Optical Depth Retrievals Research Articles

Investigating the applicability of a simple iterative approach for aerosol optical depth (AOD) retrieval over diverse land surface types from Landsat 8 and Sentinel 2 using visible and near-infrared (VNIR) spectral bands

Previous attempts at aerosol optical depth (AOD) retrieval from top-of-atmosphere satellite signals using a simplified approach have been limited in their assessment over selective surface types, calibration sites and given the low surface reflectance components at lower wavelengths, have mostly relied, fully or partially, on near-UV or blue spectral bands for AOD retrieval. The present study attempts to investigate the applicability of a simple iterative approach for retrieval of AOD over diverse land surface types using any of the visible and near-infrared (VNIR) spectral bands. The results have been validated for 18 AERONET sites from 7 countries in South Asia and we found a strong correspondence between retrieved AOD and the AERONET AOD with good correlation (r > 0.80), both at 550 nm as well as at individual spectral bands. For image-based comparison, the results have been compared with those from MODIS products. While the retrievals show strong spatial similarity with MODIS, in absolute terms, they are mostly underestimated. Nevertheless, the coincident retrievals from both sensors do appear to be very similar, with intercomparison also showing good correlation. Overall, the iterative approach for AOD retrieval performs reasonably well for different surface types and can be easily adopted for most sensors, especially those which do not have deep blue or blue spectral bands.

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).

Retrieval of nighttime aerosol optical depth by simultaneous consideration of artificial and natural light sources

Aerosol Optical Depth (AOD) is a critical optical parameter that quantifies the degree of light attenuation by aerosols and serves as a fundamental indicator of atmospheric quality. Therefore, accurate quantification and retrieval of AOD is crucial for relevant studies. However, current satellite-based AOD retrieval algorithms suffer from inapplicability under low-light conditions, limiting the development of nighttime AOD retrieval. Under this context, we proposed a novel algorithm, namely Simultaneous Consideration of Artificial and Natural light Sources (SCANS), to obtain nighttime AOD. The core of the SCANS algorithm is considering the synergy of both the natural and artificial light sources to obtain nighttime AOD by integrating atmospheric radiative transfer simulation into an extinction method and performing multiple iterations. SCANS was applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) and the retrieved nighttime AOD was validated with in-situ measurements from five AERONET sites. Results indicate that the Mean Bias Errors (MBEs) of the retrieved nighttime AOD range from 0.0 to 0.08 and the corresponding Root Mean Square Errors (RMSEs) range from 0.11 to 0.17, which exhibit better accuracy than that of the nighttime MERRA-2 AOD. We further compared the retrieved nighttime AOD with the corresponding Air Quality Index (AQI) measurements at six environment monitoring stations and obtained high correlation coefficients (i.e., ranging from 0.733 to 0.940), indicating SCANS's reliability and high accuracy. The proposed SCANS algorithm can effectively obtain nighttime AOD with high quality, thereby advancing research on the diurnal variation of crucial Earth's key elements.

Forecasting of an unusual dust event over western India by the Air Quality Early Warning System

This study presents the successful implementation of an operational Air Quality Early Warning System (AQEWS) in forecasting an unprecedented dust storm event that occurred in the western part of India from 21–24 January 2022. The AQEWS generates a daily 72-h forecast using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) at the core, which is initialized with assimilated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals and in-situ measurements of fine particulate matter (PM2.5). The AQEWS was able to forecast the outbreak of this dust storm reasonably well over the north of the Arabian Sea on 21 January, as well as its movement and dispersal towards the Indian landmass in the subsequent 72 h, resulting in a significant amount of transported dust that engulfed the west Indian region on 23–24 January. We utilize satellite observations and in situ measurements to quantify the ability of the forecasting system to predict the spatiotemporal characteristics of the dust storm. Notably, the AQEWS aerosol data assimilation process played a critical role in forecasting the anomalous increase of PM10 in several west Indian cities, as it significantly improved the initial conditions of PM10 by approximately 24–40 μg/m3 and PM2.5 concentration by approximately 30–38 μg/m3. Without assimilation, the system fails to capture the intensity, and pattern of variability of the dust storm. The mean bias of the 72-h prediction of PM10 was substantially reduced with assimilation, from −44% to 0.35% in Mumbai and from −57% to 13% in Gujarat. In selected regions, in situ measurements confirmed that the AQEWS adequately captured the peak intensity and pattern of the dust storm event. The AQEWS thus demonstrated improved forecasting skills in predicting an extreme dust event during winter and the adoption of chemical data assimilation enhanced the accuracy of the real-time air quality forecasting system.

Long-term trends of aerosol and cloud fraction over Eastern China based on ground-based observations

We looked into the long-term variability of aerosols and clouds over Eastern China (EC) using MICAPS ground-based data from 1995 to 2019 and MODIS aerosol optical depth (AOD) retrieval from 2003 to 2019. The ground-based observed visibility (VIS) was used to characterize aerosol concentrations. Our study indicate that VIS shows a significant shift over EC in recent decades, as evidenced by falling (P1: 1995–2006)-flat (P2: 2007–2013)-rising (P3: 2014–2019) periods. Correspondingly, a change was observed in cloud fraction (CF) from a strong increase to a slow fluctuation during the three periods over the Beijing-Tianjin-Hebei (BTH), but no significant variation over the Yangtze River Delta (YRD) region. In addition, the CF-VIS shows a negative correlation during both P1 and P3 periods while this relationship weakened during P3 (35% weaker in BTH and 44% weaker in YRD). However, it is important to note that this weakening may be influenced by a few factors, such as data processing methods, the location of aerosols relative to the cloud, relative humidity (RH), and large-scale circulation etc. Further studies, including numerical simulations and analysis, are required to determine how much change of cloudiness is attributed to aerosols.

Operational AOD Retrieval at Subkilometer Resolution Using OceanSat‐2 OCM Over Land: SAER Algorithm, Uncertainties, Validation & Inter‐Sensor Comparison

AbstractThe ocean color monitor (OCM) sensor onboard OceanSat‐2 is providing data in visible and near Infrared (NIR) bands. Due to the limited spectral coverage of OCM, the widely used dark‐target and deep‐blue aerosol algorithms cannot be adapted. Here, a new aerosol optical depth (AOD) retrieval algorithm for OCM (or similar sensors) over land, termed Space Applications Center AErosol Retrieval (SAER) is described. It utilizes two blue bands for the AOD inversion and, Red and NIR band to characterize the surface in visible bands without assuming red and NIR bands transparent to aerosols. Unlike the dark‐target algorithm, the SAER algorithm can retrieve AOD over bright arid and urban areas too. The uncertainty analysis of SAER suggests a theoretically expected error (EE) envelope of ±(0.06 + 0.26 × AOD) for typical retrieval conditions. OCM AOD over land is retrieved operationally for the first time over Indian and neighboring countries' landmass at the finest spatial resolution of 0.007°. The SAER algorithm is validated against in‐situ AOD measurements during the years 2016–2018 at 21 Aerosol Robotic Network stations located in south‐Asia. Overall validation using 1900 match‐up points shows correlations exceeding 0.8 with 74% of retrievals within EE. The retrievals over cropland, grassland, and mixed land cover types show high (low) correlation (bias), while over bright urban areas somewhat low (high) correlation (bias) is observed. Excluding monsoon season, OCM AOD retrievals show good performance over the year. The performance of MODerate Resolution Imaging Spectroradiometer dark‐target and OCM AOD, against common in‐situ, is close to each other. The study shows that OCM AOD can be used for air quality monitoring/modeling at high spatial resolution.

Downwind Ozone Changes of the 2019 Williams Flats Wildfire: Insights From WRF‐Chem/DART Assimilation of OMI NO2, HCHO, and MODIS AOD Retrievals

AbstractThis study investigates the impacts of the Williams Flats wildfire (August 3‐9, 2019) on ozone conccentrations and chemistry in the downwind region using an assimilation framework with satellite retrievals and meterological measurements. By assimilating satellite‐retrieved nitrogen dioxide (NO2) and formaldehyde (HCHO) columns, we improve surface ozone simulations (index of agreement of 0.94 compared to station measurements) and utilize aerosol optical depth (AOD) retrievals to simulate spatial dynamics of wildfire plumes better. Comparing simulated ozone with the Fire Influence on Regional to Global Environments and Air Quality aircraft measurements reveals a fair simulation of ozone in smoke and non‐smoke plumes within the lower troposphere (7.00 ppbv mean absolute error). Comparing near‐distance and far‐distance locations, the study observed higher‐than‐average concentrations of nitrogen oxides, peroxyacetyl nitrate, nitric acid, and oxygenated volatile organic compounds due to wildfire emissions, which increased ozone concentrations by 3–5 ppbv during the wildfire in near‐distance locations and 2–3 ppbv in far‐distance locations. During more intense wildfire days, above 120 ppbv carbon monoxide plumes transported over the Rocky Mountains to the east. Ozone regime indicators showed a clear transition area downwind of the wildfire region that exacerbated the formation of ozone downwind. Our findings suggest that a joint assimilation of NO2 column, HCHO column, and AOD can enhance our understanding of wildfire‐associated ozone chemistry and dynamics.

High-Resolution Aerosol Optical Depth Retrieval in Urban Areas Based on Sentinel-2

In this paper, an improved aerosol optical depth (AOD ) retrieval algorithm is proposed based on Sentinel-2 and AErosol RObotic NETwork (AERONET ) data. The surface reflectance for AOD retrieval was estimated from the image that had minimal aerosol contamination in a temporal window determined by AERONET data. Validation of the Sentinel-2 AOD retrievals was conducted against four Aerosol Robotic Network (AERONET ) sites located in Beijing. The results show that the Sentinel-2 AOD retrievals are highly consistent with the AERONET AOD measurements (R = 0.942), with 85.56% falling within the expected error. The mean absolute error and the root-mean-square error are 0.0688 and 0.0882, respectively. In addition, the AOD distribution map obtained by this algorithm well reflects the fine-spatial-resolution changes in AOD distribution. These results suggest that the improved high-resolution AOD retrieval algorithm is robust and has the potential advantage of retrieving high-resolution AOD over urban areas.

Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2

Abstract. Aeolian dust has significant impacts on climate, public health, infrastructure and ecosystems. Assessing dust concentrations and the impacts is challenging because the emissions depend on many environmental factors and can vary greatly with meteorological conditions. We present a data-driven aeolian dust scheme that combines machine learning components and physical equations to predict atmospheric dust concentrations and quantify the sources. The numerical scheme was trained to reproduce dust aerosol optical depth retrievals by the Infrared Atmospheric Sounding Interferometer on board the MetOp-A satellite. The input parameters included meteorological variables from the fifth-generation atmospheric reanalysis of the European Centre for Medium-Range Weather Forecasts. The trained dust scheme can be applied as an emission submodel to be used in climate and Earth system models, which is reproducibly derived from observational data so that a priori assumptions and manual parameter tuning can be largely avoided. We compared the trained emission submodel to a state-of-the-art emission parameterisation, showing that it substantially improves the representation of aeolian dust in the global atmospheric chemistry–climate model EMAC.

Review of retrieval of aerosol optical depth to estimate particle concentration and its challenges based on spatiotemporal relationships by various spectroradiometer models

This review was carried out to understand the retrieval of aerosol optical depth (AOD) datasets for estimating particle concentration and its influence on ambient air and surroundings by various models. Several studies have evaluated particle (PM10 and PM2.5) concentration profiles present in the lowest layers of the atmosphere by using AOD datasets. This study aimed at identifying consistent and precise particle estimation by various datasets retrieved by satellite‐based models for the ground‐level PM concentration. Extremities of satellite sensor data, like specific capabilities, as well as a few drawbacks are presented. Multi‐angle imaging spectroradiometer (MISR), visible infrared imaging radiometer suite (VIIRS) datasets, mixed‐effect model (MEM), and geographically weighted regression (GWR) models outperformed to estimate AOD and PM in comparison with the moderate resolution imaging spectroradiometer (MODIS) and other datasets. The improvised algorithms with higher resolution in the upcoming research would provide an even better estimation for AOD and PM.

Window-Based Filtering Aerosol Retrieval Algorithm of Fine-Scale Remote Sensing Images: A Case Using Sentinel-2 Data in Beijing Region

The satellite-based Aerosol Optical Depth (AOD) retrieval algorithms are generally needed to construct Land Surface Reflectance (LSR) database. However, errors are unavoidable due to the surface complexity, especially for the short observation period and high-resolution images, such as Sentinel-2 Multi-Spectral Instrument (MSI) data. To address this, reference day images are used instead of the LSR database. The surface is assumed to be Lambertian; however, the fact is that not all pixels meet it well. Therefore, we proposed a window-based AOD retrieval algorithm, which can ignore the unreliable/non-Lambertian pixels in a retrieval window based on two main filtering processes. Finally, using Sentinel-2 Band 1 (60 m), the AODs (120 m) of 134 reference images to 43 reference images were retrieved by this algorithm from 2017 to 2021 in Beijing region, China. The results show that the retrieved AOD with the proposed algorithm exhibits good agreement with the ground-based measured AOD (R > 0.97). The high-resolution AOD presents comparable spatial distributions to the Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm AOD (1 km) products. Moreover, the very little noise and very high spatial continuity of retrieval AOD imply that this algorithm could be ported to other algorithms as part of improving AOD quality.

Airborne HSRL-2 measurements of elevated aerosol depolarization associated with non-spherical sea salt

Airborne NASA Langley Research Center (LaRC) High Spectral Resolution Lidar-2 (HSRL-2) measurements acquired during the recent NASA Earth Venture Suborbital-3 (EVS-3) Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE) revealed elevated particulate linear depolarization associated with aerosols within the marine boundary layer. These observations were acquired off the east coast of the United States during both winter and summer 2020 and 2021 when the HSRL-2 was deployed on the NASA LaRC King Air aircraft. During 20 of 63 total flight days, particularly on days with cold air outbreaks, linear particulate depolarization at 532 nm exceeded 0.15–0.20 within the lowest several hundred meters of the atmosphere, indicating that these particles were non-spherical. Higher values of linear depolarization typically were measured at 355 nm and lower values were measured at 1,064 nm. Several lines of evidence suggest that these non-spherical particles were sea salt including aerosol extinction/backscatter ratio (“lidar ratio”) values of 20–25 sr measured at both 355 and 532 nm by the HSRL-2, higher values of particulate depolarization measured at low (< 60%) relative humidity, coincident airbornein situsize and composition measurements, and aerosol transport simulations. The elevated aerosol depolarization values were not correlated with wind speed but were correlated with salt mass fraction and effective radius of the aerosol when the relative humidity was below 60%. HSRL-2 measured median particulate extinction values of about 20 Mm−1 at 532 nm associated with these non-spherical sea salt particles and found that the aerosol optical depth (AOD) contributed by these particles remained small (0.03–0.04) but represented on average about 30%–40% of the total column AOD. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) spaceborne lidar aerosol measurements during several cold air outbreaks and CALIOP retrievals of column aerosol lidar ratio using column AOD constraints suggest that CALIOP operational aerosol algorithms can misclassify these aerosols as dusty marine rather than marine aerosols. Such misclassification leads to ∼40–50% overestimates in the assumed lidar ratio and in subsequent retrievals of aerosol optical depth and aerosol extinction.

JEDI‐Based Three‐Dimensional Ensemble‐Variational Data Assimilation System for Global Aerosol Forecasting at NCEP

AbstractA three‐dimensional ensemble‐variational global aerosol data assimilation system based on the Joint Effort for Data assimilation Integration (JEDI) was developed for the Global Ensemble Forecast System‐Aerosols (GEFS‐Aerosols) coupled with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model at the National Centers for Environmental Prediction. Aerosol mass mixing ratios in GEFS‐Aerosols were selected as control or analysis variables and were adjusted by assimilating 550 nm Aerosol Optical Depth (AOD) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments onboard the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite produced by the National Environmental Satellite, Data, and Information Service (NESDIS) at National Oceanic and Atmospheric Administration (NOAA). The original NOAA/NESDIS S‐NPP VIIRS Level 2.0 550 nm AOD retrievals were converted to JEDI Interface for Observation Data Access format. AOD forward operator and its tangent‐linear and adjoint models were implemented based on GOCART in JEDI Unified Forward Operator. A stochastically perturbed emission (SPE) approach was developed in the Common Community Physics Package‐based GEFS‐Aerosols to account for aerosol emission uncertainty. One‐month retrospective and three‐month near‐real‐time experiments consistently showed improved GEFS‐Aerosols analyses and forecasts from assimilating VIIRS 550 nm AOD retrievals against independent NASA Aqua and Terra Moderate Resolution Imaging Spectroradiometer AOD retrievals, Aerosol Robotic Network AOD, and independent AOD and aerosol analyses from NASA and European Centre for Medium‐Range Weather Forecasts. Through scaling and perturbing aerosol emissions, SPE enhanced ensemble error‐spread consistency and further improved AOD assimilation. The valid‐time‐shifting ensemble approach in a cost‐effective manner of populating background ensembles showed positive impacts on AOD assimilation.

Simultaneous assimilation of Fengyun-4A and Himawari-8 aerosol optical depth retrieval to improve air quality simulations during one storm event over East Asia

Aerosols are the main components of air pollutants, which are closely related to haze, dust storm and air pollution. In this study, an aerosol data assimilation system was developed using Gridpoint Statistical Interpolation (GSI) system to assimilate the Aerosol Optical Depth (AOD) observations from FY4 and Himawari-8 for the first time and applied in the heavy dust case over east Asia in March 2018. Three parallel experiments assimilated AOD from FY4, Himawari-8 and both the FY4 and Himawari-8 respectively and a control experiment which did not employ DA were performed. The hourly aerosol analyses and forecasts are compared with the assimilated FY-4 AOD, Himawari-8 AOD and independent AOD from Aerosol Robotic Network (AERONET). The results showed that all forms of DA experiments improved a low Bias and the RMSE reduced about 20%. The aerosol data assimilation with observations from both the FY-4 and Himawari-8 satellites substantially improved aerosol analyses and subsequent forecasts with more abundant aerosol observation information, especially over the northwest of China. This study indicates that the new generation geostationary meteorological satellites have potential to dramatically contribute to air quality forecasting.

Evaluation of MODIS combined DT and DB AOD retrievals and their association with meteorological variables over Qena, Egypt

The purpose of this study is to validate the daily Terra-MODIS level 2 combined dark target (DT) and deep blue (DB) aerosol optical depth (AOD) retrievals with a spatial resolution of 10 km against the ground-based AERONET AOD data to be used in evaluating the air pollution and impact of meteorological variables over Qena, Egypt, in 2019. The regression analysis demonstrated an accepted agreement between the MODIS and AERONET AOD data with a correlation coefficient (R) of 0.7118 and 74.22% of the collocated points fall within the expected error (EE) limits. Quality flag filtering and spatial and temporal collocation were found to have a significant impact on the regression results. Quality flag filtering increased R by 0.2091 and % within EE by 17.97, spatial collocation increased R by 0.0143 and % within EE by 1.13, and temporal collocation increased R by 0.0089 and % within EE by 4.43. By validating the MODIS AOD data seasonally and analyzing the temporal distribution of the seasonal AOD data to show the retrieval accuracy variations between seasons, it was found that the MODIS AOD observations overestimated the AERONET AOD values in all seasons, and this may be because of underestimating the surface reflectance. Perhaps the main reason for the highest overestimation in summer and autumn is the transportation of aerosols from other regions, which changes the aerosol model in Qena, making accurate aerosol-type assumptions more difficult. Therefore, this study recommends necessary improvements regarding the aerosol model selection and the surface reflectance calculations. Temperature and relative humidity were found to have a strong negative relationship with a correlation of − 0.735, and both have a moderate association with AOD with a correlation of 0.451 and − 0.356, respectively. Because Qena is not a rainy city, precipitation was found to have no correlation with the other variables.

Improved Hungarian algorithm–based task scheduling optimization strategy for remote sensing big data processing

ABSTRACT With the development of remote sensing technology and computing science, remote sensing data present typical big data characteristics. The rapid development of remote sensing big data has brought a large number of data processing tasks, which bring huge challenges to computing. Distributed computing is the primary means to process remote sensing big data, and task scheduling plays a key role in this process. This study analyzes the characteristics of batch processing of remote sensing big data. This paper uses the Hungarian algorithm as a basis for proposing a novel strategy for task assignment optimization of remote sensing big data batch workflow, called optimal sequence dynamic assignment algorithm, which is applicable to heterogeneously distributed computing environments. This strategy has two core contents: the improved Hungarian algorithm model and the multi-level optimal assignment task queue mechanism. Moreover, the strategy solves the dependency, mismatch, and computational resource idleness problems in the optimal scheduling of remote sensing batch processing tasks. The proposed strategy likewise effectively improves data processing efficiency without increasing computer hardware resources and without optimizing the computational algorithm. We experimented with the aerosol optical depth retrieval algorithm workflow using this strategy. Compared with the processing before optimization, the makespan of the proposed method was shortened by at least 20%. Compared with popular scheduling algorithm, the proposed method has evident competitiveness in acceleration effect and large-scale task scheduling.

Self-lofting of wildfire smoke in the troposphere and stratosphere: simulations and space lidar observations

Abstract. Wildfire smoke is known as a highly absorptive aerosol type in the shortwave wavelength range. The absorption of sunlight by optically thick smoke layers results in heating of the ambient air. This heating is translated into self-lofting of the smoke up to more than 1 km in altitude per day. This study aims for a detailed analysis of tropospheric and stratospheric smoke lofting rates based on simulations and observations. The main goal is to demonstrate that radiative heating of intense smoke plumes is capable of lofting them from the lower and middle free troposphere (injection heights) up to the tropopause without the need of pyrocumulonimbus (pyroCb) convection. The further subsequent ascent within the lower stratosphere (caused by self-lofting) is already well documented in the literature. Simulations of absorbed solar radiation by smoke particles and resulting heating rates, which are then converted into lofting rates, are conducted by using the ECRAD (European Centre for Medium-Range Weather Forecasts Radiation) scheme. As input parameters thermodynamic profiles from CAMS (Copernicus Atmosphere Monitoring Service) reanalysis data, aerosol profiles from ground-based lidar observations, radiosonde potential temperature profiles, CALIOP (Cloud–Aerosol Lidar with Orthogonal Polarization) aerosol measurements, and MODIS (Moderate Resolution Imaging Spectroradiometer) aerosol optical depth retrievals were used. The sensitivity analysis revealed that the lofting rate strongly depends on aerosol optical thickness (AOT), layer depth, layer height, and black carbon (BC) fraction. We also looked at the influence of different meteorological parameters such as cloudiness, relative humidity, and potential temperature gradient. To demonstrate the applicability of our self-lofting model, we compared our simulations with the lofting processes in the stratosphere observed with CALIOP after major pyroCb events (Canadian fires in 2017, Australian fires in 2019–2020). We analyzed long-term CALIOP observations of smoke layers and plumes evolving in the UTLS (upper troposphere and lower stratosphere) height region over Siberia and the adjacent Arctic Ocean during the summer season of 2019. Our results indicate that self-lofting contributed to the vertical transport of smoke. We hypothesize that the formation of a near-tropopause aerosol layer, observed with CALIOP, was the result of self-lofting processes because this is in line with the simulations. Furthermore, Raman-lidar-based aerosol typing (in Leipzig and the High Arctic) clearly indicated the dominance of smoke in the UTLS aerosol layer since August 2019, most probably also the result of smoke self-lofting.

Evaluation of aerosol–cloud interactions in E3SM using a Lagrangian framework

Abstract. A Lagrangian framework is used to evaluate aerosol–cloud interactions in the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) version 1 (E3SMv1) for measurements taken at Graciosa Island in the Azores where a U.S. Department of Energy Atmosphere Radiation Measurement (ARM) site is located. This framework uses direct measurements of cloud condensation nuclei (CCN) concentration (instead of relying on satellite retrievals of aerosol optical depth) and incorporates a suite of ground-based ARM measurements, satellite retrievals, and meteorological reanalysis products that when applied to over a 1500 trajectories provides key insights into the evolution of low-level clouds and aerosol radiative forcing that is not feasible from a traditional Eulerian analysis framework. Significantly lower concentrations (40 %) of surface CCN concentration are measured when precipitation rates in 48 h back trajectories average above 1.2 mm d−1 in the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product. The depletion of CCN concentration when precipitation rates are elevated is nearly twice as large in the ARM observations compared to E3SMv1 simulations. The model CCN concentration bias remains significant despite modifying the autoconversion and accretion rates in warm clouds. As the clouds in trajectories associated with larger surface-based CCN concentration advect away from Graciosa Island, they maintain higher values of droplet number concentrations (Nd) over multiple days in observations and E3SM simulations compared to trajectories that start with lower CCN concentrations. The response remains robust even after controlling for meteorological factors such as lower troposphere stability, the degree of cloud coupling with the surface, and island wake effects. E3SMv1 simulates a multi-day aerosol effect on clouds and a Twomey radiative effect that is within 30 % of the ARM and satellite observations. However, the mean cloud droplet concentration is more than 2–3 times larger than in the observations. While Twomey radiative effects are similar amongst autoconversion and accretion sensitivity experiments, the liquid water path and cloud fraction adjustments are positive when using a regression model as opposed to negative when using the present-day minus pre-industrial aerosol emissions approach. This result suggests that tuning the autoconversion and accretion alone is unlikely to produce the desired aerosol susceptibilities in E3SMv1.

Difference between global and regional aerosol model classifications and associated implications for spaceborne aerosol optical depth retrieval

AbstractAerosol model are generally adopted to describe the physical and optical characteristics of aerosols in different regions, and this is important for climate and environmental studies as well as satellite aerosol retrieval. However, current mainstream aerosol retrieval algorithms still lack differentiated and dynamic descriptions of aerosol models, and the aerosol optical depth (AOD) retrieval accuracy is hence largely compromised. Therefore, in this study, we aimed to investigate the difference between global and regional aerosol model classifications and the impact of these differences on satellite AOD retrieval. We developed global and regional (for China) aerosol models through the K-means cluster analysis method, and used the 6SV radiative transfer model based on these global and regional aerosol models to simulate air pollution events. The cluster analysis revealed that both global and regional aerosols can be clustered into five main aerosol types with different optical and physical parameters. The aerosol types include strongly absorbing, moderately absorbing, weakly absorbing, dust and coarse-fine mixed aerosols. These two aerosol models exhibited distinct seasonal variations. Comparing the clustering results obtained with the two models at five Aerosol Robotic Network (AERONET) sites, we found that the proportion of strongly absorbing aerosols in the global results is low. According to the China clustering results, a higher proportion of strongly absorbing aerosols occurred in autumn, winter, and early spring, and dust aerosols attained an increasing proportion in spring. Comparing the aerosol parameters with observational data, the regional aerosol model performed better than the global aerosol model. Simulations of AOD during a pollution episode and in different seasons at the Beijing-CAMS site with the 6SV radiative transfer model further confirmed that the regional aerosol model achieved a better simulation accuracy than the global model. The findings suggest that regional aerosol models can provide a useful reference for satellite remote sensing algorithm, climate change and air pollution assessment, and that the dynamic description of the aerosol model is a key parameter in satellite AOD retrieval algorithms.

Validation of the improved GOES-16 aerosol optical depth product over North America

The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) series enables retrieval of aerosol optical depth (AOD) every 5 min. However, the current version of the official GOES-16 ABI AOD product is affected by diurnally varying biases. An improved ABI AOD product based on an empirical bias correction algorithm has become available recently. This study aims to comprehensively validate this improved ABI AOD product over land and ocean. Three years of Aerosol Robotic Network (AERONET) ground-based measurements at 95 stations over North America are used for the validation. Results suggest the product performs moderately well with an overall R2 of 0.46, an MBE of 0.04, a slope of 0.97, and ∼70.0% retrievals falling within the expected error (EE) envelope. A slight overestimation is found at a majority of ground sites: ≈87% of those sites have MBE values between 0 and 0.1. The analysis shows that the retrievals perform better over water and forests, but perform poorly over urban areas, grasslands and shrublands, and is worst over mountains. Besides, the present results indicate that the combination of the retrieval and bias correction algorithms yields a systematic underestimation of AOD for coarse particles over croplands, as well as large uncertainties for smaller-size particles over shrublands. At seasonal scale, the AOD retrievals perform better in summer and autumn than in spring and winter. Despite the empirical bias correction algorithm, a systematic overestimation of 0.07 at 1700 UTC and 0.08 during 1000–1300 UTC still exist. The sampling rate of the top-2 quality AOD retrievals are 14%, 12%, 10% and 11% for the four seasons, respectively, which constitutes a significant reduction of valid data in comparison with the sampling rate of all four quality retrievals. The limited availability and accuracy of the top-2 bias-corrected ABI AOD product raises concerns about its application in air quality and energy-related applications, in particular. This study also suggests a few potential improvements to the aerosol retrieval algorithm.