Columnar Aerosol Optical Property Characterization and Aerosol Typing Based on Ground-Based Observations in a Rural Site in the Central Yangtze River Delta Region

Accurate and updated aerosol optical properties (AOPs) are of vital importance to climatology and environment-related studies for assessing the radiative impact of natural and anthropogenic aerosols. We comprehensively studied the columnar AOP observations between January 2019 and July 2020 from a ground-based remote sensing instrument located at a rural site operated by Central China Comprehensive Experimental Sites in the center of the Yangtze River Delta (YRD) region. In order to further study the aerosol type, two threshold-based aerosol classification methods were used to investigate the potential categories of aerosol particles under different aerosol loadings. Based on AOP observation and classification results, the potential relationships between the above-mentioned results and meteorological factors (i.e., humidity) and long-range transportation processes were analyzed. According to the results, obvious variation in aerosol optical depth (AOD) during the daytime, as well as throughout the year, was revealed. Investigation into AOD, single-scattering albedo (SSA), and absorption aerosol optical depth (AAOD) revealed the dominance of fine-mode aerosols with low absorptivity. According to the results of the two aerosol classification methods, the dominant aerosol types were continental (accounting for 43.9%, method A) and non-absorbing aerosols (62.5%, method B). Longer term columnar AOP observations using remote sensing alongside other techniques in the rural areas in East China are still needed for accurate parameterization in the future.

Pollen observations at four EARLINET stations during the ACTRIS-COVID-19 campaign

Lidar observations were analysed to characterize atmospheric pollen at four EARLINET (European Aerosol Research Lidar Network) stations (Hohenpeißenberg, Germany; Kuopio, Finland, Leipzig, Germany; and Warsaw, Poland) during the ACTRIS-COVID-19 campaign in May 2020. The re-analysis lidar data products, after the centralized and automatic data processing with the Single Calculus Chain (SCC), were used in this study, focusing on particle backscatter coefficients at 355 nm and 532 nm, and particle linear depolarization ratios (PDRs) at 532 nm. A novel method for the characterization of the pure pollen depolarization ratio was presented, based on the non-linear least square regression fitting using lidar-derived backscatter-related Ångström exponents (BAEs) and PDRs. Under the assumption that the BAE between 355 and 532 nm should be zero (± 0.5) for pure pollen, the pollen depolarization ratios were estimated: for Kuopio and Warsaw stations, the pollen depolarization ratios at 532 nm were of 0.24 (0.19–0.28) during the birch dominant pollen periods; whereas for Hohenpeiβenberg and Leipzig stations, the pollen depolarization ratios of 0.21 (0.15–0.27) and 0.20 (0.15–0.25) were observed for periods of mixture of birch and grass pollen. The method was also applied for the aerosol classification, using two case examples from the campaign periods: the different pollen types (or pollen mixtures) were identified at Warsaw station, and dust and pollen were classified at Hohenpeißenberg station.

Satellite-Based Aerosol Classification for Capital Cities in Asia Using a Random Forest Model

Aerosol types in Asian capital cities were classified using a random forest (RF) satellite-based aerosol classification model during 2018–2020 in an investigation of the contributions of aerosol types, with or without Aerosol Robotic Network (AERONET) observations. In this study, we used the recently developed RF aerosol classification model to detect and classify aerosols into four types: pure dust, dust-dominated aerosols, strongly absorbing aerosols, and non-absorbing aerosols. Aerosol optical and microphysical properties for each aerosol type detected by the RF model were found to be reasonably consistent with those for typical aerosol types. In Asian capital cities, pollution-sourced aerosols, especially non-absorbing aerosols, were found to predominate, although Asian cities also tend to be seasonally affected by natural dust aerosols, particularly in East Asia (March–May) and South Asia (March–August). No specific seasonal effects on aerosol type were detected in Southeast Asia, where there was a predominance of non-absorbing aerosols. The aerosol types detected by the RF model were compared with those identified by other aerosol classification models. This study indicates that the satellite-based RF model may be used as an alternative in the absence of AERONET sites or observations.

Impact of smoke and non-smoke aerosols on radiation and low-level clouds over the southeast Atlantic from co-located satellite observations

Data derived from instruments on board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat satellites as well as meteorological parameters from reanalysis are used to explore situations when moist aerosol layers overlie stratocumulus clouds over the southeast Atlantic during the biomass burning season (June to October). To separate and quantify the impacts of aerosol loading, aerosol type, and humidity on the radiative fluxes (including cloud top cooling), the data are split into different levels of aerosol and moisture loadings. The aerosol classification available from the CALIPSO products is used to compare and contrast situations with pristine air, with smoke, and with other (non-smoke) types of aerosols. A substantial number of cases with non-smoke aerosols above clouds are found to occur under similar meteorological conditions to the smoke cases. In contrast, the meteorology is substantially different for the pristine situations, making a direct comparison with the aerosol cases ambiguous. The moisture content is enhanced within the aerosol layers, but the relative humidity does not always increase monotonously with increasing optical depth. Shortwave (SW) heating rates within the moist aerosol plumes increase with increasing aerosol loading and are higher in the smoke cases compared to the non-smoke cases. However, there is no clear correlation between moisture changes and SW absorption. Cloud top cooling rates do not show a clear correlation with moisture within the overlying aerosol layers due to the strong variability of the cooling rates caused by other meteorological factors (most notably cloud top temperature). No clear influence of aerosol type or loading on cloud top cooling rates is detected. Further, there is no correlation between aerosol loading and the thermodynamic structure of the atmosphere nor the cloud top height.

Improving Spatial Coverage of Satellite Aerosol Classification Using a Random Forest Model

The spatial coverage of satellite aerosol classification was improved using a random forest (RF) model trained with observational data including target (aerosol type) and input (satellite measurement) variables. The AErosol RObotic NETwork (AERONET) aerosol-type dataset was used for the target variables. Satellite input variables with many missing data or low mean-decrease accuracy were excluded from the final input variable set, and good performance in aerosol-type classification was achieved. The performance of the RF-based model was evaluated on the basis of the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical SSA wavelength dependence for individual aerosol types was consistent with that obtained for aerosol types by the RF-based model. The spatial coverage of the RF-based model was also compared with that of previously developed models in a global-scale case study. The study demonstrates that the RF-based model allows satellite aerosol classification with improved spatial coverage, with a performance similar to that of previously developed models.

Improved Algorithms for Remote Sensing-Based Aerosol Retrieval during Extreme Biomass Burning Events

This study proposed an aerosol characterization process using satellites for severe biomass burning events. In general, these severely hazy cases are labeled as “undecided” or “hazy.” Because atmospheric aerosols are significantly affected by factors such as air quality, global climate change, local environmental risk, and human and biological health, efficient and accurate algorithms for aerosol retrieval are required for global satellite data processing. Our previous classification of aerosol types was based primarily on near-ultraviolet (UV) data, which facilitated subsequent aerosol retrieval. In this study, algorithms for aerosol classification were expanded to events with serious biomass burning aerosols (SBBAs). Once a biomass burning event is identified, the appropriate radiation simulation method can be applied to characterize the SBBAs. The second-generation global imager (SGLI) on board the Japanese mission JAXA/Global Change Observation Mission-Climate contains 19 channels, including red (674 nm) and near-infrared (869 nm) polarization channels with a high resolution of 1 km. Using the large-scale wildfires in Kalimantan, Indonesia in 2019 as an example, the complementarity between the polarization information and the nonpolarized radiance measurements from the SGLI was demonstrated to be effective in radiation simulations for biomass burning aerosol retrieval. The retrieved results were verified using NASA/AERONET ground-based measurements, and then compared against JAXA/SGLI/L2-version-1 products, and JMA/Himawari-8/AHI observations.

Global Aerosol Classification Based on Aerosol Robotic Network (AERONET) and Satellite Observation

The particle linear depolarization ratio (PLDR) and single scatter albedo (SSA) in 1020 nm from the Aerosol Robotic Network (AERONET) level 2.0 dataset was utilized among 52 stations to identify dust and dust dominated aerosols (DD), pollution dominated mixture (PDM), strongly absorbing aerosols (SA) and weakly absorbing aerosols (WA), investigate their spatial and temporal distribution, net radiative forcing and radiative forcing efficiency in global range, and further compare with VIIRS Deep Blue Production. The conclusion about net radiative forcing suggests that the high values of radiative forcing from dust and dust dominated aerosols, pollution dominated mixture both mainly come from western Africa. Strongly absorbing aerosols in South Africa and India contribute greatly to the net radiative forcing and the regions with relative high values of weakly absorbing aerosols are mainly located at East Asia and India. Lastly, the observation of VIIRS Deep Blue satellite monthly averaged products depicts the characteristics about spatial distribution of four kinds of aerosol well, the result from ground-based observation presents great significant to validate the measurements from remote sensing technology.

Aerosol classification using sun photometer and lidar data within a machine learning algorithm-a possible nowcasting application

<p>The Met Office operates a ground based operational network of nine polarisation Raman lidars (aerosol profiling instruments) and sun photometers (column integrated information). An aerosol classification scheme using supervised machine learning has been developed. The concept of Mahalanobis (~normalized) distance to identify the aerosol type  from individual Aerosol Robotic Network (AERONET) measurements including Extinction Angstrom Exponent, Absorption Angstrom Exponent, Single Scattering Albedo and Index of refraction is used for a subset of AERONET stations around the globe of known main aerosol types (training set). The aerosol types  include maritime, urban industrial, biomass burning and dust. We build a predictive model from this training set using K nearest neighbour machine learning algorithms. The relation of particle polarisation ratio and lidar ratio from the Raman lidar is used as a sanity check.  We apply the model to 3- 4 years of AERONET and profiling data across the UK, with instruments evenly distributed across the country, from Camborne in Cornwall to Lerwick in the Shetland Islands. We are showing more detailed data of a dust event in May 2016, dust/biomass burning aerosol mix from October 2017 (hurricane Ophelia) and more recent aerosol transported from the Canadian wild fires in September 2020. AERONET Level 2.0  data is compared to level 1.5 in order to determine the implications for the aerosol classification. Level 1.5 data are cloud-screened, but not quality assured and may not have the final calibration applied. Level 2.0  data have pre- and post-field calibration applied, are cloud-screened, and quality-assured data. As level 2.0 data is usually only available after 1-2 years (after a new calibration has been performed), it is important to understand the  usefulness of more readily available level 1.5 (cloud screened) data.</p><p>The aim is to build a real time aerosol classification application that can be used in Nowcasting.</p>

Aeolus aerosol and cloud product

<p>The European Satellite has the first space-borne high-spectral resolution UV lidar onboard called ALADIN. Two detection channels, a broadband (Rayleigh channel) and a narrowband (Mie channel), are implemented. Carefully calibrated, this combination offers the possibility to derive independent estimates of the backscatter and extinction coefficients of clouds andaerosols, leading to a direct estimation of the lidar ratio, useful for aerosol classification.</p><p>The presentation will show how the official processor of the mission works for the retrieval of optical properties of cloud and aerosol particles, with a focus on the currently available products (called L2A). The potential of the L2A processor will be illustrated by results obtained on data acquired since Aeolus launch and by comparisons to ground based lidars in the frame of Cal/Val activities.</p><p>The L2A product will become publicly available during Spring 2021. Thus, this is also an opportunity to introduce a few practical aspects about its usage.</p>