Fine-mode Aerosol Optical Depth Research Articles

Substantial Underestimation of Fine-Mode Aerosol Loading from Wildfires and Its Radiative Effects in Current Satellite-Based Retrievals over the United States.

Wildfires generate abundant smoke primarily composed of fine-mode aerosols. However, accurately measuring the fine-mode aerosol optical depth (fAOD) is highly uncertain in most existing satellite-based aerosol products. Deep learning offers promise for inferring fAOD, but little has been done using multiangle satellite data. We developed an innovative angle-dependent deep-learning model (ADLM) that accounts for angular diversity in dual-angle observations. The model captures aerosol properties observed from dual angles in the contiguous United States and explores the potential of Greenhouse gases Observing Satellite-2's (GOSAT-2) measurements to retrieve fAOD at a 460 m spatial resolution. The ADLM demonstrates a strong performance through rigorous validation against ground-based data, revealing small biases. By comparison, the official fAOD product from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Multiangle Imaging Spectroradiometer (MISR) during wildfire events is underestimated by more than 40% over western USA. This leads to significant differences in estimates of aerosol radiative forcing (ARF) from wildfires. The ADLM shows more than 20% stronger ARF than the MODIS, VIIRS, and MISR estimates, highlighting a greater impact of wildfire fAOD on Earth's energy balance.

Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval.

Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for climate research. Here, we propose a novel pretrained deep learning framework that can extract information underlying each satellite pixel and use it to create new latent features that can be employed for improving retrieval accuracy in regions without in situ data. With the proposed model, we developed a new global fAOD (at 0.5 μm) data from 2001 to 2020, resulting in a 10% improvement in the overall correlation coefficient (R) during site-based independent validation and a 15% enhancement in non-AERONET site areas validation. Over the past two decades, there has been a noticeable downward trend in global fAOD (-1.39 × 10-3/year). Compared to the general deep-learning model, our method reduces the global trend's previously overestimated magnitude by 7% per year. China has experienced the most significant decline (-5.07 × 10-3/year), which is 3 times greater than the global trend. Conversely, India has shown a significant increase (7.86 × 10-4/year). This study bridges the gap between sparse in situ observations and abundant satellite measurements, thereby improving predictive models for global patterns of fAOD and other climate factors.

Research on the distribution and influencing factors of fine mode aerosol optical depth (AODf) in China

Analyzing the distribution and causes of fine mode aerosol optical depth (AODf) is important for assessing the impact of air pollution on human health and sustainable development. Based on remote sensing data and station information, the study uses Theil Sen's method and the Mann-Kendall test to analyze the spatio-temporal distribution and trends of AODf. Meanwhile, the study used the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive Explanations (SHAP) algorithm to analyze the spatial drivers of AODf and interactive effects. The results show a trend of an initial increase followed by a decline in AODf across China and in most city clusters, with AODf peaking around 2011. Areas with high AODf are mainly concentrated in the eastern and central regions. On a seasonal scale, the average AODf is highest in summer (0.25), followed by winter (0.23), autumn (0.21), and spring (0.19). The AODf, mainly in the eastern and central regions, showed rapid increases and decreases in the 2000–2013 and 2013–2020 periods, respectively. Secondly, the most important factor influencing AODf is sulfate (SO4), contributing 54.84%. This is followed by elevation (DEM) at 10.31%. The SO4, organic carbon (OC), and DEM to AODf are 20–54%, 10–44%, and 11–45% respectively at the city cluster level. Lastly, the response thresholds of AODf to the main factors show significant spatial heterogeneity. The thresholds for SO4 and OC to increase AODf are in the range of 5–16 μg/m3 and 1–10 μg/m3, respectively. The study is of great importance for a comprehensive understanding of the variation patterns of AODf and for the formulation of tailored air pollution control policies based on local conditions.

Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for VIIRS

Long-term, accurate, stable, and continuous aerosol records from space are a major requirement for climate and atmospheric environment research. Due to the limited period span of the single satellite mission, solving the problem requires the combination of multiple satellite missions. In this study, a novel aerosol retrieval algorithm, named enhanced Land General Aerosol Retrieval (e-LaGA), for Visible/Infrared Imager Radiometer Suite (VIIRS) was developed to continue Moderate-resolution Imaging Spectro-radiometer (MODIS) aerosol retrieval. e-LaGA utilizes a Surface Reflectance (SR) relationship model map to more accurately estimate the SR parameter, optimizes the priori aerosol-type map, and improves the multi-band retrieval strategy using the residual-interpolation approach. Additionally, e-LaGA expands the retrieval ability of the traditional Dark Target (DT) algorithm over bright surfaces, and can more accurately retrieve Aerosol Optical Depth (AOD), Fine-mode AOD (AODF), and Fraction (FMF). The validation using global 533 AERONET sites shows that the volume of matchups of AOD (AODF) is approximately 80000, the correlation coefficient (R) is 0.902 (0.879) and the fraction of meeting the expected error envelope (±0.05 ± 0.15τ) is 0.806 (0.804). The inter-comparison indicates that the accuracy of e-LaGA AOD retrievals is comparable to seven commonly used AOD products. The validation performance and spatial distribution pattern of e-LaGA AODF and FMF are in good agreement with the POLDER (Polarization and Directionality of the Earth's Reflectances) products. e-LaGA is also applied to MODIS sensors. The VIIRS e-LaGA AOD, AODF, and FMF retrievals have high consistency with MODIS e-LaGA. Their average bias of AOD is 0.006, which is smaller than 0.024 for the Deep Blue and 0.011 for the DT. These preliminary results demonstrate the robustness of the e-LaGA algorithm and its potential for establishing a long-term climate record of total and fine-mode aerosol by combining multiple satellite missions, which is expected to reduce the uncertainty of climate change research.

Dramatic improvement of aerosol pollution status over the East Asian ocean: from the establishment of Japanese environmental quality standard for PM2.5 in 2009 to its achievement in 2021

Abstract The severe aerosol pollution in East Asia has been a focus of much research. In Japan, the environmental quality standard (EQS) for PM2.5 was established in 2009 (daily average, 35 μg/m3; annual average, 15 μg/m3), and its achievement rate was below 50% during the early 2010s. Then, the PM2.5 concentration gradually decreased, the achievement rate improved, and the EQS for PM2.5 was finally achieved (100%) in fiscal year (FY) 2021. Because transboundary aerosol pollution is an important factor in Japanese air quality, here we analyzed the long-term dataset of the satellite-measured fine-mode aerosol optical depth (AODf) over the East Asian ocean to reveal the changes in the transboundary aerosol over East Asia. Overall, a decrease in AODf was seen over the entire East Asian ocean during the period analyzed. A gradual declining trend in AODf was measured (−4% to −5%/year over the adjacent ocean around Japan) and corresponded well to the trend in PM2.5 concentration observed in Japan (−5.3%/year) during FY2010–FY2021. Due to the domestic contribution in Japan, the negative trend was slightly greater for Japanese PM2.5 concentration than for AODf over the adjacent ocean around Japan, and we concluded that the main reason for the dramatic air quality improvement in PM2.5 in Japan was driven by the improvement of transboundary aerosol pollution over East Asia. In addition, the 12-year analysis period (FY2010 to FY2021) was divided into three parts: stagnation (FY2010 to FY2014), in which PM2.5 and AODf remained the same as they were in FY2010; improvement (FY2015 to FY2018), in which PM2.5 and AODf declined dramatically; and achievement (FY2019 to FY2021), in which PM2.5 and AODf declined further.

Unveiling global land fine- and coarse-mode aerosol dynamics from 2005 to 2020 using enhanced satellite-based monthly inversion data

Accurate fine-mode and coarse-mode aerosol knowledge is crucial for understanding their impacts on the climate and Earth's ecosystems. However, current satellite-based Fine-Mode Aerosol Optical Depth (FAOD) and Coarse-Mode Aerosol Optical Depth (CAOD) methods have drawbacks including inaccuracies, low spatial coverage, and limited temporal duration. To overcome these issues, we developed new global-scale FAOD and CAOD from 2005 to 2020 using a novel deep learning model capable of the synergistic retrieval of two aerosol sizes. After validation with the aerosol robotic network (AERONET) and sky radiometer network (SKYNET), the new monthly FAOD and CAOD showed significant improvements in accuracy and spatial coverage. From 2005 to 2020, the new data showed that China had the greatest decrease in FAOD and CAOD. In contrast, India and South Latin America had a significant increase in FAOD versus North Africa in CAOD. FAOD in the regions of Argentina, Paraguay, and Uruguay in South America have shown an upward trend. The results reveal that FAOD and CAOD display distinct patterns of change in the same regions, particularly on the west coast of the United States where FAOD is increasing, while CAOD is decreasing. Aside from the year 2020 due to the global COVID-19 pandemic, the analysis showed that although China has seen at least an +85% increase in energy consumption and urban expansion in 2019 compared to 2005 due to the needs of development and construction, the implementation of China's air pollution control policies has led to a significant decrease in FAOD (−46%) and CAOD (−65%) after 2013. This research enriches our comprehension of global fine and coarse aerosol patterns, additional investigations are needed to determine the potential global implications of these changes.

Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments

Aerosol Optical Depth (AOD) data derived from satellites is crucial for estimating spatially-resolved PM concentrations, but existing AOD data over land remain affected by several limitations (e.g., data gaps, coarser resolution, higher uncertainty or lack of size fraction data), which weakens the AOD-PM relationship. We developed a 0.1° resolution daily AOD data set over Europe over the period 2003–2020, based on two-stage Quantile Machine Learning (QML) frameworks. Our approach first fills gaps in satellite AOD data and then constructs three components' models to obtain reliable full-coverage AOD along with Fine-mode AOD (fAOD) and Coarse-mode AOD (cAOD). These models are based on AERONET (AErosol RObotic NETwork) observations, Gap-filled satellite AOD, climate and atmospheric composition reanalyses. Our QML AOD products exhibit better quality with an out-of-sample R2 equal to 0.68 for AOD, 0.66 for fAOD and 0.65 for cAOD, which is 23–92 %, 11–13 % and 115–132 % higher than the corresponding satellite or reanalysis products, respectively. Over 91.6 %, 81.6 %, and 88.9 % of QML AOD, fAOD and cAOD predictions fall within ±20 % Expected Error (EE) envelopes, respectively. Previous studies reported that a weak satellite AOD-PM correlation across Europe (Pearson correlation coefficient (PCC) around 0.1). Our QML products exhibit higher correlations with ground-level PMs, particularly when broadly matched by size: AOD with PM10, fAOD with PM2.5, cAOD with PM coarse (R = 0.41, 0.45 and 0.26, respectively). Different AOD fractions more effectively distinct PM size fractions, than total AOD. Our QML aerosol dataset and models pioneer full-coverage, daily high-resolution monitoring of fine-mode and coarse-mode aerosols, effectively addressing existing AOD challenges for further PMs exposures' estimations. This dataset opens avenues for more in-depth exploration of the impacts of aerosols on human health, climate, visibility, and biogeochemical processes, offering valuable insights for air quality management and environmental health risk assessment.

MAGARA: a Multi-Angle Geostationary Aerosol Retrieval Algorithm

Abstract. For over 40 years, the Geostationary Operational Environmental Satellite (GOES) system has provided frequent snapshots of the Western Hemisphere. The advanced baseline imagers (ABIs) on the GOES-16, GOES-17, and GOES-18 platforms are the first GOES-series imagers that meet the precision requirements for high-quality, aerosol-related research. We present MAGARA, a Multi-Angle Geostationary Aerosol Retrieval Algorithm, that leverages multi-angle ABI imagery to exploit the differences in autocorrelation timescales between surface reflectance, aerosol type, and aerosol loading. MAGARA retrieves pixel-level (up to 1 km) aerosol loading and fine-mode fraction at up to the cadence of the measurements (10 min), fine- and coarse-mode aerosol particle properties at a daily cadence, and surface properties by combining the multi-angle radiances with robust surface characterization inherent to temporally tiled algorithms. We present three case studies, and because GOES-17 was not making observations for one case, we present this as a unique demonstration of the multi-angle algorithm using only a single ABI sensor. We also compare MAGARA retrievals of fine-mode (FM) aerosol optical depth (AOD), coarse-mode (CM) AOD, and single-scattering albedo (SSA) statistically, with coincident AErosol RObotic NETwork (AERONET) spectral deconvolution algorithm (SDA) and inversion retrievals for the same period, and against bias-corrected NOAA GOES-16 and GOES-17 retrieved 550 nm AOD. For MAGARA vs. coincident AERONET over-land 500 nm fine-mode fraction and AOD>0.3, MAE=0.031, RMSE=0.100, and r=0.902, indicating good sensitivity to fine-mode fraction over land, especially for smoky regions. For bias-corrected MAGARA vs. coincident AERONET spectral single-scattering albedo with MAGARA AOD>0.5 (n=116), MAE=0.010, RMSE=0.015, and the correlation is 0.87. MAGARA performs best in regions where surface reflectance varies over long timescales with minimal clouds. This represents a large portion of the western half of the United States, much of north-central Africa and the Middle East, some of central Asia, and much of Australia. For these regions, aerosol type and aerosol loading on timescales as short as 10 min could allow for novel research into aerosol–cloud interactions, improvements to air-quality modeling and forecasting, and tighter constraints on direct aerosol radiative forcing.

Aerosol classification by application of machine learning spectral clustering algorithm

Precise understanding of aerosol classification is crucial for accurately quantifying the effects of aerosols on the Earth’s energy budget, improving remote sensing retrieval algorithms, formulating climate change-related policies, and more. In this study, we used aerosol measurements from the quality assured AERosol Robotic NETwork (AERONET) and utilized a multivariate spectral clustering algorithm, a machine learning tool, to classify global aerosols. The spectral clustering algorithm is a variant of the clustering algorithm that employs eigenvalues and eigenvectors of the data matrix to project the data into a lower-dimensional space of a similar cluster. To accomplish this, we considered five aerosol optical parameters: fine-mode Aerosol Optical Depth, Extinction Angstrom Exponent, Absorption Angstrom Exponent, Single Scattering Albedo, and Refractive Index from 150 AERONET sites distributed in six continents (Africa, Asia, Australia, Europe, North and South America) during 1993 to 2022. Using the clustering analysis, we identified four primary aerosol types: dust, urban, biomass burning, and mixed aerosols. Among the continents, the African and Asian sites exhibited the highest contribution of dust aerosols, as the region has significant global dust sources. Conversely, Australia, Europe, North, and South America are predominantly influenced by fine-mode aerosols, given their considerable distance from major dust source regions.

Detection and analysis of Lhù'ààn Mân' (Kluane Lake) dust plumes using passive and active ground-based remote sensing supported by physical surface measurements

Abstract. There is growing recognition that high-latitude dust (HLD), originating from local drainage-basin flows, is the dominant source for certain important phenomena such as particle deposition on snow/ice. The analysis of such local plumes (including a better exploitation of remote sensing data) has been targeted as a key aerosol issue by the HLD community. The sub-Arctic Lhù'ààn Mân' (Kluane Lake) region in the Canadian Yukon is subject to regular drainage-basin, wind-induced dust plumes. This dust emission site is one of many current and potential proglacial dust sources in the Canadian north. In situ ground-based measurements are, due to constraints in accessing these types of regions, rare. Ground- and satellite-based remote sensing accordingly play an important role in helping to characterize local dust sources in the Arctic and sub-Arctic. We compared ground-based passive and active remote sensing springtime (May 2019) retrievals with microphysical surface-based measurements in the Lhù'ààn Mân' region in order to better understand the potential for ground- and satellite-based remote sensing of HLD plumes. This included correlation analyses between ground-based coarse mode (CM) aerosol optical depth (AOD) retrievals from AERONET AOD spectra, CM AODs derived from co-located Doppler lidar profiles, and OPS (optical particle sizer) surface measurements of CM particle-volume concentration (vc(0)). An automated dust classification scheme was developed to objectively identify local dust events. The classification process helped distinguish lidar-derived CM AODs which covaried with vdust(0) (during recognized dust events) and those that varied at the same columnar scale as AERONET-derived CM AOD (and thus could be remotely sensed). False positive cloud events for dust-induced, high-frequency variations in lidar-derived CM AODs in cloudless atmospheres indicated that the AERONET cloud-screening process was rejecting CM dust AODs. The persistence of a positive lidar ratio bias in comparing the CIMEL/lidar-derived value with a prescribed value obtained from OPS-derived particle sizes coupled with dust-speciation-derived refractive indices led to the suggestion that the prescribed value could be increased to optically derived values of 20 sr by the presence of optically significant dust particles at an effective radius of 11–12 µm. Bimodal CM PSDs (see Appendix B for a glossary) from full-fledged AERONET inversions (the combination of AOD spectra and almucantar radiances) also showed CM peaks at ∼ 1.3 and 5–6.6 µm radius: this, we argue, was associated with springtime Asian dust and Lhù'ààn Mân' dust, respectively. Correlations between the CIMEL-derived fine mode (FM) AOD and FM OPS-derived particle-volume concentrations suggest that remote sensing techniques can be employed to monitor FM dust (which is arguably a better indicator of the long-distance transport of HLD).

The Role of Midlatitude Cyclones in the Emission, Transport, Production, and Removal of Aerosols in the Northern Hemisphere

AbstractWe examine the distribution of aerosol optical depth (AOD) across 27,707 northern hemisphere (NH) midlatitude cyclones for 2005–2018 using retrievals from the Moderate Resolution Spectroradiometer (MODIS) sensor on the Aqua satellite. Cyclone‐centered composites show AOD enhancements of 20%–45% relative to background conditions in the warm conveyor belt (WCB) airstream. Fine mode AOD accounts for 68% of this enhancement annually. Relative to background conditions, coarse mode AOD is enhanced by more than a factor of two near the center of the composite cyclone, co‐located with high surface wind speeds. Within the WCB, MODIS AOD maximizes in spring, with a secondary maximum in summer. Cyclone‐centered composites of AOD from the Modern Era Retrospective analysis for Research and Applications, version 2 Global Modeling Initiative (M2GMI) simulation reproduce the magnitude and seasonality of the MODIS AOD composites and enhancements. M2GMI simulations show that the AOD enhancement in the WCB is dominated by sulfate (37%) and organic aerosol (25%), with dust and sea salt each accounting for 15%. MODIS and M2GMI AOD are 60% larger in North Pacific WCBs compared to North Atlantic WCBs and show a strong relationship with anthropogenic pollution. We infer that NH midlatitude cyclones account for 355 Tg yr−1 of sea salt aerosol emissions annually, or 60% of the 30–80°N total. We find that deposition within WCBs is responsible for up to 35% of the total aerosol deposition over the NH ocean basins. Furthermore, the cloudy environment of WCBs leads to efficient secondary sulfate production.

Relationship between the sub-micron fraction (SMF) and fine-mode fraction (FMF) in the context of AERONET retrievals

Abstract. The sub-micron (SM) aerosol optical depth (AOD) is an optical separation based on the fraction of particles below a specified cutoff radius of the particle size distribution (PSD) at a given particle radius. It is fundamentally different from spectrally separated FM (fine-mode) AOD. We present a simple (AOD-normalized) SM fraction versus FM fraction (SMF vs. FMF) linear equation that explains the well-recognized empirical result of SMF generally being greater than the FMF. The AERONET inversion (AERinv) products (combined inputs of spectral AOD and sky radiance) and the spectral deconvolution algorithm (SDA) products (input of AOD spectra) enable, respectively, an empirical SMF vs. FMF comparison at similar (columnar) remote sensing scales across a variety of aerosol types. SMF (AERinv-derived) vs. FMF (SDA-derived) behavior is primarily dependent on the relative truncated portion (εc) of the coarse-mode (CM) AOD associated with the cutoff portion of the CM PSD and, to a second order, the cutoff FM PSD and FM AOD (εf). The SMF vs. FMF equation largely explains the SMF vs. FMF behavior of the AERinv vs. SDA products as a function of PSD cutoff radius (“inflection point”) across an ensemble of AERONET sites and aerosol types (urban-industrial, biomass burning, dust, maritime and a mixed class of Arctic aerosols). The overarching dynamic was that the linear SMF vs. FMF relation pivots clockwise about the approximate (SMF, FMF) singularity of (1, 1) in a “linearly inverse” fashion (slope and intercept of approximately 1−εc and εc) with increasing cutoff radius. SMF vs. FMF slopes and intercepts derived from AERinv and SDA retrievals confirmed the general domination of εc over εf in controlling that dynamic. A more general conclusion is the apparent confirmation that the optical impact of truncating modal (whole) PSD features can be detected by an SMF vs. FMF analysis.

Estimating the Wind Dependency of Aerosol Optical Depth at Remote Oceanic Regions

In this article, the applicability of India’s indigenously developed scatterometer, Scatsat-1 for marine aerosol characterization and wind dependency of aerosol optical depth (AOD) was investigated using 3 years of satellite data. Wind data from Scatsat-1 and AOD with its size fraction from SNPP VIIRS from 2017 to 2019 were used for the investigation. A technique has been demonstrated for identifying remote oceanic regions with mostly marine aerosol using certain criteria like distance from land, higher fraction of coarse AOD, lower variation of AOD with wind direction, and so forth. Daily wind and AOD from 2017 to 2019 were used for studying the relationship between wind speed and AOD at remote oceanic locations away from terrestrial and anthropogenic influence. Five regions of interest (ROIs) of 5° by 5° were identified in different oceanic regions for carrying out the regression analysis. A linear increase of coarse mode AOD and total AOD with an increase in wind speed was observed for all five remote locations while fine mode AOD was unrelated. The slope of the linear relation agreed with Kiliyanpilakkil and Meskhidze (2011) for intermediate wind values. The remote regions also had a lower variation of AOD with change in wind direction implying less transport of aerosol from nearby landmasses.

Global validation and comparison of MODIS DT and POLDER-3 GRASP aerosol products over ocean

The Moderate Resolution Imaging Spectroradiometer (MODIS) and the third generation of Polarization and Directionality of Earth's Reflectances (POLDER-3) are two types of sensors, which have their own advantages in sensor design. MODIS is a conventional multi-spectral imager with single-angle observations and a wide spectral range of surface reflection bands from blue to shortwave infrared, while POLDER-3 is a multi-angle polarization instrument with narrower spectral range from blue to shortwave near-infrared. It is highly anticipated to understand the quantitative capability of these two sensors in aerosol retrievals. For this purpose, this study conducts a long-time series comparison and evaluation of over-ocean aerosol products from these two types of sensors during the period 2005–2013, corresponding to the entire lifetime of the POLDER-3 products. The MODIS aerosol products based on Dark Target (DT) algorithm and two POLDER-3 products based on General Retrieval of Atmosphere and Surface Properties (GRASP) “High Precision” (GRASP/HP) and “Models” (GRASP/Models) algorithm are adopted in this study. Two types of ground-based measurements from automatic Aerosol Robotic Network (AERONET) data and shipborne Maritime Aerosol Network (MAN) data are employed to validate four parameters common to both satellite products, including total Aerosol Optical Depth (AOD), Ångström Exponent (AE), fine-mode AOD (AODF) and coarse-mode AOD (AODC). The overall validation results show GRASP/Models has similar performance with MODIS DT in total AOD products. This indicates that the multi-angle polarization observations of POLDER-3 can have the same advantages as the multi-spectral observations of MODIS in total AOD retrievals. Furthermore, the statistical parameters of the validation results based on AERONET data show that GRASP/Models is slightly better than MODIS DT. However, for the validation results using MAN data, it is interesting to find that MODIS DT is slightly better than GRASP/Models in all aspects. This may be due that the manual measurements of MAN can consciously avoid more cloud contamination. Meanwhile, it suggests that MODIS DT algorithm over ocean needs further improvements in cloud mask and quality control. In addition, from the validation results of both types of ground-based data, it shows that GRASP/HP significantly overestimates AOD, but has better validation accuracy than GRASP/Models and MODIS DT in AE products, which implies that multi-angle polarization sensor has great potential in aerosol properties retrieval. For AODF and AODC, the statistical parameters of validation results for all three products show little difference. GRASP/HP is slightly better than GRASP/Models and MODIS DT in AODF products, while the AODC products of GRASP/Models and MODIS DT show slightly superior to that of GRASP/HP. This may suggest that although the higher AE validation accuracy of GRASP/HP, the higher total AOD accuracy of GRASP/Models and MODIS DT still dominates the validation results of AODF and AODC. The results could contribute to the application of over-ocean aerosol products of these two satellite in climate research, as well as to the improvement of aerosol retrieval algorithms over ocean for instruments such as single viewing spectrometer and multi-angle polarimeter.

Extended validation and evaluation of the OLCI–SLSTR SYNERGY aerosol product (SY_2_AOD) on Sentinel-3

Abstract. We present the first extended validation of a new SYNERGY global aerosol product (SY_2_AOD), which is based on synergistic use of data from the Ocean and Land Color Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR) sensors aboard the Copernicus Sentinel-3A (S3A) and Sentinel-3B (S3B) satellites. Validation covers period from 14 January 2020 to 30 September 2021. Several approaches, including statistical analysis, time series analysis, and comparison with similar aerosol products from the other spaceborne sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), were applied for validation and evaluation of S3A and S3B SY_2 aerosol products, including aerosol optical depth (AOD) provided at different wavelengths, AOD pixel-level uncertainties, fine-mode AOD, and Angström exponent. Over ocean, the performance of SY_2 AOD (syAOD) retrieved at 550 nm is good: for S3A and S3B, Pearson correlation coefficients with the Maritime Aerosol Network (MAN) component of the AErosol RObotic NETwork (AERONET) are 0.88 and 0.85, respectively; 88.6 % and 89.5 % of pixels fit into the MODIS error envelope (EE) of ±0.05 ± 0.2 × AOD. Over land, correlation coefficients with AERONET AOD (aAOD) are 0.60 and 0.63 for S3A and S3B, respectively; 51.4 % and 57.9 % of pixels fit into MODIS EE. Reduced performance over land is expected since the surface reflectance and angular distribution of scattering are higher and more difficult to predict over land than over ocean. The results are affected by a large number of outliers. Evaluation of the per-retrieval uncertainty with the χ2 test indicates that syAOD prognostic uncertainties (PU) are slightly underestimated (χ2 = 3.1); if outliers are removed, PU describes the syAOD error well (χ2 = 1.6). The regional analysis of the Angström exponent, which relates to the aerosol size distribution, shows spatial correlation with expected sources. For 40 % of the matchups with AERONET in the Northern Hemisphere (NH) and for 60 % of the matchups in the Southern Hemisphere (SH), which fit into the AE size range of [1, 1.8], an offset between SY_2 AE (syAE) and AERONET AE (aAE) is within ±0.25. General overestimation of low (< 0.5) syAE and underestimation of high (> 1.8) syAE results in high (0.94, globally) overall bias. Good agreement (bias < 0.03) was observed between Sy_2 fine-mode AOD (syFMAOD) and AERONET fine-mode AOD (aFMAOD) for aFMAOD < 1. At aFMAOD > 1, syFMAOD is considerably underestimated (by 0.3–0.5 in different aFMAOD ranges) in the NH. In the SH, only a few aFMAOD values above 1 are measured. The fine-mode fraction (FMF) in the SY_2 AOD product (syFMF) in the range of [0, 0.7] is overestimated; the positive offset of 0.3–0.5 for low (< 0.25) FMF gradually decreases. Differences between the annual and seasonal AOD values from SY_2 and MODIS (mod) Dark Target and Deep Blue products are within 0.02 for the study area (30∘ S–60∘ N, 80∘ W–45∘ E). The agreement is better over ocean; however, a difference up to 0.6 exists between syFMF and modFMF. Over bright land surface (Saharan desert) the difference in AOD between the two products is highest (up to 0.11); the sign of the difference varies over time and space. For both S3A and S3B AOD products, validation statistics are often slightly better in the Southern Hemisphere. In general, the performance of S3B is slightly better.

Retrieval of Aerosol Optical Properties over Land Using an Optimized Retrieval Algorithm Based on the Directional Polarimetric Camera

The Directional Polarization Camera (DPC) onboard the Chinese Gaofen-5 satellite, launched in May 2018, has similar specifications as the POLDER-3 instrument. The SRON Remote Sensing of Trace gas and Aerosol Products (RemoTAP) full retrieval algorithm is applied to DPC measurements to retrieve aerosol properties including the total Aerosol Optical Depth (AOD), the fine/coarse mode AOD and the SSA (Single Scattering Albedo). Measurements of the global ground-based AERONET network between December 2019 and April 2020 have been used for the validation of the DPC retrievals. According to the average Fine Mode Fraction (FMF) of the selected AERONET stations, the stations are divided into urban stations (FMF ≥ 0.5) and dust stations (FMF < 0.5). For the total AOD validation, DPC retrievals show better performance over urban stations than over dust stations, with average biases of 0.055 and 0.106, and RMSEs of 0.151 and 0.228, respectively. Regarding the fine mode AOD, the retrieval also performs better over urban stations. Compared with the total AOD validation, both the relatively lower bias (0.021 and 0.065) and the higher Gfrac (Fraction of Good retrieval, 63.8% and 47.3%, respectively) further indicate that DPC performs better when fine mode aerosols dominate. For the land SSA validation, most of our SSA retrievals (~71%) show differences with AERONET SSA retrievals lower than 0.05. Case studies over fire spots and dust over northern China demonstrate the encouraging application potential of DPC aerosol products. The difference between fine and coarse AOD can provide more aerosol source information compared with the total AOD alone. Since the SSA retrievals are particularly sensitive to absorbing fine particles, they can be easily used in the tracking of biomass burning aerosol.

The influence and contribution of fine mode particles to aerosol optical properties during haze events at the foothills of Himalaya-Karakorum region

In the last decade, the regional haze is considered as one of the most devastating weathers which causes serious impacts on air quality, human health, and transportation over the Indo-Gangetic Plain, particularly the eastern part of Pakistan. In this paper for the first time, the long-term characterization of aerosols optical properties during haze events were carried out using ground-based Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite over Lahore and Faisalabad for the period of 2009–2018. Firstly, the MODIS Aerosol Optical Depth (AOD) was correlated with AERONET AOD and reported a high degree of R-squared correlation of value 0.78 between two datasets for the months of October, November and December during the study period (2009–2018). After validation of data, the daily averaged AOD and Angstrom Exponent (AE) values exceeding 1 were selected as heavy haze events. Then the aerosol optical properties such as AOD, AE, Volume Size Distribution (VSD), and Fine Mode Fraction (FMF) of AOD were retrieved from AERONET and MODIS for further analysis. The distribution of AOD and AE showed that most of the values were greater than 1.3 and 1.1, respectively, during haze events over both study sites. The daily averaged profile of aerosol VSD exhibited a bimodal lognormal distribution having dominant fine mode peaks at radius between 0.19 and 0.25 μm as compared to coarse modes peaks. Furthermore, the contribution of fine-mode AOD to the total AOD was quite higher (i.e., 86–98%) during haze events showing the greater contribution of fine mode aerosol particles. The correlation of AE with AOD and FMF revealed that the pronounced aerosol types were fine mode aerosol particles originated due to biomass burning and urban/industrial emissions during haze events. These types of fine mode aerosol particles were also confirmed with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) subtype measurements. Furthermore, the back trajectories revealed that most air masses were received from the local/regional aerosol sources, showing the contribution of fine mode aerosol particles to haze events whilst some of the trajectories were from Afghanistan, Iran, and India showing long range transportation of air masses to the receptor sites.

Aerosol characterisation in the subtropical eastern North Atlantic region using long-term AERONET measurements

Abstract. A comprehensive characterisation of atmospheric aerosols in the subtropical eastern North Atlantic has been carried out using long-term ground-based Aerosol Robotic NETwork (AERONET) photometric observations over the period 2005–2020 from a unique network made up of four stations strategically located from sea level to 3555 m on the island of Tenerife. This site can be considered a sentinel for the passage of airmasses going to Europe from Africa, and therefore the aerosol characterisation performed here adds important information for analysing their evolution during their path toward Northern Europe. Two of these stations (Santa Cruz de Tenerife – SCO – at sea level and La Laguna – LLO – at 580 m a.s.l.) are located within the marine atmospheric boundary layer (MABL), and the other two (Izaña – IZO – at 2373 m a.s.l. and Teide Peak – TPO – at 3555 m a.s.l.) are high mountain stations within the free troposphere (FT). Monthly climatology of the aerosol optical depth (AOD), Ångström exponent (AE), aerosol concentration, size distribution and aerosol optical properties has been obtained for the MABL and FT. Measurements that are quite consistent across the four sites have been used to categorise the main atmospheric scenarios, and these measurements confirm an alternation between predominant background conditions and predominant dust-loaded Saharan air mass conditions caused by seasonal dust transport over the subtropical North Atlantic. Background conditions prevail in the MABL and FT for most of the year, while dust-laden conditions dominate in July and August. The MABL under background conditions appears as a well-mixed layer with a low aerosol concentration (the volume concentration, VolCon, ranges from 0.02 ± 0.01 to 0.04 ± 0.02 µm3 µm−2), a predominance of coarse-mode marine aerosols (the effective radius, Reff, changes from 1.60 ± 0.19 to 1.91 ± 0.34 µm), and a volume contribution of the fine-mode fraction Vf/Vt <0.35. The clean FT is characterised by remarkably low aerosol loading and a predominant impact of fine-mode aerosols throughout the year (Vf/Vt has a maximum value of 0.93 ± 0.13), with an average Reff of 0.16 ± 0.02 µm. However, under dust-laden conditions and mainly in summer, we observe a predominance of coarse-mode aerosols with maximum VolCon values of 0.26 ± 0.23 µm3 µm−2 for the MABL and 0.16 ± 0.12 (0.06 ± 0.05) µm3 µm−2 for IZO (TPO), and a similar and quite consistent fine-mode fraction of 0.12 ± 0.03 in the vertical within the MABL and FT. Similarities in micro-physical and optical intensive aerosol properties confirm that the Saharan Air Layer (SAL) is a well-mixed layer in terms of the particulate composition. An estimation of the difference in the aerosol loading in the 1 km layer between IZO and TPO (in terms of VolCon and AOD) is performed in this study, and this shows that aerosol loading at IZO is double that at TPO, but they have similar fine-mode fractions, effective radii and intensive optical properties. The long-term trend analysis at SCO shows a significant negative trend in the fine-mode AOD between 2005 and 2020 (−1.8 ± 0.5) × 10−5 yr−1, which might be linked to the large reduction in oil-refining SO2 emissions from the SCO refinery in 2012.

Retrieval of total and fine mode aerosol optical depth by an improved MODIS Dark Target algorithm.

Total and fine mode aerosol optical depth (AODT and AODF), as well as the fine mode fraction (FMF=AODF/AODT), are critical variables for climate change and atmospheric environment studies. The retrievals with high accuracy from satellite observations, particularly FMF and AODF over land, remain challenging. This study aims to improve the Moderate-resolution Imaging Spectro-radiometer (MODIS) land dark target (DT) algorithm for retrieving AODT, AODF, and FMF on a global scale. Based on the fact that the underestimated surface reflectance (SR) could overestimate the AODT and underestimate the aerosol size parameter in the DT algorithm, two robust schemes were developed to improve SR determination: the first (NEW1 DT) used the top of the atmosphere reflectance instead of SR at 2.12µm; the second (NEW2 DT) used eleven-year MODIS data to establish a monthly spectral SR relationship model (2.12-0.47 and 2.12-0.65µm) database at pixel-by-pixel scale. Then a novel lookup table approach based on the physical process was proposed to retrieve the AODF and FMF. The new MODIS AODT, FMF, and AODF were compared to AERosol RObotic NETwork (AERONET) retrievals. Results showed that the root mean square error (RMSE) was 0.096-0.103, 0.098-0.099, and 0.167-0.180 for the new AODTs, AODFs, and FMFs, respectively, which were better than that of the Collection 6.1 (C6.1) DT (0.117, 0.235, and 0.426) in the validation by global AERONET sites. From the validation results, NEW2 DT provided better AODT and coarse mode AOD retrievals, while NEW1 DT had better AODF and FMF performances. The spatial patterns of AODF, FMF, and AODC of the new DT algorithms were comparable to those of the Polarization and Directionality of the Earth's Reflectances aerosol product. Hence, the new algorithms have the potential to provide global AODT, FMF, and AODF products over land to the scientific community with high accuracy using long-term MODIS data.

The Potential for Great Barrier Reef Regional Climate Regulation via Dimethylsulfide Atmospheric Oxidation Products

Research related to the potential for coral reef-derived dimethylsulfide (DMS) oxidation products to regulate the regional climate of the Great Barrier Reef (GBR) according to the CLAW hypothesis is summarized in this mini review. The GBR has been indicated as a region of high DMS production where atmospheric emissions may be increased when corals are subject to environmental stresses associated with low tide. During low wind speeds over aerially exposed coral reefs, plumes of atmospheric DMS and new sulfate-containing nano-particle production under photo-oxidative conditions have been detected on the GBR. Hygroscopic growth of these particles in combination with coagulation and condensation processes could potentially provide a coral-mediated mechanism of new aerosol for seeding low-level stratocumulus clouds. Fine mode aerosol optical depth over GBR coral reefs has been correlated with low wind speeds and a coral stress metric formulated as a function of irradiance, water clarity, and tide height. This correlation has been proposed as a possible mechanism by which the GBR might alter the optical properties of the overlying atmosphere to attenuate local insolation leading to regional climate regulation. However, recent regional-scale aerosol-climate modeling indicates that the potential for GBR regional climate regulationviaDMS atmospheric oxidation products is weak under current anthropogenic conditions which have instigated mass coral bleaching events along the entire length of the GBR between 1998 and 2022. This increased bleaching indicates that DMS oxidation products are insufficient to regulate the regional climate of the GBR according to the CLAW hypothesis under current global warming conditions.