over the last decade resulting from volcanic eruptions, extreme smoke events, and signatures of stratospheric dynamics. Initial assessment of the product by intercomparison with the stratospheric aerosol retrievals from the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) indicates good agreement in the tropical stratospheric aerosol layer (30∘ N–30∘ S), where the average difference between zonal mean extinction profiles is typically less than 25 % between 20 and 30 km (CALIPSO biased high). However, differences can exceed 100 % in the very low aerosol loading regimes found above 25 km at higher latitudes. Similarly, there are large differences (≥100 %) within 2 to 3 km above the tropopause that might be due to cloud contamination issues.

Dual CCD detection method to retrieve aerosol extinction coefficient profile.

The profile of aerosol extinction coefficient can help understand the air pollution transportation and development of the atmospheric boundary layer. The charge-coupled device (CCD)-laser aerosol detection system (CLADS) was widely used to measure the profile of aerosol extinction coefficient, which has excellent resolution near the ground. Traditionally, a constant aerosol scattering phase function and single scattering albedo (SSA) is assumed when retrieving the profile of aerosol extinction coefficient using the measured signals from CLADS. Sensitivity studies in this research show that aerosol scattering phase function leads to an uncertainty up to 462% of the retrieved profile of aerosol extinction coefficient, while SSA leads to an uncertainty up to 25%. A new method is proposed to derive the profile of aerosol extinction coefficient by using two CCD cameras. The aerosol scattering phase function can be determined by minimizing the difference between profiles of aerosol extinction coefficient from the two CCD cameras without any assumption. The profile of aerosol extinction coefficient can be retrieved with high accuracy by using our optimized aerosol scattering phase function. This method is validated by simulation studies where the relative difference between the pre-parameterized aerosol extinction profile and retrieved aerosol extinction profile is below 6%. This dual CCD detection system is employed in a field measurement and proved to be reliable. Our proposed method can obtain more accurate profile of aerosol extinction coefficient for further works about air pollution and atmospheric boundary layer development.

An iterative calibrating method for airborne atmospheric detection lidar based on the klett forward integral equation

The calibration method of airborne atmospheric detection lidar is a subject worthy of study. A forward iterative calibration method for Klett forward integral equation is presented in this paper. The results of the preliminary inversion show that after 2∼3 times forward integration iterations, the airborne lidar data are accurately calibrated and that the accurate aerosol extinction coefficient profile can be obtained. By comparison with other two calibration methods, the Klett iterative calibration method, with smaller errors in calibration, is superior to the slope method and the calibration method by means of aerosol-free layer in stable accuracy.

Impact of planetary boundary layer structure on the formation and evolution of air-pollution episodes in Shenyang, Northeast China

The impact of the planetary boundary layer (PBL) structure on air pollution in Northeast China, where frequently experiences air pollution episodes in autumn and winter, is not well understood due to a lack of observations. In this study, four pollution episodes during autumn and winter of 2016 at Shenyang, a provincial capital city in Northeast China, were examined to investigate the linkage between the PBL structure and air pollution using meteorological sounding data and LiDAR-retrieved profiles of aerosol extinction coefficients. We also conducted a tracer simulation using the Weather Research and Forecasting model with Chemistry (WRF-Chem) to demonstrate the transport and vertical mixing of air pollutants in the PBL. The results indicated that a stable, moist and shallow surface layer (<400 m) formed and remained at night due to strong surface radiative cooling after a steep decline of temperature during the first air-pollution episode (EP1, from 12:00 Local Time (LT) on November 26 to 07:00 LT on November 27). Stable stratification and stagnant winds contributed to the increase of surface pollutant concentrations in EP1. Strong surface potential temperature inversion and enhanced local emissions during evening rush hour resulted in the formation of EP2 (13:00–23:00 LT on December 2). Observations and modelling results revealed that large amount of pollutants were transported by the southerly nocturnal low-level jets from the North China Plain to Shenyang after EP2. These pollutants were trapped in the residue layer at night and then mixed to the surface after sunrise due to convective turbulence, leading to the formation of EP3 (06:00–23:00 LT on December 3). EP4 (03:00–14:00 LT on December 4) occurred in the convergence zone ahead of an approaching trough. Low wind speed (<6 m s−1) and high relative humidity (>80%) in the PBL enhanced the deterioration of air quality near the surface.

An accurate inversion method of aerosol extinction coefficient about ground-based lidar without needing calibration

&lt;sec&gt; How to accurately calibrate the lidar data about haze in the presence of some cloud layers over the haze has always been a subject to be solved for data inversion of Mie scattering lidar. It is difficult for laser to penetrate the haze and clouds simultaneously, so the backscattering signal of lidar cannot be calibrated by using a clear air layer when the haze is under the low clouds. For the portable Mie scattering lidar with a detecting range of less than 6 km, it is also difficult to calibrate the lidar signals by using a clear air layer. An iterative algorithm for aerosol extinction coefficient is proposed based on the characteristics of the Fernald forward integral equation in this paper. By specific settings for the inversion process, the difference between the inversion value and the expected one of aerosol extinction coefficient is reduced after each iteration. After several iterations, the difference between the inversion value and the expected one of aerosol extinction coefficient is small enough to be negligible.&lt;/sec&gt;&lt;sec&gt;The disadvantage of the iterative algorithm for aerosol extinction coefficient is that the inversion results are affected by the overlap factor of lidar. The errors of lidar overlap factor measured experimentally at different times are slightly different. However, the influence about the overlap factor of lidar measured experimentally at different times on the inversion results is slightly different when the iterative algorithm for aerosol extinction coefficient is used to calculate aerosol extinction coefficient.&lt;/sec&gt;&lt;sec&gt;The results of preliminary calculation show that the iterative algorithm of aerosol extinction coefficient can accurately reproduce aerosol extinction coefficient profile without needing calibration of the lidar data. For the haze detection signal that cannot be calibrated by a clear air layer, the vertical distribution of the haze extinction coefficient can be accurately retrieved by the iterative algorithm for aerosol extinction coefficients. The vertical distribution of aerosol extinction coefficients can also be accurately retrieved by using the iterative algorithm of aerosol extinction coefficients for the Mie backscattering lidar data with the measuring height less than 6 km. Through comparative analysis and research, it is found that for the same lidar data, the aerosol extinction coefficient obtained by the iterative algorithm for aerosol extinction coefficient is closer to the actual value than that by the slope method. &lt;/sec&gt;

Accurate inversion of tropospheric aerosol extinction coefficient profile by Mie-Raman lidar

An observation experiment on atmospheric aerosols based on Mie-Raman scattering lidar in northern suburb of Nanjing was introduced. To avoid the saturation effect of strong signals near the ground, the detection height of Raman scattering lidar was set high. For better inversion results, Mie-Raman scattering lidar’s aerosol observational data were processed by wavelet denoising. Appropriate threshold was selected to denoise the experimental observed data. The atmospheric aerosol extinction coefficient profiles in the upper troposphere were retrieved by Raman scattering lidar. The atmospheric aerosol extinction coefficient profiles in the lower troposphere were retrieved by Mie scattering lidar. The retrieved upper tropospheric aerosol extinction coefficient profiles provided one boundary value for Fernald method.