Aerosol Optical Thickness Research Articles

Modeling the Effects of Vegetation and Snow on Dust Storm Over the Gobi Desert

AbstractThe Gobi Desert is a prominent dust source in Asia, where the dust storm is severe and features great interannual and seasonal variability. Previous studies have found land surface variation plausibly plays an important role in the occurrence and intensity of dust storms. However, the quantitative estimation and numerical description in current models are still limited. Here, a comprehensive study utilizing multiple observations and modeling methods to assess the influence of vegetation and snow on dust was conducted. We found that Gobi deserts exhibit substantial monthly and interannual variability in dust storms, which shows a close connection with vegetation and snow. To quantitatively understand the impact of vegetation and snow cover on dust emissions and also to better characterize such effects in numerical models, we introduced a high‐resolution dynamic dust source function that incorporates the effects of vegetation and snow on erodibility. The new parameterization noticeably improved dust‐related simulations, including aerosol optical thickness and PM10 concentrations, and provided insights into the distinct effects of vegetation and snow on dust emissions. This study sheds light on the effects of vegetation and snow on dust storms over the Gobi Desert, highlighting the importance of dynamic representation of time‐varying surface properties in dust simulation.

Global evaluation of sentinel 2 level 2A Sen2Cor aerosol optical thickness retrievals

ABSTRACT The present study aims to conduct the first known comprehensive global evaluation of Aerosol Optical Thickness (AOT) retrievals derived from the Sentinel 2 Sen2Cor algorithm between 2018 and 2022, using data from over 400 AERONET stations. The results indicate that Sentinel 2 tends to underestimate AOT, especially at higher aerosol loadings. Although there appears to be a good overall correlation (r = 0.57) with low RMSE (0.16) and relative mean bias (RMB = -2.46%) between AERONET and Sentinel 2 AOT datasets, regional analysis reveals significant variation in performance across regions, with areas like Europe and Americas exhibiting stronger correlations and lower RMSE than others like Indian subcontinent and Southern Africa. Temporal analysis also suggests an improvement in Sentinel 2 performance, particularly in capturing extreme AOT values in 2022. While elevation does not appear to have any noticeable impact on AOT estimation, slight variations could be observed over certain land cover types like sparse vegetation and permanent water bodies. Though improvements are certainly required over certain geographical regions like Indian subcontinent, Sentinel 2 AOT can, nevertheless, be reliably used for aerosol studies in urban areas or at a finer spatial resolution, especially in Europe, Mainland U.S.A. and East Asia.

Effect of coal type on aerosol optical properties at Ulaanbaatar city

In this study, seasonal characteristics of aerosol optical properties at Ulaanbaatar station of the international network of SKYNET are investigated using ground-based skyradiometer from the years extending from 2017 to 2023. The aerosol optical thickness (AOT) at 500nm wavelength and Angstrom exponent (AE) were determined in winter to evaluate the decrease in air pollution in Ulaanbaatar since implementing the government's decision to use coal briquettes for household heating. During the study period, the monthly mean AOT at 500nm varied throughout the year, with the maximum value of 0.178±0.004 obtained in winter due to households burning large amounts of biomass, and the highest AOT (0.183) was obtained in February. The mean AOT was 0.21 in the winter of 2017-2019, which decreased by 15 per cent to 0.18 in the winter of 2019-2023.

The Single-Scattering Albedo of Black Carbon Aerosols in China

Black carbon (BC) aerosols have attracted wide attention over the world due to their significant climate effects on local and global scales. BC extinction aerosol optical thickness (AOT), scattering AOT, and single scattering albedo (SSA) over China are systematically studied based on the MERRA-2 satellite reanalysis data from 1983 to 2022 in terms of the spatial, yearly, seasonal, and monthly variations. The extinction and scattering AOTs of BC show similar spatial distribution, with high values in eastern and southern China, generally as opposed to BC SSA. A decrease in BC extinction and scattering AOTs has been documented over the last decade. The mean BC extinction AOT, scattering AOT, and SSA over China are 0.0054, 0.0014, and 0.26, respectively. The BC SSA showed small variations during 1983–2022, although a high BC extinction AOT and scattering AOT have been seen in the last two decades. During different decades, the seasonal patterns of BC extinction and scattering AOTs may differ, whereas the BC SSA shows seasonal consistency. Significant monthly variations in the BC SSA are seen over four decades, which are in agreement with their seasonal patterns. The mean BC extinction AOTs are 0.037, 0.033, 0.023, and 0.0054, whereas the average BC scattering AOTs are 0.0088, 0.0082, 0.0060, and 0.0014 in the Pearl River Delta (PRD), Yangtze River Delta (YRD), Beijing–Tianjin–Hebei (BTH) region, and Tarim Basin (TB), respectively. It is interesting to see that BC SSA values in the TB region are generally higher than those over the PRD, YRD and BTH areas, whereas the reverse is true for BC extinction and scattering AOTs. This study provides references for further research on black carbon aerosols and air pollution in China.

Interdecadal Springtime Aerosol Increase in the North Indian Ocean Observed From the Satellite AVHRR Instrument

AbstractA 38‐year aerosol optical thickness (AOT) satellite product from the Advanced Very High‐Resolution Radiometer (AVHRR) is used to investigate the aerosol variabilities in the North Indian Ocean (NIO). A dipolar mode with a notable meridional contrast between the equatorial and northern NIO is revealed by the second mode of Empirical Orthogonal Function (EOF) analysis with a sharp rise over the Arabian Sea (AS) and Bay of Bengal (BoB) since 2002. Our results show that the aerosol dipolar variation is primarily modulated by an El Niño–Southern Oscillation (ENSO) during 1983–2001 (ID1) and by a warm phase of Atlantic Multi‐decadal Oscillation (AMO) during 2002–2020 (ID2), leading to a dry‐and‐warming condition spanning the coasts of East Africa, the Arabian Peninsula (AP), and South Asia (SA) that favors increasing aerosol emission and a longer life cycle in the upstream regions. A warm phase of the AMO tends to excite an anomalous cyclone in western Siberia and reinforces anticyclonic circulation in the Tibetan Plateau. Correlation analyses between the second EOF mode time series and other parameters in ID1 and ID2 show that an interannual dry‐and‐warming condition over the AP–SA is associated with a noticeable north‐south contrast of convection between the equatorial NIO and AP–SA in ID1. But in ID2, a zonal contrast of anomalous convective activities between the AP–SA and the BoB‐South China Sea is dominant, partially due to the warming AMO, which aids the development of anticyclonic cells over the TP and the strengthened subtropical westerly jet streams.

Characterization of the aerosol contribution to the top-of-atmosphere radiance for satellite ocean color retrievals

A method is proposed for characterization of the aerosol contribution to the top-of-atmosphere (TOA) radiance. The method is based on solving the problem of radiative transfer in the atmosphere-ocean system and expanding the solution in powers of the aerosol optical thickness τ a . We show that the linear term of the expansion is analytically expressed in terms of the bidirectional transmittance/reflectance of the aerosol-free Rayleigh atmosphere. A procedure is also proposed for successively extracting the terms of higher order in τ a from the data of the TOA radiance computation with the DISORT code. As analysis shows, the radiance expansion in τ a is not purely polynomial. Beginning from the quadratic term, the coefficients of the series expansion in powers of τ a become dependent logarithmically on τ a . The approach proposed enables us to reproduce analytically the τ a -dependence of the TOA radiance with controlled accuracy. We determine the expansion coefficients up to the cubic term inclusive and validate our results on the aerosol model embedded in NASA’s SeaDAS algorithm for aerosol loadings, representative for the Barents and Kara seas. In the visible and near-infrared spectral ranges, accounting for the terms up to a quadratic one is found to be sufficient for the atmospheric correction of satellite ocean color data typical for the Arctic region.

Comparative Study on the Vertical Column Concentration Inversion Algorithm of Tropospheric Trace Gas Based on the MAX-DOAS Measurement Spectrum

The tropospheric vertical column concentration (VCDtrop) of NO2, SO2, and HCHO was retrieved, respectively, by employing the geometric method (Geomtry), simplified model method (Model), and look-up table method (Table) with the observation spectra of the multi-axis differential absorption spectroscopy instrument (MAX-DOAS). The correlation and relative differences of the inversion results obtained by these three algorithms, as well as the changes in quantiles, were explored. The comparative analysis reveals that the more concentrated the vertical distribution height of gas components is in the near-surface layer, the better the conformity of the VCDtrop retrieved by different algorithms. However, the increase in relative differences is also related to the diurnal variation of gas components. The influence of aerosols on the inversion of the VCDtrop is greater than the change in the vertical distribution height of the gas component itself. The near-surface concentration and distribution height of gas components are the internal factors that give rise to relative differences in the inversion of the VCDtrop by different algorithms, while aerosols are one of the extremely important external reasons. The VCDtrop inverted by Geomtry without considering the influence of aerosols is generally larger except for NO2. Model sets up aerosols in accordance with the height and meteorological conditions of the atmospheric environment. Table can invert the aerosol profile in real time. Compared with Model, it shows a significant improvement in the refined setting of aerosols. Moreover, while obtaining the vertical distribution of aerosols, it can invert the diurnal variation of the VCDtrop. The VCDtrop inverted by Table is the smallest, and the relative difference with Model is on average about 10% smaller. The relative difference of the VCDtrop for the same height (aerosol optical thickness) quantile is 7–15% (about 25% lower on average). When comparing the inversion results of Table with the Ozone Monitoring Instrument (OMI) satellite product, the MAX-DOAS inversion results of NO2, SO2, and HCHO are all larger than the OMI product. This is related to the different observation methods of the MAX-DOAS and OMI and the configuration between the aerosol layer and the distribution height of gas components.

MAP-IO: an atmospheric and marine observatory program on board Marion Dufresne over the Southern Ocean

Abstract. This article is devoted to the presentation of the MAP-IO observation program. This program, launched in early 2021, has enabled the observation of nearly 700 d of measurements over the Indian and Southern Ocean with the equipment of 17 meteorological and oceanographic scientific instruments on board the ship Marion Dufresne. Several observational techniques have been developed to respond to the difficulties of observations on board the ship, in particular for passive remote sensing data, as well as for quasi-autonomous data acquisition and transfer. The first measurements made it possible to draw up unprecedented climatological data of the Southern Ocean regarding the size distribution and optical thickness of aerosols, the concentration of trace gases and greenhouse gases, UV, and integrated water vapor. High-resolution observations of phytoplankton in surface waters have also shown a great variability in latitude in terms of abundance and community structure (diversity). The operational success of this program and these unique scientific results together establish a proof of concept and underline the need to transform this program into a permanent observatory. The multi-year rotations over the Indian Ocean will enable us to assess the trends and seasonal variability of phytoplankton, greenhouse gases, ozone, and marine aerosols in a sensitive and poorly documented climatic region. Without being exhaustive, MAP-IO should make it possible to better understand and assess the biological carbon pump, to study the variability of gases and aerosols in a region that is remote in relation to the main anthropogenic sources, and to monitor the transport of stratospheric ozone by the Brewer–Dobson circulation. The meteorological MAP-IO data set is publicly available at https://www.aeris-data.fr/catalogue-map-io/ (last access: 26 August 2024) (atmospheric data) and at https://doi.org/10.17882/89505 (Thyssen et al., 2022a) (phytoplankton data).

A framework for natural resource management with geospatial machine learning: a case study of the 2021 Almora forest fires

BackgroundWildfires have a substantial impact on air quality and ecosystems by releasing greenhouse gases (GHGs), trace gases, and aerosols into the atmosphere. These wildfires produce both light-absorbing and merely scattering aerosols that can act as cloud condensation nuclei, altering cloud reflectivity, cloud lifetime, and precipitation frequency. Uttarakhand province in India experiences frequent wildfires that affect its protected ecosystems. Thus, a natural resource management system is needed in this region to assess the impact of wildfire hazards on land and atmosphere. We conducted an analysis of a severe fire event that occurred between January and April 2021 in the Kumaun region of Uttarakhand, by utilizing open-source geospatial data. Near-real-time satellite observations of pre- and post-fire conditions within the study area were used to detect changes in land and atmosphere. Supervised machine learning algorithm was also implemented to estimate burned above ground biomass (AGB) to monitor biomass stock.ResultsThe study found that 21.75% of the total burned area burned with moderate to high severity, resulting in a decreased Soil Adjusted Vegetation Index value (> 0.3), a reduced Normalized Differential Moisture Index value (> 0.4), and a lowered Normalized Differential Vegetation Index (> 0.5). The AGB estimate demonstrated a significant simple determination (r2 = 0.001702) and probability (P < 2.2 10−16), along with a positive correlation (r ≤ 0.24) with vegetation and soil indices. The algorithm predicted that 17.56 tonnes of biomass per hectare burned in the Kumaun forests. This fire incident resulted in increased emissions of carbon dioxide (CO2; ~ 0.8 10−4 kg carbon h−1), methane (CH4; ~ 200 10−9 mol fraction in dry air), carbon monoxide (CO; 2000 1015 molecules cm−2 total column), and formaldehyde (HCHO; 3500 1013 molecules cm−2 total column), along with increased aerosol optical thickness (varying from 0.2 to 0.5).ConclusionsWe believe that our proposed operational framework for managing natural resources and assessing the impact of natural hazards can be used to efficiently monitor near-real-time forest-fire-caused changes in land and atmosphere. This method makes use of openly accessible geospatial data that can be employed for several objectives, including monitoring carbon stocks, greenhouse gas emissions, criterion air pollution, and radiative forcing of the climate, among many others. Our proposed framework will assist policymakers and the scientific community in mitigating climate change problems and in developing adaptation policies.

Regional validation of the solar irradiance tool SolaRes in clear-sky conditions, with a focus on the aerosol module

Abstract. The Solar Resource estimate (SolaRes) tool based on the Speed-up Monte Carlo Advanced Radiative Transfer code using GPU (SMART-G) has the ambition to fulfil both research and industrial applications by providing accurate, precise, and high-time-resolution simulations of the solar resource. We investigate the capacity of SolaRes to reproduce the radiation field, relying on 2 years of ground-based measurements by pyrheliometers and pyranometers acquired in northern France (Lille and Palaiseau). Our main objective is to provide, as a first step in clear-sky conditions, a thorough regional validation of SolaRes, allowing us to investigate aerosol impacts on solar resource. We perform comparisons between SolaRes-simulated and clear-sky-measured global horizontal irradiance (GHI), direct normal irradiance (DNI), diffuse horizontal irradiance (DifHI), and global and diffuse irradiance on a tilted plane (GTI, DifTI), and we even consider the circumsolar contributions. Using spectral aerosol optical thickness (AOT) data sets as input, which are delivered by the AErosol RObotic NETwork (AERONET) and the Copernicus Atmosphere Monitoring Service (CAMS), we examine the influence of aerosol input data sets in SolaRes on the comparison scores. Two aerosol models are mixed to compute aerosol optical properties. We also perform a sensitivity study on the aerosol parametrisation and investigate the influence of applying more or less strict cloud-screening methods to derive ground-based proof data sets of clear-sky moments. SolaRes is validated with the (relative) root mean square difference (RMSD) in GHI as low as 1 % and a negligible mean bias difference (MBD). The impact of the cloud-screening method in GHI is 0.5 % of RMSD and 0.3 % of MBD. SolaRes also estimates the circumsolar contribution, which improves MBD in DNI and DifHI by 1 % and 4 %, respectively, and RMSD by ∼ 0.5 %. MBD in DNI is around −1 % and RMSD around 2 %, and MBD in DifHI is 2 % and RMSD around 9 %. RMSD and MBD in both DNI and DifHI are larger than in GHI because they are more sensitive to the aerosol and surface properties. DifTI measured on a vertical plane facing south is simulated by SolaRes with an RMSD of 8 %, comparable to that obtained for DifHI. Our results suggest a strong influence of reflection by not only ground surface but also surrounding buildings. The sensitivity studies on the aerosol parameterisation show that the spectral AOT contains enough information for high performance in DNI simulations, with low influence of the choice of the aerosol models on the RMSD. However, choosing a model with smaller aerosol single scattering albedo significantly decreases SolaRes DifHI and GHI. The best combination in Lille and Palaiseau consists of continental clean mixed with desert dust. Also, complementary information on angular scattering and aerosol absorption provided by the AERONET-inverted model further improves simulated clear-sky GHI by reducing RMSD by ∼ 0.5 % and MBD by ∼ 0.8 %. Eventually, the choice of the data source has a significant influence. Indeed, using CAMS AOT instead of AERONET AOT increases the RMSD in GHI by ∼ 1 % and MBD by ∼ 0.4 % and RMSD in DNI by 5 %. The RMSD in GHI remains slightly smaller than state-of-the-art methods.

Aerosols and black carbon variability using OMI and MERRA-2 and their relationship to near-surface air temperature.

An extinction of incoming solar radiation is taking place by absorption and scattering by dust, water droplets, and gaseous molecules. Such phenomena are responsible for altering meteorological variables. In the present study, temporal analysis of the aerosol optical thickness (AOT) and black carbon (BC) surface mass concentration was undertaken using an ozone monitoring instrument (OMI) and modern-era retrospective analysis for research and applications, version 2 (MERRA-2) satellite from the year 2018 to 2022. The study was mainly focused on the western states of India which are Rajasthan, Gujarat, and Maharashtra. The correlation of AOT and BC surface mass concentration with near-surface temperature (2m above ground level) was analyzed. BC and temperature shows strong negative correlation as BC is known for its absorption of radiation. It accumulates in the atmosphere and contributes to atmospheric warming while simultaneously bringing down the near-surface air temperature due to the reduced sunlight reaching the ground. Also, seasonal analysis was conducted for winter, summer, monsoon, and post-monsoon, which shows the higher values of AOT in monsoon; however, seasonal average BC surface mass concentration was found high in winter in each year for all three states. AERONET data from Jaipur, Rajasthan, and Pune, Maharashtra for the year 2021 was used to further evaluate the AOT generated from OMI. The results demonstrated a significant connection, with R2 values of 0.62 and 0.69, respectively. The temperature retrieved from MERRA-2 was also validated with ground truth data of the Continuous Ambient Air Quality Monitoring Station (CAAQMS) at both stations showing high agreement with R2 > 0.70.

A near-global multiyear climate data record of the fine-mode and coarse-mode components of atmospheric pure dust

Abstract. A new four-dimensional, multiyear, and near-global climate data record of the fine-mode (submicrometer in terms of diameter) and coarse-mode (supermicrometer in terms of diameter) components of atmospheric pure dust is presented. The separation of the two modes of dust in detected atmospheric dust layers is based on a combination of (1) the total pure-dust product provided by the well-established European Space Agency (ESA) “LIdar climatology of Vertical Aerosol Structure” (LIVAS) database and (2) the coarse-mode component of pure dust provided by the first step of the two-step POlarization LIdar PHOtometer Networking (POLIPHON) technique, developed in the framework of the European Aerosol Research Lidar Network (EARLINET). Accordingly, the fine-mode component of pure dust is extracted as the residual between the LIVAS total pure dust and the coarse-mode component of pure dust. Intermediate steps involve the implementation of regionally dependent lidar-derived lidar ratio values and AErosol RObotic NETwork (AERONET)-based climatological extinction-to-volume conversion factors, facilitating conversion of dust backscatter into extinction and subsequently extinction into mass concentration. The decoupling scheme is applied to observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) at 532 nm. The final products consist of the fine mode and coarse mode of atmospheric pure dust, quality-assured profiles of backscatter coefficient at 532 nm, extinction coefficient at 532 nm, and mass concentration for each of the two components. The datasets are established primarily with the original L2 horizontal (5 km) and vertical (60 m) resolution of the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) along the CALIPSO orbit path and secondly in averaged profiles of seasonal–temporal resolution, 1° × 1° spatial resolution, and the original vertical resolution of CALIPSO, focusing on the latitudinal band extending between 70° S and 70° N and covering more than 15 years of Earth observations (June 2006–December 2021). The quality of the CALIPSO-based fine-mode and coarse-mode dust products is assessed through the use of AERONET fine-mode and coarse-mode aerosol optical thickness (AOT) interpolated to 532 nm and the AERosol properties – Dust (AER-D) campaign airborne in situ particle size distributions (PSDs) as reference datasets during atmospheric conditions characterized by dust presence. The near-global fine-mode and coarse-mode pure-dust climate data record is considered unique with respect to a wide range of potential applications, including climatological, time series, and trend analysis over extensive geographical domains and temporal periods, validation of atmospheric dust models and reanalysis datasets, assimilation activities, and investigation of the role of airborne dust in radiation and air quality.

Remote sensing of eruptions and transport of Taal volcano in January 2020

The eruption and transport of the Taal volcano (14 N, 121 E) in January 2020 have been investigated using remote sensing measurements of several satellites, including the Moderate Resolution Imaging Spectroradiometer, Ozone Monitoring Instrument (OMI), Ozone Mapping and Profiler Suite, and Himawari-8. The aerosol optical thickness (AOT), angstrom exponent (AE), and column densities of sulfur dioxide (SO2) derived from satellites are analyzed in this study. The ground-based AERONET data of Manila city and OMI SO2 are studied to validate high AOT events. Our findings indicate that ash and gases followed opposite transport paths. This differential transport is consistent with various wind data, HYSPLIT back and forward trajectory calculations, and emission dispersion models. The potential influences of deposition on ocean biology and climate are also discussed.

Comment on “Stratospheric Aerosol Composition Observed by the Atmospheric Chemistry Experiment Following the 2019 Raikoke Eruption” by Boone et al.

AbstractBased on satellite observations in the Arctic stratosphere at latitudes from 61° to 66°N in the second half of 2019, Boone et al. (2022, https://doi.org/10.1029/2022jd036600) provide the impression that the aerosol in the upper troposphere and lower stratosphere (UTLS) over the entire Arctic consisted of sulfate aerosol originating from the Raikoke volcanic eruption in the summer of 2019. Here, we show that this was most probably not the case and the aerosol layering conditions were much more complex. By combining the stratospheric aerosol typing results of Boone et al. (2022, https://doi.org/10.1029/2022jd036600) with lidar observations at 85°–86°N of Ohneiser et al. (2021, https://doi.org/10.5194/acp‐21‐15783‐2021) of a dominating wildfire smoke layer in the UTLS height range, we demonstrate that the Arctic UTLS aerosol most likely consisted of Siberian wildfire smoke in the lower part and sulfate aerosol in the upper part of the aerosol layer which extended from 7 to 19 km height and was well observable until May 2020. The smoke‐ and sulfate‐related aerosol optical thickness (AOT) fractions were about 0.7–0.8 and 0.2–0.3, respectively, according to our analysis. The sulfate AOT is in good agreement with model‐based predictions of the Raikoke sulfate AOT.

Effects of surface coating on the shortwave and longwave radiative effects of dust aerosol in comparison with external mixing: A theoretical study

Dust particles can be coated with a surface layer of pollutants such as sulfate and nitrate after mixing with local pollution during long-range transport. Previous studies investigated the effects of surface coating on scattering properties and direct radiative effects (DRE) of dust in the solar shortwave (SW) spectral region. In this research, we carried out a theoretical study of the surface coating effects in both solar SW and the terrestrial longwave (LW) and compared the results with external mixing. Three dust coating schemes were developed to study a hypothetical sulfate-dust coating case, in which the thickness of the coating sulfate layer is proportional to the size, surface area, and mass of the dust core, respectively. We found that at the 0.55 µm the aerosol optical thickness (AOD) of the externally mixed dust-sulfate increases more efficiently with the increasing sulfate mass than the coated dust cases, whereas the opposite is true at the 10 µm. This is because at 0.55 µm the difference in the total geometrical cross section is the dominant factor for the AOD difference, while at the 10 µm the dominant factor is the difference in extinction cross section. The differences in dust DRE at the top of atmosphere and surface between the external mixing and coated dust cases can be largely explained by the differences in AOD. Dust absorption in the SW is found to be significantly enhanced by the surface coating of non-absorptive sulfate due to the so-called “lensing effect”. When SW and LW DREs are combined, the volume- and area-proportional coating schemes have a significantly weaker cooling effect than externally mixed dust-sulfate. This research provides the theoretical understanding of how surface coating affects the SW, LW and total dust DREs.

Geostationary aerosol retrievals of extreme biomass burning plumes during the 2019–2020 Australian bushfires

Abstract. Extreme biomass burning (BB) events, such as those seen during the 2019–2020 Australian bushfire season, are becoming more frequent and intense with climate change. Ground-based observations of these events can provide useful information on the macro- and micro-physical properties of the plumes, but these observations are sparse, especially in regions which are at risk of intense bushfire events. Satellite observations of extreme BB events provide a unique perspective, with the newest generation of geostationary imagers, such as the Advanced Himawari Imager (AHI), observing entire continents at moderate spatial and high temporal resolution. However, current passive satellite retrieval methods struggle to capture the high values of aerosol optical thickness (AOT) seen during these BB events. Accurate retrievals are necessary for global and regional studies of shortwave radiation, air quality modelling and numerical weather prediction. To address these issues, the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm has used AHI data to measure extreme BB plumes from the 2019–2020 Australian bushfire season. The sensitivity of the retrieval to the assumed optical properties of BB plumes is explored by comparing retrieved AOT with AErosol RObotic NETwork (AERONET) level-1.5 data over the AERONET site at Tumbarumba, New South Wales, between 1 December 2019 at 00:00 UTC and 3 January 2020 at 00:00 UTC. The study shows that for AOT values &gt; 2, the sensitivity to the assumed optical properties is substantial. The ORAC retrievals and AERONET data are compared against the Japan Aerospace Exploration Agency (JAXA) Aerosol Retrieval Product (ARP), Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue over land, MODIS MAIAC, Sentinel-3 SYN and VIIRS Deep Blue products. The comparison shows the ORAC retrieval significantly improves coverage of optically thick plumes relative to the JAXA ARP, with approximately twice as many pixels retrieved and peak retrieved AOT values 1.4 times higher than the JAXA ARP. The ORAC retrievals have accuracy scores of 0.742–0.744 compared to the values of 0.718–0.833 for the polar-orbiting satellite products, despite successfully retrieving approximately 28 times as many pixels over the study period as the most successful polar-orbiting satellite product. The AHI and MODIS satellite products are compared for three case studies covering a range of BB plumes over Australia. The results show good agreement between all products for plumes with AOT values ≤ 2. For extreme BB plumes, the ORAC retrieval finds values of AOT &gt; 15, significantly higher than those seen in events classified as extreme by previous studies, although with high uncertainty. A combination of hard limits in the retrieval algorithms and misclassification of BB plumes as cloud prevents the JAXA and MODIS products from returning AOT values significantly greater than 5.

Aerosol impacts on the East Asian winter monsoon: Insights from TaiESM1 and CMIP6 simulations

AbstractThe East Asian winter monsoon (EAWM) is an important climate pattern in Southeast Asia. This study delved into the aerosol effects on the EAWM by analysing simulations conducted with the Taiwan Earth System Model version 1 (TaiESM1) model, part of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our assessment, validated against observational and reanalysis data, revealed that TaiESM1 effectively replicates the robust circulation of the EAWM and aerosol concentrations, placing its performance above average level among CMIP6 models. Notably, the simulated global mean aerosol optical thickness exhibited an approximately 20% increase from 1850 to 2014. The increased aerosols corresponded to a weakened Aleutian Low and an intensified Siberian High, leading to a weakened EAWM in the lower atmosphere over extratropical areas. In addition, the cooling across the Northern Hemisphere might affect enhanced northerly winds at approximately 10°N, which are situated in the southern part of the EAWM. An analysis of the near‐surface temperature budget indicated that the aerosol‐induced cooling in East China and India was primarily driven by changes in shortwave and longwave radiation fluxes, albeit slightly offset by sensible heat and latent heat fluxes. Furthermore, our study also reveals that while the mechanisms through which anthropogenic aerosols affect the northern portion of the EAWM vary across most CMIP6 models, their impacts on the tropical region remain consistent.

Particulate matter (pm10) monitoring in the United Arab Emirates using a satellite remote sensing based model

Particulate matter (PM) is one of the major factors causing air pollution, which is considered a concern for human health. Hence, measuring and monitoring the concentrations of these particles is essential. In this study, the main objective is to develop a remote sensing based PM10 monitoring model for the United Arab Emirates (UAE) using Landsat 8 imagery. Landsat 8 images acquired during the four-year period from 2016 to 2022 were obtained and used along with PM10 data collected at 41 ground monitoring stations corresponding to the acquisition of the satellite data (data from 30 stations used for model development 11 stations were used for model testing). The Landsat 8 data was obtained from the United States Geological Survey (USGS) Core Science Systems in the form of Digital Numbers (DNs). The DNs of the four optical bands of Landsat 8 were then converted to top of the atmosphere reflectance (TOA) through radiometric processing, and then used to estimate the Aerosol Optical Thickness. A spectral PM10 model was then developed through regression analysis, correlating AOT to PM10 values obtained at the ground stations. The model provided an R-squared value of 65% and a Root Mean Square Error (RMSE) of 12.55 µg/m3. The results suggest that the developed model is robust in estimating PM10 values and can therefore be used for satellite-based monitoring at any location in the UAE.

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.

A spatiotemporal attention-augmented ConvLSTM model for ocean remote sensing reflectance prediction

Remote sensing reflectance (Rrs) is an essential parameter in ocean color remote sensing and a fundamental input for the estimation of ocean color elements. Predicting Rrs has the potential to enable simultaneous prediction of multiple marine environmental parameters, facilitating multi-perspective analysis of marine environmental changes. This paper proposes a spatiotemporal attention-augmented ConvLSTM-based model for ocean Rrs prediction. The developed model can predict Rrs for up to seven days by simultaneously learning spatiotemporal features from time series Rrs and auxiliary environmental variables. According to the experiments, the proposed model achieves optimal performances on Rrs predictions at 443, 488, and 555 nm, with Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) for the first four prediction days less than 5.6*10-4 sr-1 and 8.6 %, respectively, which are better than the performance of the convolutional neural network (CNN), the LSTM, CNN-LSTM, and the ConvLSTM. The spatial and temporal variations of Rrs are also compared to evaluate the effectiveness of the model, presenting a consistent spatiotemporal pattern between predicted and observed Rrs. We also found that integrating sea surface temperature (SST), photosynthetically available radiation (PAR), and aerosol optical thickness at 869 nm (AOT869) into the model can improve the prediction accuracy in various degrees. This work suggests the proposed deep learning model can predict Rrs for 7 days with a convincing performance, providing critical data and technical support for ocean-related applications, such as algae bloom monitoring.