Aerosol Extinction Coefficient Research Articles

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.

A cold front induced co-occurrence of O3 and PM2.5 pollution in a Pearl River Delta city: Temporal variation, vertical structure, and mechanism

In this study, the spatiotemporal variabilities and characteristics of ozone (O3) and fine particulate matter (PM2.5) were reconstructed, and the interaction between meteorological conditions and the co-occurrence of O3 and PM2.5 in Zhuhai, a city in the Pearl River Delta (China), was analysed. The vertical distributions of lower tropospheric O3, aerosol extinction coefficient, and wind velocity were measured using a ground-based LiDAR system. The diurnal variations in air pollutant concentrations and meteorological conditions at ground level were examined from 28 November to December 8, 2020 considering the weather conditions in Zhuhai. Heavy pollution episodes with increased concentrations of O3 and PM2.5 were observed from 6 to 7 December after a period of cold air invasion. The maximum hourly average concentrations of O3 and PM2.5 at the ground level reached up to 190 μg/m3, 98 μg/m3, respectively. The horizontal wind speed rapidly decreased to less than 2 m/s during the heavy pollution episodes driven by O3 and PM2.5, whereas the vertical wind velocity was dominated by the downdraught. When the large-scale synoptic winds were weak, a strengthening sea breeze in the afternoon could promote the landward propagation of warm marine air masses, and a lower surface wind speed was driven by the convergence of cold air from the north and warm air from the south. In turn, this increased the residence time of air pollutants and promoted their conversion to secondary pollutants. Regarding the pollution sources, the results indicated that the Pearl River Estuary represented a ‘pool’ of O3 and PM2.5 pollution. In addition, the contribution of regional pollutant transport could not be ignored when considering the accumulative increase in air pollution. Overall, the relatively weak synoptic winds, low mixing height, and high generation of pollution around Zhuhai collectively resulted in high concentrations of O3 and PM2.5.

Zonal variations in the vertical distribution of atmospheric aerosols over the Indian region and the consequent radiative effects

Abstract. The vertical structure of atmospheric aerosols over the Indian mainland and the surrounding oceans and its spatial distinctiveness and resultant atmospheric heating are characterised using long-term (2007–2020) satellite observations, assimilated aerosol single scattering albedo, and radiative transfer calculations. The results show strong, seasonally varying zonal gradients in the concentration and vertical extent of aerosols over the study region. Compared to the surrounding oceans, where the vertical extent of aerosols is confined within 3 km, the aerosol extinction coefficients extend to considerably higher altitudes over the mainland, reaching as high as 6 km during pre-monsoon and monsoon seasons. Longitudinally, the vertical extent is highest around 75∘ E and decreasing gradually towards either side of the study region, particularly over peninsular India. Particulate depolarisation ratio profiles affirm the ubiquity of dust aerosols in western India from the surface to nearly 6 km. While the presence of low-altitude dust aerosols decreases further east, the high-altitude (above 4 km) dust layers remain aloft throughout the year with seasonal variations in the zonal distribution over north-western India. High-altitude (around 4 km) dust aerosols are observed over southern peninsular India and the surrounding oceans during the monsoon season. Radiative transfer calculations show that these changes in the vertical distribution of aerosols result in enhanced atmospheric heating at the lower altitudes during the pre-monsoon, especially in the 2–3 km altitude range throughout the Indian region. These results have strong implications for aerosol–radiation interactions in regional climate simulations.

An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application

Abstract. Aerosol vertical stratification is important for global climate and planetary boundary layer (PBL) stability, and no single method can obtain spatiotemporally continuous vertical profiles. This paper develops an online data assimilation (DA) framework for the Eulerian atmospheric chemistry-transport model (CTM) Nested Air Quality Prediction Model System (NAQPMS) with the Parallel Data Assimilation Framework (PDAF) as the NAQPMS-PDAF for the first time. Online coupling occurs based on a memory-based way with two-level parallelization, and the arrangement of state vectors during the filter is specifically designed. Scaling tests demonstrate that the NAQPMS-PDAF can make efficient use of parallel computational resources for up to 25 000 processors with a weak scaling efficiency of up to 0.7. The 1-month long aerosol extinction coefficient profiles measured by the ground-based lidar and the concurrent hourly surface PM2.5 are solely and simultaneously assimilated to investigate the performance and application of the DA system. The hourly analysis and subsequent 1 h simulation are validated through lidar and surface PM2.5 measurements assimilated and not assimilated. The results show that lidar DA can significantly improve the underestimation of aerosol loading, especially at a height of approximately 400 m in the free-running (FR) experiment, with the mean bias (BIAS) changing from −0.20 (−0.14) km−1 to −0.02 (−0.01) km−1 and correlation coefficients increasing from 0.33 (0.28) to 0.91 (0.53) averaged over sites with measurements assimilated (not assimilated). Compared with the FR experiment, simultaneously assimilating PM2.5 and lidar can have a more consistent pattern of aerosol vertical profiles with a combination of surface PM2.5 and lidar, independent extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET). Lidar DA has a larger temporal impact than that in PM2.5 DA but has deficiencies in subsequent quantification on the surface PM2.5. The proposed NAQPMS-PDAF has great potential for further research on the impact of aerosol vertical distribution.

Wildfire Smoke Observations in the Western United States from the Airborne Wyoming Cloud Lidar during the BB-FLUX Project. Part I: Data Description and Methodology

Abstract During the summer of 2018, the upward-pointing Wyoming Cloud Lidar (WCL) was deployed on board the University of Wyoming King Air (UWKA) research aircraft for the Biomass Burning Flux Measurements of Trace Gases and Aerosols (BB-FLUX) field campaign. This paper describes the generation of calibrated attenuated backscatter coefficients and aerosol extinction coefficients from the WCL measurements. The retrieved aerosol extinction coefficients at the flight level strongly correlate (correlation coefficient, rr > 0.8) with in situ aerosol concentration and carbon monoxide (CO) concentration, providing a first-order estimate for converting WCL extinction coefficients into vertically resolved CO and aerosol concentration within wildfire smoke plumes. The integrated CO column concentrations from the WCL data in nonextinguished profiles also correlate (rr = 0.7) with column measurements by the University of Colorado Airborne Solar Occultation Flux instrument, indicating the validity of WCL-derived extinction coefficients. During BB-FLUX, the UWKA sampled smoke plumes from more than 20 wildfires during 35 flights over the western United States. Seventy percent of flight time was spent below 3 km above ground level (AGL) altitude, although the UWKA ascended up to 6 km AGL to sample the top of some deep smoke plumes. The upward-pointing WCL observed a nearly equal amount of thin and dense smoke below 2 km and above 5 km due to the flight purpose of targeted fresh fire smoke. Between 2 and 5 km, where most of the wildfire smoke resided, the WCL observed slightly more thin smoke than dense smoke due to smoke spreading. Extinction coefficients in dense smoke were 2–10 times stronger, and dense smoke tended to have larger depolarization ratio, associated with irregular aerosol particles.

Simultaneous Extraction of Planetary Boundary-Layer Height and Aerosol Optical Properties from Coherent Doppler Wind Lidar.

Planetary boundary-layer height is an important physical quantity for weather forecasting models and atmosphere environment assessment. A method of simultaneously extracting the surface-layer height (SLH), mixed-layer height (MLH), and aerosol optical properties, which include aerosol extinction coefficient (AEC) and aerosol optical depth (AOD), based on the signal-to-noise ratio (SNR) of the same coherent Doppler wind lidar (CDWL) is proposed. The method employs wavelet covariance transform to locate the SLH and MLH using the local maximum positions and an automatic algorithm of dilation operation. AEC and AOD are determined by the fitting curve using the SNR equation. Furthermore, the method demonstrates the influential mechanism of optical properties on the SLH and MLH. MLH is linearly correlated with AEC and AOD because of solar heating increasing. The results were verified by the data of an ocean island site in China.

Vertical aerosol data assimilation technology and application based on satellite and ground lidar: A review and outlook

Observations and numerical models are mainly used to investigate the spatiotemporal distribution and vertical structure characteristics of aerosols to understand aerosol pollution and its effects. However, the limitations of observations and the uncertainties of numerical models bias aerosol calculations and predictions. Data assimilation combines observations and numerical models to improve the accuracy of the initial, analytical fields of models and promote the development of atmospheric aerosol pollution research. Numerous studies have been conducted to integrate multi-source data, such as aerosol optical depth and aerosol extinction coefficient profile, into various chemical transport models using various data assimilation algorithms and have achieved good assimilation results. The definition of data assimilation and the main algorithms will be briefly presented, and the progress of aerosol assimilation according to two types of aerosol data, namely, aerosol optical depth and extinction coefficient, will be presented. The application of vertical aerosol data assimilation, as well as the future trends and challenges of aerosol data assimilation, will be further analysed.

Prediction of Aerosol Extinction Coefficient in Coastal Areas of South China Based on Attention-BiLSTM

The aerosol extinction coefficient (AEC) characterises the attenuation of the light propagating in a turbid medium with suspended particles. Therefore, it is of great significance to carry out AEC prediction research using state-of-art neural network (NN) methods. The attention mechanism (AM) has become an indispensable part of NNs that focuses on input weight assignment. Traditional AM is used in time steps to help generate the outputs. To select important features of meteorological parameters (MP) that are helpful for forecasting, in this study, we apply AM to features instead of time steps. Then we propose a bidirectional long short-term memory (BiLSTM) NN based on AM to predict the AEC. The proposed method can remember information twice (i.e., forward and backward), which can provide more context for AEC forecasting. Finally, an in situ measured MP dataset is applied in the proposed model, which presents Maoming coastal area’s atmospheric conditions in November 2020. The experimental results show that the model proposed in this paper has higher accuracy compared with traditional NN, providing a novel solution to the AEC prediction problem for the current studies of marine aerosol.

Dust Aerosol Vertical Profiles in the Hinterland of Taklimakan Desert During Summer 2019

Dust aerosol vertical profiles are very essential to accurately evaluate their climate forcing and trans-subcontinental transportation to downstream areas. We initiated a joint comprehensive field experiment to investigate the vertical profiles and optical characteristics of dust aerosol in the hinterland of Taklimakan Desert (TD) during summer 2019. After smoothing the raw signals, the CHM15k ceilometer could distinctly detect a moderate intensity of dust layer, cloud layer, and subsequent rainfall process. The results showed that dust events frequently occurred in TD during the entire period; the overall mean PM2.5, PM10, PM2.5/PM10, and Ångström exponent are 110.4 ± 121.0 μg/m3, 317.2 ± 340.0 μg/m3, 0.35 ± 0.07, and 0.28 ± 0.12, respectively, suggesting that dust particles are predominant aerosol types in TD. There was an obvious summertime dust stagnation layer persistently hanged over the desert at 1.5–3.0-km height. A deep and intense daytime convective structure was also detected by the ceilometer, with maximum aerosol mixing layer height of ∼3 km that appeared at 12:00 UTC, which was in favor of lifting the ground-generated dust particles into the upper atmosphere. The normalized range-corrected signal log10(RCS), aerosol extinction σ(z), and backscattering coefficient β(z) of the ceilometer were higher than 6.2, 0.5 km−1, and 0.01 km−1 sr−1 for heavy dust storms, respectively, and the corresponding vertical optical range was smaller than 1.0 km. The aerosol lidar ratio was equal to 50 sr, which was greatly different from those of clear-sky cases. The retrieved σ(z) values were about 2.0, 0.5, 0.14, and 0.10 km−1 at 200-m height, respectively, under strong dust storm, blowing dust, floating dust, and clear-sky conditions. This indicated that the aerosol extinction coefficients under dust events were about 3–10 times greater than those of clear-sky cases. The statistics of the aerosol optical parameters under different dust intensities in TD were very helpful to explore and validate dust aerosols in the application of climate models or satellite remote sensing.

Assessment of PM2.5 using satellite lidar observations: Effect of bio-mass burning emissions over India

The present study estimates the particulate matter with aerodynamic diameters less than 2.5 μm (PM2.5) over the Indian sub-continent using near-surface retrieval of aerosol extinction coefficient (2007–2021) of Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. Climatology of wintertime PM2.5 during the last 15 years shows the highest concentration over the middle Indo-Gangetic Plain (IGP) and northwest India with a 3 to 4 fold increase in magnitude compared to the peninsular India. Surface-level PM2.5 mass concentration during winter (December to February) shows statistically significant positive trends over the Indian subcontinent. It increases at a rate of ~3% over the IGP and arid regions of northwest India, and ~4% over peninsular India during the last fifteen years (2006–2020). Interannual variability of average near-surface PM2.5 concentration over the Indian sub-continent during the fog occurring season (December to February) shows a statistically significant correlation with the post-harvest agro-residue burning over the western IGP (Punjab and Haryana) during November. The wintertime near-surface PM2.5 concentration shows a higher correlation with anthropogenic agro-residue burning activity compared to meteorological parameters. The influence of agro-residue burning during November over northern India extends up to peninsular India and might contribute to continental pollution outflow and associated aerosol plumes persisting over the Northern Indian Ocean during the winter season. Sustainable energy recovery solutions to the agro-residue burning need to be implemented to effectively reduce the far-reaching implications of the post-monsoon burning activity over the western IGP.

Analysis of Wildfires in the Mid and High Latitudes Using a Multi-Dataset Approach: A Case Study in California and Krasnoyarsk Krai

In this study, we investigate the emissions from wildfires in the mid latitude (California) and high latitude (Krasnoyarsk Krai) during the periods of 16–17 August 2020 and 28 July 2019, respectively. Wildfires are unique in themselves as they are driven by various factors such as fuel type, topology, and meteorology. In this study, we analyze whether there are any major variations in the emissions and transport of pollutants between two large wildfire cases in the mid latitude of California and high latitude of Krasnoyarsk Krai. The study is important to understand and characterize the emission regime from biomass burning of different land covers using a mutli-dataset approach. We analyze whether there are any major variations in the emissions and transport of pollutants from these wildfires. For example, the aerosol extinction coefficient profile showed smoke detected at the highest altitude of 9 km in Krasnoyarsk Krai, whereas in California the highest altitude was observed at approximately 6 km. Moreover, large values of black carbon (BC) concentration were observed in Krasnoyarsk Krai approximately 7 µg/m3 compared to the 0.44 µg/m3 observed in California. Areas with an immense dense vegetation are prone to large emissions. The results from this case study suggest that high latitude wildfires emit more pollutants than mid latitude wildfires. However, more studies in the future will be conducted to conclude this observation and finding with certainty.

Variations and drivers of aerosol vertical characterization after clean air policy in China based on 7-years consecutive observations

Understanding the aerosol vertical characterization is of great importance to both climate and atmospheric environment. This study investigated the variations of aerosol profiles over eight regions of interest in China after clean air policy (2013-2019) and discussed the drivers of the vertical aerosol structure, using observations from active satellite measurements (CALIPSO). From the annual variation, the amplitude of extinction coefficient profiles showed a decreasing trend with fluctuations, and the maximum was 0.21 km−1 in Beijing-Tianjin-Hebei (JJJ). For regions suffered from air pollution, the variation was greatest below 0.45 km, while it was between 1-1.5 km for Sichuan Basin. The correlation coefficient between the relative humidity (RH) and the extinction coefficient indicated that the increase of RH inhibited the decrease of the extinction coefficient in the Yangtze River Delta. In most regions, the main aerosol subtypes were polluted dust and polluted continental, but they were coarser in JJJ and North West. The frequency of concurrency of dust and polluted dust aerosols decreased in JJJ, but polluted continental aerosols occurred more frequently. Further, the aerosol extinction coefficient profiles under different pollution conditions showed that it changed most during heavy pollution periods in JJJ, especially in 2017, with a significant aerosol loading between ∼700 and 1200 m. The atmospheric reanalysis data revealed that the weak convergence at low level and the divergence at high level supported the upward transport of aerosols in 2017. Overall, the differences in divergence allocation, RH, and wind filed were the main meteorological drivers.

Effect of Aerosol Vertical Distribution on the Modeling of Solar Radiation

Default aerosol extinction coefficient profiles are commonly used instead of measured profiles in radiative transfer modeling, increasing the uncertainties in the simulations. The present study aimed to determine the magnitude of these uncertainties and contribute towards the understanding of the complex interactions between aerosols and solar radiation. Default, artificial and measured profiles of the aerosol extinction coefficient were used to simulate the profiles of different radiometric quantities in the atmosphere for different surface, atmospheric, and aerosol properties and for four spectral bands: ultraviolet-B, ultraviolet-A, visible, and near-infrared. Case studies were performed over different areas in Europe and North Africa. Analysis of the results showed that under cloudless skies, changing the altitude of an artificial aerosol layer has minor impact on the levels of shortwave radiation at the top and bottom of the atmosphere, even for high aerosol loads. Differences of up to 30% were, however, detected for individual spectral bands. Using measured instead of default profiles for the simulations led to more significant differences in the atmosphere, which became very large during dust episodes (10–60% for actinic flux at altitudes between 1 and 2 km, and up to 15 K/day for heating rates depending on the site and solar elevation).

Observing system simulation experiment (OSSE)-quantitative evaluation of lidar observation networks to improve 3D aerosol forecasting in China

In order to evaluate the potential impact of an aerosol lidar monitoring network in China on improving air quality prediction, an observing system simulation experiment (OSSE) was conducted using the Weather Research and Forecasting Chemistry (WRF-Chem) model and three-dimensional variational (3DVAR) data assimilation (DA) system. The impact of lidar numbers and locations on DA performance was evaluated using sensitivity analysis. Three ground-based lidar monitoring networks with 590, 310, and 99 lidars were constructed based on the distribution of existing surface air pollution monitoring stations in China to study the added value of lidar measurements. Four assimilation experiments were conducted using varying aerosol extinction coefficient data (AEXT) from three lidar monitoring networks and surface particulate matter (PM) concentration data over a four-week period. The results showed that compared with only assimilating surface PM2.5, the correlation coefficient (CORR) of PM2.5 was significantly higher, the root mean square error (RMSE) and mean fraction error (MFE) were significantly lower, and the mean mass concentration was closer to the simulated observation after adding lidar profile measurements to the 3DVAR DA system. Additionally, the evaluation of DA with 590 stations indicated spatial differences in the improvement of the positive effect of assimilation. Overall, the improvement effects of assimilation in Central and East China were better than those in other regions. Finally, it was observed that the positive effect of DA on PM2.5 prediction increased with an increase in the lidar number. However, with an increase in the number of lidars, the improvement of the positive effect of DA decreased gradually. The results indicate that it is necessary to balance the forecasting effect and economic cost while establishing a lidar observation network.

Distinct Diurnal Cycle of Supercooled Water Cloud Fraction Dominated by Dust Extinction Coefficient

AbstractCloud phase (CP) partitioning (e.g., supercooled water cloud fraction, SCF) is often misrepresented in model simulations. One of the remarkable uncertainties involves the poor understanding of SCF diurnal cycle due to lack of observations. By using 33 months of lidar observations from Cloud‐Aerosol Transport System which provides cloud top phase at different local times for a given grid and isotherm, this study first characterizes the diurnal cycle of SCF between 51°S and 51°N. Distinct SCF diurnal variations have been found near‐global at different isotherms, especially for −22.5°C, where SCF diurnal ranges even reach 60%. Correlation analysis shows a robust dependence of SCF diurnal cycle on the variation of dust aerosol extinction coefficient instead of other dust load indicators and meteorological parameters. Given the important roles of SCF on the radiation budget, this study will help us better constrain aerosol‐cloud interaction and simulations of CP partitioning in models.

Dark/bright band of a melting layer detected by coherent Doppler lidar and micro rain radar

Observation of a melting layer using a 1.55 µm coherent Doppler lidar (CDL) is first presented during a stratiform precipitation event. Simultaneous radar measurements are also performed by co-located 1.24 cm micro rain radar (MRR) and 10.6 cm Doppler weather radar (DWR). As a well-known bright band in radar reflectivity appears during precipitation, an interesting dark band about 160 m below that in lidar backscattering is observed. Due to the absorption effect, the backscattering from raindrops at 1.55 µm is found much weaker than that at short wavelengths usually used in direct detection lidars. However, the CDL provides additional Doppler information which is helpful for melting layer identification. For example, a spectrum bright band with broadened width and sign conversion of skewness is detected in this case. After a deep analysis of the power spectra, the aerosol and precipitation components are separated. The fall speed of hydrometeors given by CDL is found smaller than that of MRR, with the differences of approximately 0.5 m/s and 1.5 m/s for the snow and rainfall, respectively. To illustrate the influence of absorption effect, simulations of the backscatter coefficient and extinction coefficient of aerosol and rainfall are also performed at the wavelength range of 0.3 ∼ 2.2 µm using the Mie theory.

Aerosols consistently suppress the convective boundary layer development

Aerosols with different vertical distribution and various optical properties induce diverse heating rates and thereby affecting convective boundary layer (CBL) development. Our results showed consistent CBL-suppression of aerosols during daytime with numerical experiments, in which aerosols were specified at different heights with synthesized single scattering albedo from 64 studies and asymmetry factor from 20 studies globally. Absorbing aerosols concentrated below but close to the CBL top had the strongest suppression effect on CBL development relative to that concentrated near surface or above CBL. Aerosol cooling effect by attenuating incident solar radiation and surface heat flux exceeded its warming effect by reheating the atmosphere layer with absorbed shortwave radiation, and eventually declined net heating rate, which inhibited CBL development, lowered mixed-layer potential temperature and stabilized atmospheric stratification. Stove effect of absorbing aerosols (CBL enhancement) under a zero background aerosol extinction coefficient is negligible for dominant dome effect (CBL suppression) which consistently suppresses CBL development regardless of aerosol vertical height and background aerosol extinction coefficient. Our study also highlighted the importance of specifying background aerosol extinction coefficient in numerical experiments for accurate assessment of aerosol radiative forcing and CBL-aerosol interactions.

Biomass burning aerosol heating rates from the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) 2016 and 2017 experiments

Abstract. Aerosol heating due to shortwave absorption has implications for local atmospheric stability and regional dynamics. The derivation of heating rate profiles from space-based observations is challenging because it requires the vertical profile of relevant properties such as the aerosol extinction coefficient and single-scattering albedo (SSA). In the southeastern Atlantic, this challenge is amplified by the presence of stratocumulus clouds below the biomass burning plume advected from Africa, since the cloud properties affect the magnitude of the aerosol heating aloft, which may in turn lead to changes in the cloud properties and life cycle. The combination of spaceborne lidar data with passive imagers shows promise for future derivations of heating rate profiles and curtains, but new algorithms require careful testing with data from aircraft experiments where measurements of radiation, aerosol, and cloud parameters are better colocated and readily available. In this study, we derive heating rate profiles and vertical cross sections (curtains) from aircraft measurements during the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) project in the southeastern Atlantic. Spectrally resolved irradiance measurements and the derived column absorption allow for the separation of total heating rates into aerosol and gas (primarily water vapor) absorption. The nine cases we analyzed capture some of the co-variability of heating rate profiles and their primary drivers, leading to the development of a new concept: the heating rate efficiency (HRE; the heating rate per unit aerosol extinction). HRE, which accounts for the overall aerosol loading as well as vertical distribution of the aerosol layer, varies little with altitude as opposed to the standard heating rate. The large case-to-case variability for ORACLES is significantly reduced after converting from heating rate to HRE, allowing us to quantify its dependence on SSA, cloud albedo, and solar zenith angle.

Measurement report: Vehicle-based multi-lidar observational study of the effect of meteorological elements on the three-dimensional distribution of particles in the western Guangdong–Hong Kong–Macao Greater Bay Area

Abstract. The distribution of meteorological elements has always been an important factor in determining the horizontal and vertical distribution of particles in the atmosphere. To study the effect of meteorological elements on the three-dimensional distribution structure of particles, mobile vehicle lidar and fixed-location observations were collected in the western Guangdong–Hong Kong–Macao Greater Bay Area of China during September and October in 2019 and 2020. Vertical aerosol extinction coefficient, depolarization ratio, and wind and temperature profiles were measured using a micro pulse lidar, a Raman scattering lidar, and a Doppler wind profile lidar installed on a mobile monitoring vehicle. The mechanism of how wind and temperature in the boundary layer affects the horizontal and vertical distribution of particles was analysed. The results show that particles were mostly distributed in downstream areas on days with moderate wind speed in the boundary layer, whereas they were distributed homogeneously on days with weaker wind. There are three typical types of vertical distribution of particles in the western Guangdong–Hong Kong–Macao Greater Bay Area (GBA): surface single layer, elevated single layer, and double layer. Analysis of wind profiles and Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) backward trajectory reveals different sources of particles for the three types. Particles concentrating near the temperature inversion and multiple inversions could cause more than one peak in the extinction coefficient profile. There were two mechanisms affecting the distribution of particulate matter in the upper and lower boundary layers. Based on this observational study, a general model of meteorological elements affecting the vertical distribution of urban particulate matter is proposed.

Predicting Aerosol Extinction Coefficient With LiDAR Data Based on Deep Belief Network

Aerosol extinction coefficient (AEC) is a crucial factor of aerosol optical properties that have an important influence on the atmospheric process. As a combination of laser technology and radar technology, LiDAR is a powerful remote sensing technology for aerosol measurement. However, many uncertainties still exist in LiDAR data retrieval because frequently used retrieval methods need some assumptions or complex numerical operations for retrieving AEC, which may cause large deviation. In this letter, combining the advantages and disadvantages of Mie scattering LiDAR and high spectral resolution LiDAR (HSRL), a new inversion model based on deep belief network (DBN) is proposed to predict AEC, which can effectively avoid the uncertainty caused by many assumptions and improve the detection accuracy of Mie scattering LiDAR. The experiment results indicate that the trained DBN model is robust and satisfactory and provides a competitive solution for predicting AEC of Mie scattering LiDAR. In addition, this letter reveals that deep learning method has huge potential for LiDAR data retrieval.