Articles | Volume 18, issue 6
https://doi.org/10.5194/amt-18-1415-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-1415-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multi-layer retrieval of aerosol optical depth in the troposphere using SEVIRI data: a case study of the European continent
Maryam Pashayi
Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, 817467344, Iran
Mehran Satari
CORRESPONDING AUTHOR
Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, 817467344, Iran
Mehdi Momeni Shahraki
Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, 817467344, Iran
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M. Babadi, M. Sattari, and S. Iran Pour
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W18, 147–152, https://doi.org/10.5194/isprs-archives-XLII-4-W18-147-2019, https://doi.org/10.5194/isprs-archives-XLII-4-W18-147-2019, 2019
H. Kamangir, M. Momeni, and M. Satari
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W4, 111–116, https://doi.org/10.5194/isprs-archives-XLII-4-W4-111-2017, https://doi.org/10.5194/isprs-archives-XLII-4-W4-111-2017, 2017
Related subject area
Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Ground-based contrail observations: comparisons with reanalysis weather data and contrail model simulations
Satellite Aerosol Composition Retrieval from a combination of three different Instruments: Information content analysis
Towards Gridded Nighttime Aerosol Optical Thickness Retrievals Using VIIRS Day/Night Band Observations Over Regions with Artificial Light Sources
Retrieval of stratospheric aerosol extinction coefficients from sun-normalized Ozone Mapper and Profiler Suite Limb Profiler (OMPS-LP) measurements
Optical Properties of North Atlantic Aerosols Through a compact dual-wavelength depolarization Lidar Observations
Total column optical depths retrieved from CALIPSO lidar ocean surface backscatter
ACDL/DQ-1 Calibration Algorithms. Part I: Nighttime 532 nm Polarization and High-Spectral-Resolution Channel
ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications
Post-process correction improves the accuracy of satellite PM2.5 retrievals
Increasing aerosol optical depth spatial and temporal availability by merging datasets from geostationary and sun-synchronous satellites
Synergy of active and passive airborne observations for heating rates calculation during the AEROCLO-SA field campaign in Namibia
Multi-angle aerosol optical depth retrieval method based on improved surface reflectance
Comparison of diurnal aerosol products retrieved from combinations of micro-pulse lidar and sun photometer observations over the KAUST observation site
First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia
Aerosol optical depth data fusion with Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT-2) instruments GEMS, AMI, and GOCI-II: statistical and deep neural network methods
Stratospheric aerosol characteristics from SCIAMACHY limb observations: two-parameter retrieval
Retrieval and analysis of the composition of an aerosol mixture through Mie–Raman–fluorescence lidar observations
Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements
Transport of the Hunga volcanic aerosols inferred from Himawari-8/9 limb measurements
A near-global multiyear climate data record of the fine-mode and coarse-mode components of atmospheric pure dust
Innovative aerosol hygroscopic growth study from Mie–Raman–fluorescence lidar and microwave radiometer synergy
Evaluation of calibration performance of a low-cost particulate matter sensor using collocated and distant NO2
Geostationary aerosol retrievals of extreme biomass burning plumes during the 2019–2020 Australian bushfires
Multi-wavelength dataset of aerosol extinction profiles retrieved from GOMOS stellar occultation measurements
Deep-Pathfinder: a boundary layer height detection algorithm based on image segmentation
An iterative algorithm to simultaneously retrieve aerosol extinction and effective radius profiles using CALIOP
Cloud detection from multi-angular polarimetric satellite measurements using a neural network ensemble approach
Producing aerosol size distributions consistent with optical particle counters measurements using space-based measurements of aerosol extinction coefficient
Retrieving UV–Vis spectral single-scattering albedo of absorbing aerosols above clouds from synergy of ORACLES airborne and A-train sensors
Characterization of stratospheric particle size distribution uncertainties using SAGE II and SAGE III/ISS extinction spectra
Parameterizing spectral surface reflectance relationships for the Dark Target aerosol algorithm applied to a geostationary imager
Aerosol and cloud data processing and optical property retrieval algorithms for the spaceborne ACDL/DQ-1
Derivation of depolarization ratios of aerosol fluorescence and water vapor Raman backscatters from lidar measurements
Long-term aerosol particle depolarization ratio measurements with HALO Photonics Doppler lidar
HETEAC-Flex: an optimal estimation method for aerosol typing based on lidar-derived intensive optical properties
MAGARA: a Multi-Angle Geostationary Aerosol Retrieval Algorithm
Multi-section reference value for the analysis of horizontally scanning aerosol lidar observations
Retrieval of aerosol optical depth over the Arctic cryosphere during spring and summer using satellite observations
Quantifying particulate matter optical properties and flow rate in industrial stack plumes from the PRISMA hyperspectral imager
Aerosol retrieval over snow using the RemoTAP algorithm
Combined sun-photometer–lidar inversion: lessons learned during the EARLINET/ACTRIS COVID-19 campaign
Simultaneous retrieval of aerosol and ocean properties from PACE HARP2 with uncertainty assessment using cascading neural network radiative transfer models
Linear polarization signatures of atmospheric dust with the SolPol direct-sun polarimeter
Retrieval of aerosol properties from zenith sky radiance measurements
An ensemble method for improving the estimation of planetary boundary layer height from radiosonde data
Detection and analysis of Lhù'ààn Mân' (Kluane Lake) dust plumes using passive and active ground-based remote sensing supported by physical surface measurements
Cloud top heights and aerosol layer properties from EarthCARE lidar observations: the A-CTH and A-ALD products
Influence of electromagnetic interference on the evaluation of lidar-derived aerosol properties from Ny-Ålesund, Svalbard
Global 3-D distribution of aerosol composition by synergistic use of CALIOP and MODIS observations
Aerosol optical depth retrieval from the EarthCARE Multi-Spectral Imager: the M-AOT product
Jade Low, Roger Teoh, Joel Ponsonby, Edward Gryspeerdt, Marc Shapiro, and Marc E. J. Stettler
Atmos. Meas. Tech., 18, 37–56, https://doi.org/10.5194/amt-18-37-2025, https://doi.org/10.5194/amt-18-37-2025, 2025
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The radiative forcing due to contrails is of the same order of magnitude as aviation CO2 emissions but has a higher uncertainty. Observations are vital to improve our understanding of the contrail lifecycle, improve models, and measure the effect of mitigation action. Here, we use ground-based cameras combined with flight telemetry to track visible contrails and measure their lifetime and width. We evaluate model predictions and demonstrate the capability of this approach.
Ulrike Stöffelmair, Thomas Popp, Marco Vountas, and Hartmut Bösch
EGUsphere, https://doi.org/10.5194/egusphere-2024-2800, https://doi.org/10.5194/egusphere-2024-2800, 2024
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The aerosol composition has a large influence on the climate system. This study uses realistic simulated scenarios to look at the information content of the combination of three satellite-based instruments (SLSTR, IASI and GOME-2). It shows that it is possible to retrieve 6 to 15 different aerosol components in addition to the AOD and different surface parameter. The results will be used for the development of a synergistic multi-sensor retrieval algorithms.
Jianglong Zhang, Jeffrey S. Reid, Blake Sorenson, Steven D. Miller, Miguel O. Román, Zhuosen Wang, Robert J. D. Spurr, Shawn Jaker, Thomas F. Eck, and Juli I. Rubin
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-181, https://doi.org/10.5194/amt-2024-181, 2024
Revised manuscript accepted for AMT
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Using observations from the Visible Infrared Imaging Radiometer Suite Day-Night Band, we developed a method for constructing gridded nighttime aerosol optical thickness (AOT) data based on the spatial derivative of measured top-of-atmosphere attenuated upwelling artificial lights at night. The gridded nighttime AOT retrievals, compared against Aerosol Robotic Network data, show reasonable skill levels for potential data assimilation, air quality and climate studies of significant events.
Alexei Rozanov, Christine Pohl, Carlo Arosio, Adam Bourassa, Klaus Bramstedt, Elizaveta Malinina, Landon Rieger, and John P. Burrows
Atmos. Meas. Tech., 17, 6677–6695, https://doi.org/10.5194/amt-17-6677-2024, https://doi.org/10.5194/amt-17-6677-2024, 2024
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We developed a new algorithm to retrieve vertical distributions of aerosol extinction coefficients in the stratosphere. The algorithm is applied to measurements of scattered solar light from the spaceborne OMPS-LP (Ozone Mapper and Profiler Suite Limb Profiler) instrument. The retrieval results are compared to data from other spaceborne instruments and used to investigate the evolution of the aerosol plume following the eruption of the Hunga Tonga–Hunga Ha'apai volcano in January 2022.
Yenny González, María F. Sánchez-Barrero, Ioana Popovici, África Barreto, Stephane Victori, Ellsworth J. Welton, Rosa D. García, Pablo G. Sicilia, Fernando A. Almansa, Carlos Torres, and Philippe Goloub
EGUsphere, https://doi.org/10.5194/egusphere-2024-2727, https://doi.org/10.5194/egusphere-2024-2727, 2024
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We characterize the optical properties of various aerosols using a compact dual-wavelength depolarization lidar (CIMEL CE376) at 532 and 808 nm. Through a modified two-wavelength Klett inversion method, we assess the vertical distribution and temporal evolution of Saharan dust, volcanic aerosols, and wildfire smoke in the subtropical North Atlantic from August 2021 to August 2023. The study confirms the CE376 lidar's effectiveness in monitoring and characterizing atmospheric aerosols over time.
Robert A. Ryan, Mark A. Vaughan, Sharon D. Rodier, Jason L. Tackett, John A. Reagan, Richard A. Ferrare, Johnathan W. Hair, John A. Smith, and Brian J. Getzewich
Atmos. Meas. Tech., 17, 6517–6545, https://doi.org/10.5194/amt-17-6517-2024, https://doi.org/10.5194/amt-17-6517-2024, 2024
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We introduce Ocean Derived Column Optical Depth (ODCOD), a new way to estimate column optical depths using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements from the ocean surface. ODCOD estimates include contributions from particulates in the full column, which CALIOP estimates do not, making it a complement measurement to CALIOP’s standard estimates. We find that ODCOD compares well with other established data sets in the daytime but tends to estimate higher at night.
Fanqian Meng, Junwu Tang, Guangyao Dai, Wenrui Long, Kangwen Sun, Zhiyu Zhang, Xiaoquan Song, Jiqiao Liu, Weibiao Chen, and Songhua Wu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-179, https://doi.org/10.5194/amt-2024-179, 2024
Revised manuscript accepted for AMT
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This paper presents a comprehensive calibration procedure for the first spaceborne high-spectral-resolution lidar with an iodine vapor absorption filter ACDL on board DQ-1 by utilizing nighttime 532 nm multi-channel data. And analyzed the error sources of the multi-channel calibration coefficients and assessed the results. The results shows that the ACDL polarization and HSRL channel calibration is reliable and operates within the expected error range of approximately 5 %.
Annachiara Bellini, Henri Diémoz, Luca Di Liberto, Gian Paolo Gobbi, Alessandro Bracci, Ferdinando Pasqualini, and Francesca Barnaba
Atmos. Meas. Tech., 17, 6119–6144, https://doi.org/10.5194/amt-17-6119-2024, https://doi.org/10.5194/amt-17-6119-2024, 2024
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We provide a comprehensive overview of the Italian Automated LIdar-CEilometer network, ALICENET, describing its infrastructure, aerosol retrievals, and main applications. The supplement covers data-processing details. We include examples of output products, comparisons with independent data, and examples of the network capability to provide near-real-time aerosol fields over Italy. ALICENET is expected to benefit the sectors of air quality, radiative budget/solar energy, and aviation safety.
Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, and Antti Lipponen
Atmos. Meas. Tech., 17, 5747–5764, https://doi.org/10.5194/amt-17-5747-2024, https://doi.org/10.5194/amt-17-5747-2024, 2024
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This study focuses on improving the accuracy of satellite-based PM2.5 retrieval, crucial for monitoring air quality and its impact on health. It employs machine learning to correct the AOD-to-PM2.5 conversion ratio using various data sources. The approach produces high-resolution PM2.5 estimates with improved accuracy. The method is flexible and can incorporate additional training data from different sources, making it a valuable tool for air quality monitoring and epidemiological studies.
Pawan Gupta, Robert C. Levy, Shana Mattoo, Lorraine A. Remer, Zhaohui Zhang, Virginia Sawyer, Jennifer Wei, Sally Zhao, Min Oo, V. Praju Kiliyanpilakkil, and Xiaohua Pan
Atmos. Meas. Tech., 17, 5455–5476, https://doi.org/10.5194/amt-17-5455-2024, https://doi.org/10.5194/amt-17-5455-2024, 2024
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In this study, for the first time, we combined aerosol data from six satellites using a unified algorithm. The global datasets are generated at a high spatial resolution of about 25 km with an interval of 30 min. The new datasets are compared against ground truth and verified. They will be useful for various applications such as air quality monitoring, climate research, pollution diurnal variability, long-range smoke and dust transport, and evaluation of regional and global models.
Mégane Ventura, Fabien Waquet, Isabelle Chiapello, Gérard Brogniez, Frédéric Parol, Frédérique Auriol, Rodrigue Loisil, Cyril Delegove, Luc Blarel, Oleg Dubovik, Marc Mallet, Cyrille Flamant, and Paola Formenti
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-121, https://doi.org/10.5194/amt-2024-121, 2024
Revised manuscript accepted for AMT
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Biomass burning aerosols (BBA) from Central Africa, are transported above stratocumulus clouds. The absorption of solar energy by aerosols induce warming, altering the clouds dynamics. We developed an approach that combines polarimeter and lidar to quantify it. This methodology is assessed during the AEROCLO-SA campaign. To validate it, we used flux measurements acquired during aircraft loop descents. Major perspective is the generalization of this method to the global level.
Lijuan Chen, Ren Wang, Ying Fei, Peng Fang, Yong Zha, and Haishan Chen
Atmos. Meas. Tech., 17, 4411–4424, https://doi.org/10.5194/amt-17-4411-2024, https://doi.org/10.5194/amt-17-4411-2024, 2024
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This study explores the problems of surface reflectance estimation from previous MISR satellite remote sensing images and develops an error correction model to obtain a higher-precision aerosol optical depth (AOD) product. High-accuracy AOD is important not only for the daily monitoring of air pollution but also for the study of energy exchange between land and atmosphere. This will help further improve the retrieval accuracy of multi-angle AOD on large spatial scales and for long time series.
Anton Lopatin, Oleg Dubovik, Georgiy Stenchikov, Ellsworth J. Welton, Illia Shevchenko, David Fuertes, Marcos Herreras-Giralda, Tatsiana Lapyonok, and Alexander Smirnov
Atmos. Meas. Tech., 17, 4445–4470, https://doi.org/10.5194/amt-17-4445-2024, https://doi.org/10.5194/amt-17-4445-2024, 2024
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We compare aerosol properties over the King Abdullah University of Science and Technology campus using Generalized Retrieval of Aerosol and Surface Properties (GRASP) and the Micro-Pulse Lidar Network (MPLNET). We focus on the impact of different aerosol retrieval assumptions on daytime and nighttime retrievals and analyze seasonal variability in aerosol properties, aiding in understanding aerosol behavior and improving retrieval. Our work has implications for climate and public health.
Yeseul Cho, Jhoon Kim, Sujung Go, Mijin Kim, Seoyoung Lee, Minseok Kim, Heesung Chong, Won-Jin Lee, Dong-Won Lee, Omar Torres, and Sang Seo Park
Atmos. Meas. Tech., 17, 4369–4390, https://doi.org/10.5194/amt-17-4369-2024, https://doi.org/10.5194/amt-17-4369-2024, 2024
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Aerosol optical properties have been provided by the Geostationary Environment Monitoring Spectrometer (GEMS), the world’s first geostationary-Earth-orbit (GEO) satellite instrument designed for atmospheric environmental monitoring. This study describes improvements made to the GEMS aerosol retrieval algorithm (AERAOD) and presents its validation results. These enhancements aim to provide more accurate and reliable aerosol-monitoring results for Asia.
Minseok Kim, Jhoon Kim, Hyunkwang Lim, Seoyoung Lee, Yeseul Cho, Yun-Gon Lee, Sujung Go, and Kyunghwa Lee
Atmos. Meas. Tech., 17, 4317–4335, https://doi.org/10.5194/amt-17-4317-2024, https://doi.org/10.5194/amt-17-4317-2024, 2024
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Information about aerosol loading in the atmosphere can be collected from various satellite instruments. Aerosol products from various satellite instruments have their own error characteristics. This study statistically merged aerosol optical depth datasets from multiple instruments aboard geostationary satellites considering uncertainties. Also, a deep neural network technique is adopted for aerosol data merging.
Christine Pohl, Felix Wrana, Alexei Rozanov, Terry Deshler, Elizaveta Malinina, Christian von Savigny, Landon A. Rieger, Adam E. Bourassa, and John P. Burrows
Atmos. Meas. Tech., 17, 4153–4181, https://doi.org/10.5194/amt-17-4153-2024, https://doi.org/10.5194/amt-17-4153-2024, 2024
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Knowledge of stratospheric aerosol characteristics is important for understanding chemical and climate aerosol feedbacks. Two particle size distribution parameters, the aerosol extinction coefficient and the effective radius, are obtained from SCIAMACHY limb observations. The aerosol characteristics show good agreement with independent data sets from balloon-borne and satellite observations. This data set expands the limited knowledge of stratospheric aerosol characteristics.
Igor Veselovskii, Boris Barchunov, Qiaoyun Hu, Philippe Goloub, Thierry Podvin, Mikhail Korenskii, Gaël Dubois, William Boissiere, and Nikita Kasianik
Atmos. Meas. Tech., 17, 4137–4152, https://doi.org/10.5194/amt-17-4137-2024, https://doi.org/10.5194/amt-17-4137-2024, 2024
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The paper presents a new method that categorizes atmospheric aerosols by analyzing their optical properties with a Mie–Raman–fluorescence lidar. The research specifically looks into understanding the presence of smoke, urban, and dust aerosols in the mixtures identified by this lidar. The reliability of the results is evaluated using the Monte Carlo technique. The effectiveness of this approach is successfully demonstrated through testing in ATOLL, an observatory influenced by diverse aerosols.
Michael D. Himes, Ghassan Taha, Daniel Kahn, Tong Zhu, and Natalya A. Kramarova
EGUsphere, https://doi.org/10.5194/egusphere-2024-1823, https://doi.org/10.5194/egusphere-2024-1823, 2024
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The Ozone Mapping and Profiler Suite's Limb Profiler (OMPS LP) provides near-global coverage and information about how aerosols from volcanic eruptions and major wildfires are vertically distributed through the atmosphere. We developed a machine learning method to characterize aerosols using OMPS LP measurements about 60 times faster than the current approach. This near-real-time characterization can be used to ensure aviation flight paths avoid dangerous conditions.
Fred Prata
Atmos. Meas. Tech., 17, 3751–3764, https://doi.org/10.5194/amt-17-3751-2024, https://doi.org/10.5194/amt-17-3751-2024, 2024
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Geostationary satellite data have been used to measure the stratospheric aerosols from the explosive Hunga volcanic eruption by using the data in a novel way. The onboard imager views part of the Earth's limb and data from this region were analysed to generate vertical cross-sections of aerosols high in the atmosphere. The analyses show the hemispheric spread of the aerosols and their vertical structure in layers from 22–28 km in the stratosphere.
Emmanouil Proestakis, Antonis Gkikas, Thanasis Georgiou, Anna Kampouri, Eleni Drakaki, Claire L. Ryder, Franco Marenco, Eleni Marinou, and Vassilis Amiridis
Atmos. Meas. Tech., 17, 3625–3667, https://doi.org/10.5194/amt-17-3625-2024, https://doi.org/10.5194/amt-17-3625-2024, 2024
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A new four-dimensional, multiyear, and near-global climate data record of the fine-mode (submicrometer diameter) and coarse-mode (supermicrometer diameter) components of atmospheric pure dust is presented. The dataset 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 datasets, and air quality.
Robin Miri, Olivier Pujol, Qiaoyun Hu, Philippe Goloub, Igor Veselovskii, Thierry Podvin, and Fabrice Ducos
Atmos. Meas. Tech., 17, 3367–3375, https://doi.org/10.5194/amt-17-3367-2024, https://doi.org/10.5194/amt-17-3367-2024, 2024
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This paper focuses on the use of fluorescence to study aerosols with lidar. An innovative method for aerosol hygroscopic growth study using fluorescence is presented. The paper presents case studies to showcase the effectiveness and potential of the proposed approach. These advancements will contribute to better understanding the interactions between aerosols and water vapor, with future work expected to be dedicated to aerosol–cloud interaction.
Kabseok Ko, Seokheon Cho, and Ramesh R. Rao
Atmos. Meas. Tech., 17, 3303–3322, https://doi.org/10.5194/amt-17-3303-2024, https://doi.org/10.5194/amt-17-3303-2024, 2024
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In our study, we examined how NO2, temperature, and relative humidity influence the calibration of PurpleAir PA-II sensors. We found that incorporating NO2 data from collocated reliable instruments enhances PM2.5 calibration performance. Due to the impracticality of collocating reliable NO2 instruments with sensors, we suggest using distant NO2 data for calibration. We demonstrated that performance improves when distant NO2 correlates highly with collocated NO2 measurements.
Daniel J. V. Robbins, Caroline A. Poulsen, Steven T. Siems, Simon R. Proud, Andrew T. Prata, Roy G. Grainger, and Adam C. Povey
Atmos. Meas. Tech., 17, 3279–3302, https://doi.org/10.5194/amt-17-3279-2024, https://doi.org/10.5194/amt-17-3279-2024, 2024
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Extreme wildfire events are becoming more common with climate change. The smoke plumes associated with these wildfires are not captured by current operational satellite products due to their high optical thickness. We have developed a novel aerosol retrieval for the Advanced Himawari Imager to study these plumes. We find very high values of optical thickness not observed in other operational satellite products, suggesting these plumes have been missed in previous studies.
Viktoria F. Sofieva, Monika Szelag, Johanna Tamminen, Didier Fussen, Christine Bingen, Filip Vanhellemont, Nina Mateshvili, Alexei Rozanov, and Christine Pohl
Atmos. Meas. Tech., 17, 3085–3101, https://doi.org/10.5194/amt-17-3085-2024, https://doi.org/10.5194/amt-17-3085-2024, 2024
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We have developed the new multi-wavelength dataset of aerosol extinction profiles, which are retrieved from the averaged transmittance spectra by the Global Ozone Monitoring by Occultation of Stars instrument aboard Envisat. The retrieved aerosol extinction profiles are provided in the altitude range 10–40 km at 400, 440, 452, 470, 500, 525, 550, 672 and 750 nm for the period 2002–2012. FMI-GOMOSaero aerosol profiles have improved quality; they are in good agreement with other datasets.
Jasper S. Wijnands, Arnoud Apituley, Diego Alves Gouveia, and Jan Willem Noteboom
Atmos. Meas. Tech., 17, 3029–3045, https://doi.org/10.5194/amt-17-3029-2024, https://doi.org/10.5194/amt-17-3029-2024, 2024
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The mixing of air in the lower atmosphere influences the concentration of air pollutants and greenhouse gases. Our study developed a new method, Deep-Pathfinder, to estimate mixing layer height. Deep-Pathfinder analyses imagery with aerosol observations using artificial intelligence techniques for computer vision. Compared to existing methods, it improves temporal consistency and resolution and can be used in real time, which is valuable for aviation, forecasting, and air quality monitoring.
Liang Chang, Jing Li, Jingjing Ren, Changrui Xiong, and Lu Zhang
Atmos. Meas. Tech., 17, 2637–2648, https://doi.org/10.5194/amt-17-2637-2024, https://doi.org/10.5194/amt-17-2637-2024, 2024
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We described a modified lidar inversion algorithm to retrieve aerosol extinction and size distribution simultaneously from two-wavelength elastic lidar measurements. Its major advantage is that the lidar ratio of each layer is determined iteratively by a lidar ratio–Ångström exponent lookup table. The algorithm was applied to the Raman lidar and CALIOP measurements. The retrieved results by our method are in good agreement with those achieved by Raman method.
Zihao Yuan, Guangliang Fu, Bastiaan van Diedenhoven, Hai Xiang Lin, Jan Willem Erisman, and Otto P. Hasekamp
Atmos. Meas. Tech., 17, 2595–2610, https://doi.org/10.5194/amt-17-2595-2024, https://doi.org/10.5194/amt-17-2595-2024, 2024
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Currently, aerosol properties from spaceborne multi-angle polarimeter (MAP) instruments can only be retrieved in cloud-free areas or in areas where an aerosol layer is located above a cloud. Therefore, it is important to be able to identify cloud-free pixels for which an aerosol retrieval algorithm can provide meaningful output. The developed neural network cloud screening demonstrates that cloud masking for MAP aerosol retrieval can be based on the MAP measurements themselves.
Nicholas Ernest, Larry W. Thomason, and Terry Deshler
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-62, https://doi.org/10.5194/amt-2024-62, 2024
Revised manuscript accepted for AMT
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We use balloon-borne measurements of aerosol size distribution (ASD) made by the University of Wyoming (UW) to derive distributions which are representative of the ASDs that underlie measurements made by the Stratospheric Aerosol and Gas Experiment II (SAGE II). A simple single mode log-normal distribution has in the past been used to derive ASD from SAGE II data; here we derive bimodal log-normal distributions. Reproducing median aerosol properties, however sometimes with wide variance.
Hiren T. Jethva, Omar Torres, Richard A. Ferrare, Sharon P. Burton, Anthony L. Cook, David B. Harper, Chris A. Hostetler, Jens Redemann, Vinay Kayetha, Samuel LeBlanc, Kristina Pistone, Logan Mitchell, and Connor J. Flynn
Atmos. Meas. Tech., 17, 2335–2366, https://doi.org/10.5194/amt-17-2335-2024, https://doi.org/10.5194/amt-17-2335-2024, 2024
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We introduce a novel synergy algorithm applied to ORALCES airborne measurements of above-cloud aerosol optical depth and UV–Vis satellite observations from OMI and MODIS to retrieve spectral aerosol single-scattering albedo of lofted layers of carbonaceous smoke aerosols over clouds. The development of the proposed aerosol–cloud algorithm implies a possible synergy of CALIOP and OMI–MODIS passive sensors to deduce a global product of AOD and SSA of absorbing aerosols above clouds.
Travis N. Knepp, Mahesh Kovilakam, Larry Thomason, and Stephen J. Miller
Atmos. Meas. Tech., 17, 2025–2054, https://doi.org/10.5194/amt-17-2025-2024, https://doi.org/10.5194/amt-17-2025-2024, 2024
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An algorithm is presented to derive a new SAGE III/ISS (Stratospheric Aerosol and Gas Experiment III on the International Space Station) Level-2 product: the size distribution of stratospheric particles. This is a significant improvement over previous techniques in that we now provide uncertainty estimates for all inferred parameters. We also evaluated the stability of this method in retrieving bimodal distribution parameters. We present a special application to the 2022 eruption of Hunga Tonga.
Mijin Kim, Robert C. Levy, Lorraine A. Remer, Shana Mattoo, and Pawan Gupta
Atmos. Meas. Tech., 17, 1913–1939, https://doi.org/10.5194/amt-17-1913-2024, https://doi.org/10.5194/amt-17-1913-2024, 2024
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The study focused on evaluating and modifying the surface reflectance parameterization (SRP) of the Dark Target (DT) algorithm for geostationary observation. When using the DT SRP with the ABIs sensor on GOES-R, artificial diurnal signatures were present in AOD retrieval. To overcome this issue, a new SRP was developed, incorporating solar zenith angle and land cover type. The revised SRP resulted in improved AOD retrieval, demonstrating reduced bias around local noon.
Guangyao Dai, Songhua Wu, Wenrui Long, Jiqiao Liu, Yuan Xie, Kangwen Sun, Fanqian Meng, Xiaoquan Song, Zhongwei Huang, and Weibiao Chen
Atmos. Meas. Tech., 17, 1879–1890, https://doi.org/10.5194/amt-17-1879-2024, https://doi.org/10.5194/amt-17-1879-2024, 2024
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An overview is given of the main algorithms applied to derive the aerosol and cloud optical property product of the Aerosol and Carbon Detection Lidar (ACDL), which is capable of globally profiling aerosol and cloud optical properties with high accuracy. The paper demonstrates the observational capabilities of ACDL for aerosol and cloud vertical structure and global distribution through two optical property product measurement cases and global aerosol optical depth profile observations.
Igor Veselovskii, Qiaoyun Hu, Philippe Goloub, Thierry Podvin, William Boissiere, Mikhail Korenskiy, Nikita Kasianik, Sergey Khaykyn, and Robin Miri
Atmos. Meas. Tech., 17, 1023–1036, https://doi.org/10.5194/amt-17-1023-2024, https://doi.org/10.5194/amt-17-1023-2024, 2024
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Measurements of transported smoke layers were performed with a lidar in Lille and a five-channel fluorescence lidar in Moscow. Results show the peak of fluorescence in the boundary layer is at 438 nm, while in the smoke layer it shifts to longer wavelengths. The fluorescence depolarization is 45 % to 55 %. The depolarization ratio of the water vapor channel is low (2 ± 0.5 %) in the absence of fluorescence and can be used to evaluate the contribution of fluorescence to water vapor signal.
Viet Le, Hannah Lobo, Ewan J. O'Connor, and Ville Vakkari
Atmos. Meas. Tech., 17, 921–941, https://doi.org/10.5194/amt-17-921-2024, https://doi.org/10.5194/amt-17-921-2024, 2024
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This study offers a long-term overview of aerosol particle depolarization ratio at the wavelength of 1565 nm obtained from vertical profiling measurements by Halo Doppler lidars during 4 years at four different locations across Finland. Our observations support the long-term usage of Halo Doppler lidar depolarization ratio such as the detection of aerosols that may pose a safety risk for aviation. Long-range Saharan dust transport and pollen transport are also showcased here.
Athena Augusta Floutsi, Holger Baars, and Ulla Wandinger
Atmos. Meas. Tech., 17, 693–714, https://doi.org/10.5194/amt-17-693-2024, https://doi.org/10.5194/amt-17-693-2024, 2024
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We introduce an aerosol-typing scheme (HETEAC-Flex) based on lidar-derived intensive optical properties and applicable to ground-based and spaceborne lidars. HETEAC-Flex utilizes the optimal estimation method and enables the identification of up to four different aerosol components, as well as the determination of their contribution to the aerosol mixture in terms of relative volume. The aerosol components represent common aerosol types such as dust, sea salt, smoke and pollution.
James A. Limbacher, Ralph A. Kahn, Mariel D. Friberg, Jaehwa Lee, Tyler Summers, and Hai Zhang
Atmos. Meas. Tech., 17, 471–498, https://doi.org/10.5194/amt-17-471-2024, https://doi.org/10.5194/amt-17-471-2024, 2024
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We present the new Multi-Angle Geostationary Aerosol Retrieval Algorithm (MAGARA) that fuses observations from GOES-16 and GOES-17 to retrieve information about aerosol loading (at 10–15 min cadence) and aerosol particle properties (daily), all at pixel-level resolution. We present MAGARA results for three case studies: the 2018 California Camp Fire, the 2019 Williams Flats Fire, and the 2019 Kincade Fire. We also compare MAGARA aerosol loading and particle properties with AERONET.
Juseon Shin, Gahyeong Kim, Dukhyeon Kim, Matthias Tesche, Gahyeon Park, and Youngmin Noh
Atmos. Meas. Tech., 17, 397–406, https://doi.org/10.5194/amt-17-397-2024, https://doi.org/10.5194/amt-17-397-2024, 2024
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We introduce the multi-section method, a novel approach for stable extinction coefficient retrievals in horizontally scanning aerosol lidar measurements, in this study. Our method effectively removes signal–noise-induced irregular peaks and derives a reference extinction coefficient, αref, from multiple scans, resulting in a strong correlation (>0.74) with PM2.5 mass concentrations. Case studies demonstrate its utility in retrieving spatio-temporal aerosol distributions and PM2.5 concentrations.
Basudev Swain, Marco Vountas, Adrien Deroubaix, Luca Lelli, Yanick Ziegler, Soheila Jafariserajehlou, Sachin S. Gunthe, Andreas Herber, Christoph Ritter, Hartmut Bösch, and John P. Burrows
Atmos. Meas. Tech., 17, 359–375, https://doi.org/10.5194/amt-17-359-2024, https://doi.org/10.5194/amt-17-359-2024, 2024
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Aerosols are suspensions of particles dispersed in the air. In this study, we use a novel retrieval of satellite data to investigate an optical property of aerosols, the aerosol optical depth, in the high Arctic to assess their direct and indirect roles in climate change. This study demonstrates that the presented approach shows good quality and very promising potential.
Gabriel Calassou, Pierre-Yves Foucher, and Jean-François Léon
Atmos. Meas. Tech., 17, 57–71, https://doi.org/10.5194/amt-17-57-2024, https://doi.org/10.5194/amt-17-57-2024, 2024
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We propose analyzing the aerosol composition of plumes emitted by different industrial stacks using PRISMA satellite hyperspectral observations. Three industrial sites have been observed: a coal-fired power plant in South Africa, a steel plant in China, and gas flaring at an oil extraction site in Algeria. Aerosol optical thickness and particle radius are retrieved within the plumes. The mass flow rate of particulate matter is estimated in the plume using the integrated mass enhancement method.
Zihan Zhang, Guangliang Fu, and Otto Hasekamp
Atmos. Meas. Tech., 16, 6051–6063, https://doi.org/10.5194/amt-16-6051-2023, https://doi.org/10.5194/amt-16-6051-2023, 2023
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In order to conduct accurate aerosol retrieval over snow, the Remote Sensing of Trace Gases and Aerosol Products (RemoTAP) algorithm is extended with a bi-directional reflection distribution function for snow surfaces. The experiments with both synthetic and real data show that the extended RemoTAP maintains capability for snow-free pixels and has obvious advantages in accuracy and the fraction of successful retrievals for retrieval over snow, especially over surfaces with snow cover > 75 %.
Alexandra Tsekeri, Anna Gialitaki, Marco Di Paolantonio, Davide Dionisi, Gian Luigi Liberti, Alnilam Fernandes, Artur Szkop, Aleksander Pietruczuk, Daniel Pérez-Ramírez, Maria J. Granados Muñoz, Juan Luis Guerrero-Rascado, Lucas Alados-Arboledas, Diego Bermejo Pantaleón, Juan Antonio Bravo-Aranda, Anna Kampouri, Eleni Marinou, Vassilis Amiridis, Michael Sicard, Adolfo Comerón, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Salvatore Romano, Maria Rita Perrone, Xiaoxia Shang, Mika Komppula, Rodanthi-Elisavet Mamouri, Argyro Nisantzi, Diofantos Hadjimitsis, Francisco Navas-Guzmán, Alexander Haefele, Dominika Szczepanik, Artur Tomczak, Iwona S. Stachlewska, Livio Belegante, Doina Nicolae, Kalliopi Artemis Voudouri, Dimitris Balis, Athena A. Floutsi, Holger Baars, Linda Miladi, Nicolas Pascal, Oleg Dubovik, and Anton Lopatin
Atmos. Meas. Tech., 16, 6025–6050, https://doi.org/10.5194/amt-16-6025-2023, https://doi.org/10.5194/amt-16-6025-2023, 2023
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EARLINET/ACTRIS organized an intensive observational campaign in May 2020, with the objective of monitoring the atmospheric state over Europe during the COVID-19 lockdown and relaxation period. The work presented herein focuses on deriving a common methodology for applying a synergistic retrieval that utilizes the network's ground-based passive and active remote sensing measurements and deriving the aerosols from anthropogenic activities over Europe.
Meng Gao, Bryan A. Franz, Peng-Wang Zhai, Kirk Knobelspiesse, Andrew M. Sayer, Xiaoguang Xu, J. Vanderlei Martins, Brian Cairns, Patricia Castellanos, Guangliang Fu, Neranga Hannadige, Otto Hasekamp, Yongxiang Hu, Amir Ibrahim, Frederick Patt, Anin Puthukkudy, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 5863–5881, https://doi.org/10.5194/amt-16-5863-2023, https://doi.org/10.5194/amt-16-5863-2023, 2023
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This study evaluated the retrievability and uncertainty of aerosol and ocean properties from PACE's HARP2 instrument using enhanced neural network models with the FastMAPOL algorithm. A cascading retrieval method is developed to improve retrieval performance. A global set of simulated HARP2 data is generated and used for uncertainty evaluations. The performance assessment demonstrates that the FastMAPOL algorithm is a viable approach for operational application to HARP2 data after PACE launch.
Vasiliki Daskalopoulou, Panagiotis I. Raptis, Alexandra Tsekeri, Vassilis Amiridis, Stelios Kazadzis, Zbigniew Ulanowski, Vassilis Charmandaris, Konstantinos Tassis, and William Martin
Atmos. Meas. Tech., 16, 4529–4550, https://doi.org/10.5194/amt-16-4529-2023, https://doi.org/10.5194/amt-16-4529-2023, 2023
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Atmospheric dust particles may present a preferential alignment due to their shape on long range transport. Since dust is abundant and plays a key role to global climate, the elusive observation of orientation will be a game changer to existing measurement techniques and the representation of particles in climate models. We utilize a specifically designed instrument, SolPol, and target the Sun from the ground for large polarization values under dusty conditions, a clear sign of orientation.
Sara Herrero-Anta, Roberto Román, David Mateos, Ramiro González, Juan Carlos Antuña-Sánchez, Marcos Herreras-Giralda, Antonio Fernando Almansa, Daniel González-Fernández, Celia Herrero del Barrio, Carlos Toledano, Victoria E. Cachorro, and Ángel M. de Frutos
Atmos. Meas. Tech., 16, 4423–4443, https://doi.org/10.5194/amt-16-4423-2023, https://doi.org/10.5194/amt-16-4423-2023, 2023
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This paper shows the potential of a simple radiometer like the ZEN-R52 as a possible alternative for aerosol property retrieval in remote areas. A calibration method based on radiative transfer simulations together with an inversion methodology using the GRASP code is proposed here. The results demonstrate that this methodology is useful for the retrieval of aerosol extensive properties like aerosol optical depth (AOD) and aerosol volume concentration for total, fine and coarse modes.
Xi Chen, Ting Yang, Zifa Wang, Futing Wang, and Haibo Wang
Atmos. Meas. Tech., 16, 4289–4302, https://doi.org/10.5194/amt-16-4289-2023, https://doi.org/10.5194/amt-16-4289-2023, 2023
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Uncertainties remain great in the planetary boundary layer height (PBLH) determination from radiosonde, especially during the transition period of different PBL regimes. We combine seven existing methods along with statistical modification on gradient-based methods. We find that the ensemble method can eliminate the overestimation of PBLH and reduce the inconsistency between individual methods. The ensemble method improves the effectiveness of PBLH determination to 62.6 %.
Seyed Ali Sayedain, Norman T. O'Neill, James King, Patrick L. Hayes, Daniel Bellamy, Richard Washington, Sebastian Engelstaedter, Andy Vicente-Luis, Jill Bachelder, and Malo Bernhard
Atmos. Meas. Tech., 16, 4115–4135, https://doi.org/10.5194/amt-16-4115-2023, https://doi.org/10.5194/amt-16-4115-2023, 2023
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We used (columnar) ground-based remote sensing (RS) tools and surface measurements to characterize local (drainage-basin) dust plumes at a site in the Yukon. Plume height, particle size, and column-to-surface ratios enabled insights into how satellite RS could be used to analyze Arctic-wide dust transport. This helps modelers refine dust impacts in their climate change simulations. It is an important step since local dust is a key source of dust deposition on snow in the sensitive Arctic region.
Ulla Wandinger, Moritz Haarig, Holger Baars, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 4031–4052, https://doi.org/10.5194/amt-16-4031-2023, https://doi.org/10.5194/amt-16-4031-2023, 2023
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We introduce the algorithms that have been developed to derive cloud top height and aerosol layer products from observations with the Atmospheric Lidar (ATLID) onboard the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE). The products provide information on the uppermost cloud and geometrical and optical properties of aerosol layers in an atmospheric column. They can be used individually but also serve as input for algorithms that combine observations with EarthCARE’s lidar and imager.
Tim Poguntke and Christoph Ritter
Atmos. Meas. Tech., 16, 4009–4014, https://doi.org/10.5194/amt-16-4009-2023, https://doi.org/10.5194/amt-16-4009-2023, 2023
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In this work we analyze the impact of electromagnetic interference on an aerosol lidar. We found that aging transient recorders may produce a noise with fixed frequency that can be removed a posteriori.
Rei Kudo, Akiko Higurashi, Eiji Oikawa, Masahiro Fujikawa, Hiroshi Ishimoto, and Tomoaki Nishizawa
Atmos. Meas. Tech., 16, 3835–3863, https://doi.org/10.5194/amt-16-3835-2023, https://doi.org/10.5194/amt-16-3835-2023, 2023
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A synergistic retrieval method of aerosol components (water-soluble, light-absorbing, dust, and sea salt particles) from CALIOP and MODIS observations was developed. The total global 3-D distributions and those for each component showed good consistency with the CALIOP and MODIS official products and previous studies. The shortwave direct radiative effects of each component at the top and bottom of the atmosphere and for the heating rate were also consistent with previous studies.
Nicole Docter, Rene Preusker, Florian Filipitsch, Lena Kritten, Franziska Schmidt, and Jürgen Fischer
Atmos. Meas. Tech., 16, 3437–3457, https://doi.org/10.5194/amt-16-3437-2023, https://doi.org/10.5194/amt-16-3437-2023, 2023
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We describe the stand-alone retrieval algorithm used to derive aerosol properties relying on measurements of the Multi-Spectral Imager (MSI) aboard the upcoming Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite. This aerosol data product will be available as M-AOT after the launch of EarthCARE. Additionally, we applied the algorithm to simulated EarthCARE MSI and Moderate Resolution Imaging Spectroradiometer (MODIS) data for prelaunch algorithm verification.
Cited articles
Ahmed, A., Song, W., Zhang, Y., Haque, M. A., and Liu, X.: Hybrid BO-XGBoost and BO-RF Models for the Strength Prediction of Self-Compacting Mortars with Parametric Analysis, Materials, 16, 4366, https://doi.org/10.3390/ma16124366, 2023.
Ajtai, N., Mereuta, A., Stefanie, H., Radovici, A., Botezan, C., Zawadzka-Manko, O., Stachlewska, I. S., Stebel, K., and Zehner, C.: SEVIRI Aerosol Optical Depth Validation Using AERONET and Intercomparison with MODIS in Central and Eastern Europe, Remote Sensing, 13, 844, https://doi.org/10.3390/rs13050844, 2021.
Amini, S., Momeni, M., and Monadjemi, A.: Sensitivity analysis of Look-up table for satellite-based aerosol optical depth retrieval, J. Aerosol Sci., 158, 105842, https://doi.org/10.1016/j.jaerosci.2021.105842, 2021.
Benesty, J., Chen, J., Huang, Y., and Cohen, I.: Pearson Correlation Coefficient, in: Noise Reduction in Speech Processing, Springer Topics in Signal Processing, Vol 2, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-00296-0_5, 2009.
Berhane, S. A., Althaf, P., Kumar, K. R., Bu, L., and Yao, M.: A Comprehensive Analysis of AOD and its Species from Reanalysis Data over the Middle East and North Africa Regions: Evaluation of Model Performance Using Machine Learning Techniques, Earth Systems and Environment, 1–26, https://doi.org/10.1007/s41748-024-00513-x, 2024.
Bösenberg, J., Ansmann, A., Baldasano, J. M., Balis, D., Böckmann, C., Calpini, B., Chaikovsky, A., Flamant, P., Hagard, A., Mitev, V., Papayannis, A., Pelon, J., Resendes, D., Schneider, J., Spinelli, N., Trickl, T., Vaughan, G., Visconti, G., and Wiegner, M.: EARLINET: a European Aerosol Research Lidar Network, in: Advances in Laser Remote Sensing, edited by: Dabas, A., Loth, C., and Pelon, J., Ecole Polytechnique, Palaiseau Cedex, France, 155–158, 2001.
Bösenberg, J., Matthias, V., Amodeo, A., Amoiridis, V., Ansmann, A., Baldasano, J. M., Balin, I., Balis, D., Böckmann, C., Boselli, A., Carlsson, G., Chaikovsky, A., Chourdakis, G., Comeron, A., Tomasi, F. D., Eixmann, R., Freudenthaler, V., Giehl, H., Grigorov, I., Hågård, A., Iarlori, M., Kirsche, A., Kolarov, G., Komguem, L., Kreipl, S., Kumpf, W., Larcheveque, G., Linné, H., Matthey, R., Mattis, I., Mekler, A., Mironova, I., Mitev, V., Mona, L., Müller, D., Music, S., Nickovic, S., Pandolfi, M., Papayannis, A., Pappalardo, G., Pelon, J., Pérez, C., Perrone, R. M., Persson, R., Resendes, D. P., Rizi, V., Rocadenbosch, F., Rodrigues, J. A., Sauvage, L., Schneidenbach, L., Schumacher, R., Shcherbakov, V., Simeonov, V., Sobolewski, P., Spinelli, N., Stachlewska, I., Stoyanov, D., Trickl, T., Tsaknakis, G., Vaughan, G., Wandinger, U., Wang, X., Wiegner, M., Zavrtanik, M., and Zerefos, C.: EARLINET project: A European Aerosol Research Lidar Network, Max-Planck Institute (MPI), Final Report No. 348, 1–250, 2003.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Ceamanos, X., Six, B., Moparthy, S., Carrer, D., Georgeot, A., Gasteiger, J., Riedi, J., Attié, J.-L., Lyapustin, A., and Katsev, I.: Instantaneous aerosol and surface retrieval using satellites in geostationary orbit (iAERUS-GEO) – estimation of 15 min aerosol optical depth from MSG/SEVIRI and evaluation with reference data, Atmos. Meas. Tech., 16, 2575–2599, https://doi.org/10.5194/amt-16-2575-2023, 2023.
Chen, B., Dong, L., Huang, J., Wang, Y., Jing, Z., Yan, W., Wang, X., Song, Z., Huang, Z., Guan, X., Dong, X., and Huang, Y.: Analysis of Long-Term Trends in the Vertical Distribution and Transport Paths of Atmospheric Aerosols in Typical Regions of China Using 15 Years of CALIOP Data, J. Geophys. Res.-Atmos., 128, e2022JD038066, https://doi.org/10.1029/2022JD038066, 2023.
Chen, B., Ye, Y., Tong, C., Deng, J., Wang, K., and Hong, Y.: A novel big data mining framework for reconstructing large-scale daily MAIAC AOD data across China from 2000 to 2020, GISci. Remote Sens., 59, 670–685, https://doi.org/10.1080/15481603.2022.2051382, 2022.
Chen, T. and Guestrin, C.: Xgboost: A scalable tree boosting system, arXiv [preprint], 785–794, https://doi.org/10.1145/2939672.2939785, 10 June 2016.
Chen, W., Ran, H., Cao, X., Wang, J., Teng, D., Chen, J., and Zheng, X.: Estimating PM2.5 with high-resolution 1-km AOD data and an improved machine learning model over Shenzhen, China, Sci. Total Environ., 746, 141093, https://doi.org/10.1016/j.scitotenv.2020.141093, 2020.
Chin, M., Diehl, T., Ginoux, P., and Malm, W.: Intercontinental transport of pollution and dust aerosols: implications for regional air quality, Atmos. Chem. Phys., 7, 5501–5517, https://doi.org/10.5194/acp-7-5501-2007, 2007.
Choobari, O. A., Zawar-Reza, P., and Sturman, A.: The global distribution of mineral dust and its impacts on the climate system: A review, Atmos. Res., 138, 152–165, 2014.
Copernicus Climate Change Service (C3S) Climate Data Store (CDS): In situ atmospheric harmonized temperature, relative humidity and wind from 1978 onward from baseline radiosonde networks, C3S CDS [data set], https://doi.org/10.24381/cds.f101d0bf, 2021.
Da, C.: Preliminary assessment of the Advanced Himawari Imager (AHI) measurement onboard Himawari-8 geostationary satellite, Remote Sens. Lett., 6, 637–646, https://doi.org/10.1080/2150704X.2015.1066522, 2015.
European Organization for the Exploitation of Meteorological Satellites: Conversion from radiances to reflectances for SEVIRI warm channels, Rep. EUM/MET/TEN/12/0332, 8 pp., https://user.eumetsat.int/dashboard (last access: 20 June 2023), 2012.
Fisher, D., Muller, J. P., and Yershov, V. N.: Automated stereo retrieval of smoke plume injection heights and retrieval of smoke plume masks from AATSR and their assessment with CALIPSO and MISR, IEEE T. Geosci. Remote, 52, 1249–1258, 2013.
Ge, B., Li, Z., Liu, L., Yang, L., Chen, X., Hou, W., Zhang, Y., Li, D., Li, L., and Qie, L.: A dark target method for Himawari-8/AHI aerosol retrieval: Application and validation, IEEE T. Geosci. Remote, 57, 381–394, https://doi.org/10.1109/TGRS.2018.2854743, 2018.
Georgoulias, A. K., Alexandri, G., Kourtidis, K. A., Lelieveld, J., Zanis, P., Pöschl, U., Levy, R., Amiridis, V., Marinou, E., and Tsikerdekis, A.: Spatiotemporal variability and contribution of different aerosol types to the aerosol optical depth over the Eastern Mediterranean, Atmos. Chem. Phys., 16, 13853–13884, https://doi.org/10.5194/acp-16-13853-2016, 2016.
Granados-Muñoz, M. J., Navas-Guzmán, F., Guerrero-Rascado, J. L., Bravo-Aranda, J. A., Binietoglou, I., Pereira, S. N., Basart, S., Baldasano, J. M., Belegante, L., Chaikovsky, A., Comerón, A., D'Amico, G., Dubovik, O., Ilic, L., Kokkalis, P., Muñoz-Porcar, C., Nickovic, S., Nicolae, D., Olmo, F. J., Papayannis, A., Pappalardo, G., Rodríguez, A., Schepanski, K., Sicard, M., Vukovic, A., Wandinger, U., Dulac, F., and Alados-Arboledas, L.: Profiling of aerosol microphysical properties at several EARLINET/AERONET sites during the July 2012 ChArMEx/EMEP campaign, Atmos. Chem. Phys., 16, 7043–7066, https://doi.org/10.5194/acp-16-7043-2016, 2016.
Grigas, T., Hervo, M., Gimmestad, G., Forrister, H., Schneider, P., Preißler, J., Tarrason, L., and O'Dowd, C.: CALIOP near-real-time backscatter products compared to EARLINET data, Atmos. Chem. Phys., 15, 12179–12191, https://doi.org/10.5194/acp-15-12179-2015, 2015.
Gupta, G., Ratnam, M. V., Madhavan, B. L., Prasad, P., and Narayanamurthy, C. S.: Vertical and spatial distribution of elevated aerosol layers obtained using long-term ground-based and space-borne lidar observations, Atmos. Environ., 246, 118172, https://doi.org/10.1016/j.atmosenv.2020.118172, 2021.
Han, B., Ding, H., Ma, Y., and Gong, W.: Improving retrieval accuracy for aerosol optical depth by fusion of MODIS and CALIOP data, Teh. Vjesn., 24, 791–800, https://doi.org/10.17559/TV-20160429044233, 2017.
Hollstein, A. and Fischer, J.: Retrieving aerosol height from the oxygen A band: a fast forward operator and sensitivity study concerning spectral resolution, instrumental noise, and surface inhomogeneity, Atmos. Meas. Tech., 7, 1429–1441, https://doi.org/10.5194/amt-7-1429-2014, 2014.
Hyslop, N. P.: Impaired visibility: the air pollution people see, Atmos. Environ., 43, 182–195, https://doi.org/10.1016/j.atmosenv.2008.09.067, 2009.
Kalluri, S., Gundy, J., Haman, B., Paullin, A., Van Rompay, P., Vititoe, D., and Weiner, A.: A high performance remote sensing product generation system based on a service oriented architecture for the next generation of Geostationary Operational Environmental Satellites, Remote Sensing, 7, 10385–10399, https://doi.org/10.3390/rs70810385, 2015.
Kaufman, Y. J., Tanré, D., Gordon, H. R., Nakajima, T., Lenoble, J., Frouin, R., Grassl, H., Herman, B. M., and Teillet, P. M.: Passive remote sensing of tropospheric aerosol and atmospheric correction for the aerosol effect, J. Geophys. Res.-Atmos., 102, 16815–16830, https://doi.org/10.1029/97JD01496, 1997.
Kittaka, C., Winker, D. M., Vaughan, M. A., Omar, A., and Remer, L. A.: Intercomparison of column aerosol optical depths from CALIPSO and MODIS-Aqua, Atmos. Meas. Tech., 4, 131–141, https://doi.org/10.5194/amt-4-131-2011, 2011.
Kocaman, S., Debaecker, V., Bas, S., Saunier, S., Garcia, K., and Just, D.: A comprehensive geometric quality assessment approach for MSG SEVIRI imagery, Adv. Space Res., 69, 1462–1480, https://doi.org/10.1016/j.asr.2021.11.018, 2022.
Lebo, Z. J.: The sensitivity of a numerically simulated idealized squall line to the vertical distribution of aerosols, J. Atmos. Sci., 71, 4581–4596, https://doi.org/10.1175/JAS-D-14-0068.1, 2014.
Lee, S., Park, S., Lee, M. I., Kim, G., Im, J., and Song, C. K.: Air quality forecasts improved by combining data assimilation and machine learning with satellite AOD, Geophys. Res. Lett., 49, e2021GL096066, https://doi.org/10.1029/2021GL096066, 2022.
Li, C., Li, J., Dubovik, O., Zeng, Z. C., and Yung, Y. L.: Impact of aerosol vertical distribution on aerosol optical depth retrieval from passive satellite sensors, Remote Sensing, 12, 1524, https://doi.org/10.3390/rs12091524, 2020.
Li, J., Carlson, B. E., Yung, Y. L., Lv, D., Hansen, J., Penner, J. E., Liao, H., Ramaswamy, V., Kahn, R. A., Zhang, P., Dubovik, O., Ding, A., Lacis, A. A., Zhang, L., and Dong, Y.: Scattering and absorbing aerosols in the climate system, Nature Reviews Earth & Environment, 3, 363–379, https://doi.org/10.1038/s43017-022-00296-7, 2022.
Lipponen, A., Mielonen, T., Pitkänen, M. R. A., Levy, R. C., Sawyer, V. R., Romakkaniemi, S., Kolehmainen, V., and Arola, A.: Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land, Atmos. Meas. Tech., 11, 1529–1547, https://doi.org/10.5194/amt-11-1529-2018, 2018.
Liu, B., Ma, Y., Gong, W., Zhang, M., Wang, W., and Shi, Y.: Comparison of AOD from CALIPSO, MODIS, and sun photometer under different conditions over Central China, Scientific Reports, 8, 10066, https://doi.org/10.1038/s41598-018-28417-7, 2018.
Liu, C., Shen, X., and Gao, W.: Intercomparison of CALIOP, MODIS, and AERONET aerosol optical depth over China during the past decade, Int. J. Remote Sens., 39, 7251–7275, https://doi.org/10.1080/01431161.2018.1466070, 2018.
Lyapustin, A., Wang, Y., Laszlo, I., Kahn, R., Korkin, S., Remer, L., and Reid, J. S.: Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm, J. Geophys. Res.-Atmos., 116, D03211, https://doi.org/10.1029/2010JD014986, 2011.
Marinescu, P. J., van den Heever, S. C., Saleeby, S. M., Kreidenweis, S. M., and DeMott, P. J.: The microphysical roles of lower-tropospheric versus midtropospheric aerosol particles in mature-stage MCS precipitation, J. Atmos. Sci., 74, 3657–3678, https://doi.org/10.1175/jas-d-16-0361.1, 2017.
Marshak, A. and Knyazikhin, Y.: The spectral invariant approximation within canopy radiative transfer to support the use of the EPIC/DSCOVR oxygen B-band for monitoring vegetation, J. Quant. Spectrosc. Ra., 191, 7–12, https://doi.org/10.1016/j.jqsrt.2017.01.015, 2017.
Mehta, M., Jain, A., and Chauhan, P.: Aerosol optical depth retrieval over land from OCEANSAT-2/OCM-2 data – A simple physics based approach, Atmos. Pollut. Res., 13, 101339, https://doi.org/10.1016/j.apr.2022.101339, 2022.
Muller, J. P., Mandanayake, A., Moroney, C., Davies, R., Diner, D. J., and Paradise, S.: MISR stereoscopic image matchers: Techniques and results, IEEE T. Geosci. Remote, 40, 1547–1559, 2002.
Nanda, S., de Graaf, M., Veefkind, J. P., Sneep, M., ter Linden, M., Sun, J., and Levelt, P. F.: A first comparison of TROPOMI aerosol layer height (ALH) to CALIOP data, Atmos. Meas. Tech., 13, 3043–3059, https://doi.org/10.5194/amt-13-3043-2020, 2020.
Nicolae, V., Talianu, C., Andrei, S., Antonescu, B., Ene, D., Nicolae, D., Dandocsi, A., Toader, V. E., Ştefan, S., Savu, T., and Vasilescu, J.: Multiyear typology of long-range transported aerosols over Europe, Atmosphere, 10, 482, https://doi.org/10.3390/atmos10090482, 2019.
Ortiz-Amezcua, P., Guerrero-Rascado, J. L., Granados-Muñoz, M. J., Benavent-Oltra, J. A., Böckmann, C., Samaras, S., Stachlewska, I. S., Janicka, Ł., Baars, H., Bohlmann, S., and Alados-Arboledas, L.: Microphysical characterization of long-range transported biomass burning particles from North America at three EARLINET stations, Atmos. Chem. Phys., 17, 5931–5946, https://doi.org/10.5194/acp-17-5931-2017, 2017.
Pashayi, M., Satari, M., and Shahraki, M. M.: Improvement of spatial-temporal resolution of aerosol profile by using multi-source satellite data over the Persian Gulf, Atmos. Environ., 292, 119410, https://doi.org/10.1016/j.atmosenv.2022.119410, 2023.
Pashayi, M., Satari, M., Shahraki, M. M., and Amini, S.: MAIAC AOD profiling over the Persian Gulf: A seasonal-independent machine learning approach, Atmos. Pollut. Res., 15, 102128, https://doi.org/10.1016/j.apr.2024.102128, 2024.
Pashayi, M., Satari, M., and Momeni Shahraki, M.: Sub-Hourly Multi-Layer AOD Estimations for Europe During the Saharan Dust Event (07:12–16:12, March 18, 2022), TIB AV-Portal [video], https://doi.org/10.5446/69730, 2025a.
Pashayi, M., Satari, M., and Momeni Shahraki, M.: Sub-Hourly Multi-Layer AOD Estimations for Europe During the Volcanic Eruption Event (from 07:27 to 15:57, August 14, 2023), TIB AV-Portal [video], https://doi.org/10.5446/69731, 2025a.
Pasternak, F., Lorsignol, J., and Wolff, L.: Spinning enhanced visible and infrared imager (SEVIRI): the new imager for meteosat second generation, in: Space Optics 1994: Earth Observation and Astronomy, SPIE, 2209, 86–94, https://doi.org/10.1117/12.185247, 1994.
Pérez, C., Nickovic, S., Baldasano, J. M., Sicard, M., Rocadenbosch, F., and Cachorro, V. E.: A long Saharan dust event over the western Mediterranean: Lidar, Sun photometer observations, and regional dust modeling?, J. Geophys. Res., 111, D15214, https://doi.org/10.1029/2005JD006579, 2006a.
Pérez, C., Nickovic, S., Pejanovic, G., Baldasano, J. M., and Özsoy, E.: Interactive dust-radiation modeling: A step to improve weather forecasts, J. Geophys. Res., 111, D16206, https://doi.org/10.1029/2005JD006717, 2006b.
Pope III, C. A., Lefler, J. S., Ezzati, M., Higbee, J. D., Marshall, J. D., Kim, S. Y., Bechle, M., Gilliat, K. S., Vernon, S. E., Robinson, A. L., and Burnett, R. T.: Mortality risk and fine particulate air pollution in a large, representative cohort of U.S. adults, Environ. Health Persp., 127, 77007, https://doi.org/10.1289/EHP4438, 2019.
Radosavljevic, V., Vucetic, S., and Obradovic, Z.: A data-mining technique for aerosol retrieval across multiple accuracy measures, IEEE Geosci. Remote S., 7, 411–415, https://doi.org/10.1109/LGRS.2009.2037720, 2010.
Redemann, J., Vaughan, M. A., Zhang, Q., Shinozuka, Y., Russell, P. B., Livingston, J. M., Kacenelenbogen, M., and Remer, L. A.: The comparison of MODIS-Aqua (C5) and CALIOP (V2 & V3) aerosol optical depth, Atmos. Chem. Phys., 12, 3025–3043, https://doi.org/10.5194/acp-12-3025-2012, 2012.
Rogozovsky, I., Ansmann, A., Althausen, D., Heese, B., Engelmann, R., Hofer, J., Baars, H., Schechner, Y., Lyapustin, A., and Chudnovsky, A.: Impact of aerosol layering, complex aerosol mixing, and cloud coverage on high-resolution MAIAC aerosol optical depth measurements: Fusion of lidar, AERONET, satellite, and ground-based measurements, Atmos. Environ., 247, 118163, https://doi.org/10.1016/j.atmosenv.2020.118163, 2021.
Rogozovsky, I., Ohneiser, K., Lyapustin, A., Ansmann, A., and Chudnovsky, A.: The impact of different aerosol layering conditions on the high-resolution MODIS/MAIAC AOD retrieval bias: the uncertainty analysis, Atmos. Environ., 309, 119930, https://doi.org/10.1016/j.atmosenv.2023.119930, 2023.
Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., and Ratier, A.: An introduction to Meteosat second generation (MSG), B. Am. Meteorol. Soc., 83, 977–992, https://doi.org/10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2, 2002.
Schmit, T. J., Lindstrom, S. S., Gerth, J. J., and Gunshor, M. M.: Applications of the 16 spectral bands on the Advanced Baseline Imager (ABI), Journal of Operational Meteorology, 6, 33–46, https://doi.org/10.15191/nwajom.2018.0604, 2018.
Seidel, F. C., Kokhanovsky, A. A., and Schaepman, M. E.: Fast retrieval of aerosol optical depth and its sensitivity to surface albedo using remote sensing data, Atmos. Res., 116, 22–32, https://doi.org/10.1016/j.atmosres.2011.03.006, 2012.
She, L., Zhang, H. K., Li, Z., de Leeuw, G., and Huang, B.: Himawari-8 aerosol optical depth (AOD) retrieval using a deep neural network trained using AERONET observations, Remote Sensing, 12, 4125, https://doi.org/10.3390/rs12244125, 2020.
Stebel, K., Stachlewska, I. S., Nemuc, A., Horálek, J., Schneider, P., Ajtai, N., Diamandi, A., Benešová, N., Boldeanu, M., Botezan, C., Marková, J., Dumitrache, R., Iriza-Burcă, A., Juras, R., Nicolae, D., Nicolae, V., Novotný, P., Ștefănie, H., Vaněk, L., Vlček, O., Zawadzka-Manko, O., and Zehner, C.: SAMIRA-Satellite based monitoring initiative for regional air quality, Remote Sensing, 13, 2219, https://doi.org/10.3390/rs13112219, 2021.
Sulla-Menashe, D. and Friedl, M. A.: User guide to collection 6 MODIS land cover (MCD12Q1 and MCD12C1) product, USGS, Reston, VA, USA, 1, 18 pp., https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf (last access: 23 October 2023), 2018.
Tang, D., Liu, D., Tang, Y., Seyler, B. C., Deng, X., and Zhan, Y.: Comparison of GOCI and Himawari-8 aerosol optical depth for deriving full-coverage hourly PM2.5 across the Yangtze River Delta, Atmos. Environ., 217, 116973, https://doi.org/10.1016/j.atmosenv.2019.116973, 2019.
Tesche, M., Ansmann, A., Mueller, D., Althausen, D., Mattis, I., Heese, B., Freudenthaler, V., Wiegner, M., Esselborn, M., Pisani, G., and Knippertz, P.: Vertical profiling of Saharan dust with Raman lidars and airborne HSRL in southern Morocco during SAMUM, Tellus B, 61, 144–164, https://doi.org/10.1111/j.1600-0889.2008.00390.x, 2009.
Tjemkes, S., Stuhlmann, R., Hewison, T., Müller, J., Gartner, V., and Rota, S.: The conversion from effective radiances to equivalent brightness temperatures, EUMETSAT, Technical report, Version 1, https://www-cdn.eumetsat.int/files/2020-04/pdf_effect_rad_to_brightness.pdf (last access: 20 April 2024), 2012.
Tsang, L., Kong, J. A., and Shin, R. T.: Radiative transfer theory for active remote sensing of a layer of nonspherical particles, Radio Sci., 19, 629–642, https://doi.org/10.1029/RS019i002p00629, 1984.
Val Martin, M., Kahn, R. A., and Tosca, M. G.: A global analysis of wildfire smoke injection heights derived from space-based multi-angle imaging, Remote Sensing, 10, 1609, https://doi.org/10.3390/rs10101609, 2018.
Veefkind, J. P., Aben, I., McMullan, K., Förster, H., De Vries, J., Otter, G., Claas, J., Eskes, H. J., de Haan, J. F., Kleipool, Q., van Weele, M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann, P., Voors, R., Kruizinga, B., Vink, R., and Levelt, P. F.: TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications, Remote Sens. Environ., 120, 70–83, https://doi.org/10.1016/j.rse.2011.09.027, 2012.
Wang, H., Sun, Z., Li, H., Gao, Y., Wu, J., and Cheng, T.: Vertical-distribution characteristics of atmospheric aerosols under different thermodynamic conditions in Beijing, Aerosol Air Qual. Res., 18, 2775–2787, https://doi.org/10.4209/aaqr.2018.03.0078, 2018.
Wei, X., Chang, N. B., Bai, K., and Gao, W.: Satellite remote sensing of aerosol optical depth: Advances, challenges, and perspectives, Crit. Rev. Env. Sci. Tec., 50, 1640–1725, https://doi.org/10.1080/10643389.2019.1665944, 2020.
Winker, D. M., Hunt, W., and Hostetler, C.: Status and performance of the Caliop lidar, Laser Radar Techniques for Atmospheric Sensing, 5575, 8–15, https://doi.org/10.1117/12.571955, 2004.
Winker, D. M., Hunt, W. H., and McGill, M. J.: Initial performance assessment of CALIOP, Geophys. Res. Lett., 34, L19803, https://doi.org/10.1029/2007GL030135, 2007.
Winker, D. M., Tackett, J. L., Getzewich, B. J., Liu, Z., Vaughan, M. A., and Rogers, R. R.: The global 3-D distribution of tropospheric aerosols as characterized by CALIOP, Atmos. Chem. Phys., 13, 3345–3361, https://doi.org/10.5194/acp-13-3345-2013, 2013.
Winker, D. M., Vaughan, M., and Hunt, W.: The CALIPSO mission and initial results from CALIOP, Proceedings of SPIE, 6409, https://doi.org/10.1117/12.698003, 2006.
Witthuhn, J., Hünerbein, A., and Deneke, H.: Evaluation of satellite-based aerosol datasets and the CAMS reanalysis over the ocean utilizing shipborne reference observations, Atmos. Meas. Tech., 13, 1387–1412, https://doi.org/10.5194/amt-13-1387-2020, 2020.
Wu, Y., de Graaf, M., and Menenti, M.: The impact of aerosol vertical distribution on aerosol optical depth retrieval using CALIPSO and MODIS data: Case study over dust and smoke regions, J. Geophys. Res.-Atmos., 122, 8801–8815, https://doi.org/10.1002/2016JD026355, 2017.
Xu, H., Ma, J., Luo, W., Wan, C., and Li, Z.: Research on the distribution and influencing factors of fine mode aerosol optical depth (AODf) in China, Atmos. Environ., 334, 120721, https://doi.org/10.1016/j.atmosenv.2024.120721, 2024.
Xu, X., Wang, J., Wang, Y., Zeng, J., Torres, O., Yang, Y., Marshak, A., Reid, J., and Miller, S.: Passive remote sensing of altitude and optical depth of dust plumes using the oxygen A and B bands: First results from EPIC/DSCOVR at Lagrange-1 point, Geophys. Res. Lett., 44, 7544–7554, https://doi.org/10.1002/2017GL073939, 2017.
Xu, X., Wang, J., Wang, Y., Zeng, J., Torres, O., Reid, J. S., Miller, S. D., Martins, J. V., and Remer, L. A.: Detecting layer height of smoke aerosols over vegetated land and water surfaces via oxygen absorption bands: hourly results from EPIC/DSCOVR in deep space, Atmos. Meas. Tech., 12, 3269–3288, https://doi.org/10.5194/amt-12-3269-2019, 2019.
Yu, H., Kaufman, Y. J., Chin, M., Feingold, G., Remer, L. A., Anderson, T. L., Balkanski, Y., Bellouin, N., Boucher, O., Christopher, S., DeCola, P., Kahn, R., Koch, D., Loeb, N., Reddy, M. S., Schulz, M., Takemura, T., and Zhou, M.: A review of measurement-based assessments of the aerosol direct radiative effect and forcing, Atmos. Chem. Phys., 6, 613–666, https://doi.org/10.5194/acp-6-613-2006, 2006.
Zakšek, K., Hort, M., Zaletelj, J., and Langmann, B.: Monitoring volcanic ash cloud top height through simultaneous retrieval of optical data from polar orbiting and geostationary satellites, Atmos. Chem. Phys., 13, 2589–2606, https://doi.org/10.5194/acp-13-2589-2013, 2013.
Zawadzka, O. and Markowicz, K.: Retrieval of aerosol optical depth from optimal interpolation approach applied to SEVIRI data, Remote Sensing, 6, 7182–7211, https://doi.org/10.3390/rs6087182, 2014.
Zawadzka-Manko, O., Stachlewska, I. S., and Markowicz, K. M.: Near-real-time application of seviri aerosol optical depth algorithm, Remote Sensing, 12, 1481, https://doi.org/10.3390/rs12091481, 2020.
Zege, E. P., Ivanov, A. P., and Katsev, I. L.: Image transfer through a scattering medium, Springer-Verlag, Heidelberg, Vol. 349, ISBN: 978-3-642-75288-9, 1991.
Zeng, Z. C., Natraj, V., Xu, F., Pongetti, T. J., Shia, R. L., Kort, E. A., Toon, G. C., Sander, S. P., and Yung, Y. L.: Constraining aerosol vertical profile in the boundary layer using hyperspectral measurements of oxygen absorption, Geophys. Res. Lett., 45, 10772–10780, https://doi.org/10.1029/2018GL079286, 2018.
Zhang, M. Z., Deng, X., Zhu, R. H., Ren, Y. Z., and Xue, H. W.: The impact of aerosol vertical distribution on a deep convective cloud, Atmosphere, 12, 675, https://doi.org/10.3390/atmos12060675, 2021.
Zhang, Z., Wu, W., Fan, M., Tao, M., Wei, J., Jin, J., Tan, Y., and Wang, Q.: Validation of Himawari-8 aerosol optical depth retrievals over China, Atmos. Environ., 199, 32–44, https://doi.org/10.1016/j.atmosenv.2018.11.024, 2019.
Zhao, C., Liu, Z., Wang, Q., Ban, J., Chen, N. X., and Li, T.: High-resolution daily AOD estimated to full coverage using the random forest model approach in the Beijing-Tianjin-Hebei region, Atmos. Environ., 203, 70–78, https://doi.org/10.1016/j.atmosenv.2019.01.045, 2019.
Short summary
Multi-layer aerosol optical depth (AOD) is retrieved using the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) and machine learning, trained on Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. The model provides AOD at a 3 km × 3 km spatial and 15 min temporal resolution over Europe. It accurately captured multi-layer AOD dynamics during Saharan dust transport and the Mount Etna eruption, demonstrating consistent physical accuracy.
Multi-layer aerosol optical depth (AOD) is retrieved using the geostationary Spinning Enhanced...