Articles | Volume 12, issue 11
https://doi.org/10.5194/amt-12-6017-2019
© Author(s) 2019. 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-12-6017-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Neural network for aerosol retrieval from hyperspectral imagery
Steffen Mauceri
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Sciences, CU Boulder, Boulder,
Colorado, USA
Laboratory for Atmospheric and Space Physics, Boulder, Colorado, USA
Bruce Kindel
Laboratory for Atmospheric and Space Physics, Boulder, Colorado, USA
Steven Massie
Laboratory for Atmospheric and Space Physics, Boulder, Colorado, USA
Peter Pilewskie
Department of Atmospheric and Oceanic Sciences, CU Boulder, Boulder,
Colorado, USA
Laboratory for Atmospheric and Space Physics, Boulder, Colorado, USA
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Satellite overlap is often carried out as a check on the stability of the data collected. We looked at how length of overlap influences how much information can be derived from the overlap period. Several results surprised us: the confidence we could have in the matchup of two records was independent of the offset, and understanding of the relative drift between the two satellite data sets improved significantly with 2–3 years of overlap. Sudden jumps could easily be confused with drift.
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Changes in cloud vertical structure (i.e. the shape of cloud ice water content (IWC) vertical structure) due to variations in aerosol, observed by three different satellite experiments (MODIS, OMI, and MLS) are calculated in the Tropics during 2007–2010. This topic is of interest because aerosol-cloud interactions are the largest source of uncertainty in climate models. Analysis of the effects of MODIS aerosol, OMI absorptive aerosol, and MLS CO (an absorptive aerosol proxy) upon deep convective
B. C. Kindel, P. Pilewskie, K. S. Schmidt, T. Thornberry, A. Rollins, and T. Bui
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Measurements of upper tropospheric-lower stratospheric water vapor amounts in the tropics were made using the 1400 and 1900nm water vapor bands present in airborne solar spectral irradiance data. These were validated with radiative transfer modeling using in situ profiles of water vapor, temperature, and pressure. An approach to extending these types of measurements from aircraft altitudes to the top of the atmosphere to infer stratospheric water vapor amount is outlined.
A. C. Kren, D. R. Marsh, A. K. Smith, and P. Pilewskie
Atmos. Chem. Phys., 14, 4843–4856, https://doi.org/10.5194/acp-14-4843-2014, https://doi.org/10.5194/acp-14-4843-2014, 2014
X. Jiang, M. C. Barth, C. Wiedinmyer, and S. T. Massie
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-13-21383-2013, https://doi.org/10.5194/acpd-13-21383-2013, 2013
Revised manuscript not accepted
I. Ermolli, K. Matthes, T. Dudok de Wit, N. A. Krivova, K. Tourpali, M. Weber, Y. C. Unruh, L. Gray, U. Langematz, P. Pilewskie, E. Rozanov, W. Schmutz, A. Shapiro, S. K. Solanki, and T. N. Woods
Atmos. Chem. Phys., 13, 3945–3977, https://doi.org/10.5194/acp-13-3945-2013, https://doi.org/10.5194/acp-13-3945-2013, 2013
Y. L. Roberts, P. Pilewskie, B. C. Kindel, D. R. Feldman, and W. D. Collins
Atmos. Chem. Phys., 13, 3133–3147, https://doi.org/10.5194/acp-13-3133-2013, https://doi.org/10.5194/acp-13-3133-2013, 2013
H. M. Worden, M. N. Deeter, C. Frankenberg, M. George, F. Nichitiu, J. Worden, I. Aben, K. W. Bowman, C. Clerbaux, P. F. Coheur, A. T. J. de Laat, R. Detweiler, J. R. Drummond, D. P. Edwards, J. C. Gille, D. Hurtmans, M. Luo, S. Martínez-Alonso, S. Massie, G. Pfister, and J. X. Warner
Atmos. Chem. Phys., 13, 837–850, https://doi.org/10.5194/acp-13-837-2013, https://doi.org/10.5194/acp-13-837-2013, 2013
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Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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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.
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
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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.
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.
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.
Jade Low, Roger Teoh, Joel Ponsonby, Edward Gryspeerdt, Marc Shapiro, and Marc Stettler
EGUsphere, https://doi.org/10.5194/egusphere-2024-1458, https://doi.org/10.5194/egusphere-2024-1458, 2024
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The radiative forcing due to contrails is the same order of magnitude as aviation CO2 emissions yet has a higher uncertainty. Observations are vital to improve understanding of the contrail lifecycle, to improve model and to 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.
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.
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.
Alexei Rozanov, Christine Pohl, Carlo Arosio, Adam Bourassa, Klaus Bramstedt, Elizaveta Malinina, Landon Rieger, and John P. Burrows
EGUsphere, https://doi.org/10.5194/egusphere-2024-358, https://doi.org/10.5194/egusphere-2024-358, 2024
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We developed a new algorithm to retrieve vertical distributions of the aerosol extinction coefficient in the stratosphere. The algorithm is applied to measurements of the scattered solar light form the space borne OMPS-LP (Ozone Mapping and Profiler Suite-Limb Profiler) instrument. The retrieval results are compared to the data from other space borne instruments and used to investigate the evolution of the aerosol plume after the eruption of the Hunga Tonga-Hunga Ha'apai volcano in January 2022.
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.
Theano Drosoglou, Ioannis-Panagiotis Raptis, Massimo Valeri, Stefano Casadio, Francesca Barnaba, Marcos Herreras-Giralda, Anton Lopatin, Oleg Dubovik, Gabriele Brizzi, Fabrizio Niro, Monica Campanelli, and Stelios Kazadzis
Atmos. Meas. Tech., 16, 2989–3014, https://doi.org/10.5194/amt-16-2989-2023, https://doi.org/10.5194/amt-16-2989-2023, 2023
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Aerosol optical properties derived from sun photometers depend on the optical depth of trace gases absorbing solar radiation at specific spectral ranges. Various networks use satellite-based climatologies to account for this or neglect their effect. In this work, we evaluate the effect of NO2 absorption in aerosol retrievals from AERONET and SKYNET over two stations in Rome, Italy, with relatively high NO2 spatiotemporal variations, using NO2 data from the Pandora network and the TROPOMI sensor.
Adolfo Comerón, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Michaël Sicard, Federico Dios, Cristina Gil-Díaz, Daniel Camilo Fortunato dos Santos Oliveira, and Francesc Rocadenbosch
Atmos. Meas. Tech., 16, 3015–3025, https://doi.org/10.5194/amt-16-3015-2023, https://doi.org/10.5194/amt-16-3015-2023, 2023
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We derive an explicit (i.e., non-iterative) formula for the retrieval of the overlap function in an aerosol lidar with both elastic and Raman N2 and/or O2 channels used for independent measurements of aerosol backscatter and extinction coefficients. The formula requires only the measured, range-corrected elastic and the corresponding Raman signals, plus an assumed lidar ratio. We assess the influence of the lidar ratio error in the overlap function retrieval and present retrieval examples.
Abdanour Irbah, Julien Delanoë, Gerd-Jan van Zadelhoff, David P. Donovan, Pavlos Kollias, Bernat Puigdomènech Treserras, Shannon Mason, Robin J. Hogan, and Aleksandra Tatarevic
Atmos. Meas. Tech., 16, 2795–2820, https://doi.org/10.5194/amt-16-2795-2023, https://doi.org/10.5194/amt-16-2795-2023, 2023
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The Cloud Profiling Radar (CPR) and ATmospheric LIDar (ATLID) aboard the EarthCARE satellite are used to probe the Earth's atmosphere by measuring cloud and aerosol profiles. ATLID is sensitive to aerosols and small cloud particles and CPR to large ice particles, snowflakes and raindrops. It is the synergy of the measurements of these two instruments that allows a better classification of the atmospheric targets and the description of the associated products, which are the subject of this paper.
Mahesh Kovilakam, Larry Thomason, and Travis Knepp
Atmos. Meas. Tech., 16, 2709–2731, https://doi.org/10.5194/amt-16-2709-2023, https://doi.org/10.5194/amt-16-2709-2023, 2023
Short summary
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The paper describes SAGE III/ISS aerosol/cloud categorization and its implications on Global Space-based Stratospheric Aerosol Climatology (GloSSAC). The presence of data from the SAGE type of multi-wavelength measurements is important in GloSSAC. The new aerosol/cloud categorization method described in this paper will help retain more measurements, particularly in the lower stratosphere during and following a volcanic event and other processes.
Minseok Kim, Jhoon Kim, Hyunkwang Lim, Seoyoung Lee, Yeseul Cho, Huidong Yeo, and Sang-Woo Kim
Atmos. Meas. Tech., 16, 2673–2690, https://doi.org/10.5194/amt-16-2673-2023, https://doi.org/10.5194/amt-16-2673-2023, 2023
Short summary
Short summary
Aerosol height information is important when seeking an understanding of the vertical structure of the aerosol layer and long-range transport. In this study, a geometrical aerosol top height (ATH) retrieval using a parallax of two geostationary satellites is investigated. With sufficient longitudinal separation between the two satellites, a decent ATH product could be retrieved.
Jianglong Zhang, Jeffrey S. Reid, Steven D. Miller, Miguel Román, Zhuosen Wang, Robert J. D. Spurr, and Shawn Jaker
Atmos. Meas. Tech., 16, 2531–2546, https://doi.org/10.5194/amt-16-2531-2023, https://doi.org/10.5194/amt-16-2531-2023, 2023
Short summary
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We adapted the spherical harmonics discrete ordinate method 3-dimentional radiative transfer model (3-D RTM) and developed a nighttime 3-D RTM capability for simulating top-of-atmosphere radiances from artificial light sources for aerosol retrievals. Our study suggests that both aerosol optical depth and aerosol plume height can be effectively retrieved using nighttime observations over artificial light sources, through the newly developed radiative transfer modeling capability.
Xavier Ceamanos, Bruno Six, Suman Moparthy, Dominique Carrer, Adèle Georgeot, Josef Gasteiger, Jérôme Riedi, Jean-Luc Attié, Alexei Lyapustin, and Iosif Katsev
Atmos. Meas. Tech., 16, 2575–2599, https://doi.org/10.5194/amt-16-2575-2023, https://doi.org/10.5194/amt-16-2575-2023, 2023
Short summary
Short summary
A new algorithm to retrieve the diurnal evolution of aerosol optical depth over land and ocean from geostationary meteorological satellites is proposed and successfully evaluated with reference ground-based and satellite data. The high-temporal-resolution aerosol observations that are obtained from the EUMETSAT Meteosat Second Generation mission are unprecedented and open the door to studies that cannot be conducted with the once-a-day observations available from low-Earth-orbit satellites.
Cited articles
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J.,
Devin, M., Ghemawat, S., Irving, G., Isard, M., and Kudlur, M.: Tensorflow: a system for
large-scale machine learning, OSDI, 16, 265–283, 2016.
Adler-Golden, S. M., Matthew, M. W., Bernstein, L. S., Levine, R. Y., Berk, A., Richtsmeier, S. C., Acharya, P. K., Anderson, G. P., Felde, J. W., Gardner, J. A., and Hoke, M. L.: Atmospheric correction for shortwave spectral
imagery based on MODTRAN4, P. Soc. Photo-Opt. Ins., 3753,
61–70, 1999.
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness,
Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989.
Alexander, D. T. L., Crozier, P. A., and Anderson, J. R.: Brown Carbon
Spheres in East Asian Outflow and Their Optical Properties, Science,
321, 833–836, 2008.
Baldridge, A. M., Hook, S. J., Grove, C. I., and Rivera, G.: The ASTER
spectral library version 2.0, Remote Sens. Environ., 113, 711–715, 2009.
Benedetti, A., Morcrette, J.-J., Boucher, O., Dethof, A., Engelen, R. J., Fisher, M., Flentje, H., Huneeus, N., Jones, L., Kaiser, J. W., Kinne, S., Mangold, A., Razinger, M., Simmons, A. J., and Suttie, M.: Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 2. Data assimilation,
J. Geophys. Res.-Atmos., 114, D13, https://doi.org/10.1029/2008JD011115, 2009.
Berk, A., Conforti, P., Kennett, R., Perkins, T., Hawes, F., and Van Den
Bosch, J.: MODTRAN 6: A major upgrade of the MODTRAN radiative transfer
code, Proc. SPIE, 90880H, 113–119, https://doi.org/10.1117/12.2050433, 2014.
Bilal, M., Nichol, J. E., and Chan, P. W.: Validation and accuracy assessment
of a Simplified Aerosol Retrieval Algorithm (SARA) over Beijing under low
and high aerosol loadings and dust storms, Remote Sens. Environ., 153,
50–60, https://doi.org/10.1016/j.rse.2014.07.015, 2014.
Blackwell, W. J.: Neural network Jacobian analysis for high-resolution
profiling of the atmosphere, EURASIP J. Adv. Signal Process., 71, https://doi.org/10.1186/1687-6180-2012-71,
2012.
Chimot, J., Veefkind, J. P., Vlemmix, T., de Haan, J. F., Amiridis, V., Proestakis, E., Marinou, E., and Levelt, P. F.: An exploratory study on the aerosol height retrieval from OMI measurements of the 477 nm O2 – O2 spectral band using a neural network approach, Atmos. Meas. Tech., 10, 783–809, https://doi.org/10.5194/amt-10-783-2017, 2017.
Chu, D. A., Kaufman, Y. J., Zibordi, G., Chern, J. D., Mao, J., Li, C., and
Holben, B. N.: Global monitoring of air pollution over land from the Earth
Observing System-Terra Moderate Resolution Imaging Spectroradiometer
(MODIS), J. Geophys. Res.-Atmos., 108, D21, https://doi.org/10.1029/2002JD003179, 2003.
Davis, C. O., Bowles, J., Leathers, R. A., Korwan, D., Downes, T. V.,
Snyder, W. A., Rhea, W. J., Chen, W., Fisher, J., Bissett, W. P., and Reisse,
R. A.: Ocean PHILLS hyperspectral imager: design, characterization, and
calibration, Opt. Express, 10, 210–221, https://doi.org/10.1364/OE.10.000210, 2002.
Dennsion, P. and Gardner, M.: Hawaii 2000 vegetation species spectra,
Ecological Spectral
Information System, https://doi.org/10.21232/C2HT0K, 2000.
Diner, D. J., Beckert, J. C., Reilly, T. H., Bruegge, C. J., Conel, J. E., Kahn, R. A., Martonchik, J. V., Ackerman, T. P., Davies, R., Gerstl, S. A. W., Gordon, H. R., Muller, J. P., Myneni, R. B., Sellers, P. J., Pinty, B., and Verstraete, M. M.: Multi-angle Imaging SpectroRadiometer (MISR) instrument
description and experiment overview, IEEE T. Geosci. Remote,
36, 1072–1087, 1998.
Di Noia, A., Hasekamp, O. P., Wu, L., van Diedenhoven, B., Cairns, B., and Yorks, J. E.: Combined neural network/Phillips–Tikhonov approach to aerosol retrievals over land from the NASA Research Scanning Polarimeter, Atmos. Meas. Tech., 10, 4235–4252, https://doi.org/10.5194/amt-10-4235-2017, 2017.
Dubovik, O., Holben, B., Eck, T. F., Smirnov, A., Kaufman, Y. J., King, M.
D., Tanré, D., and Slutsker, I.: Variability of Absorption and Optical
Properties of Key Aerosol Types Observed in Worldwide Locations, J. Atmos.
Sci., 59, 590–608, https://doi.org/10.1175/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2, 2002.
Dubovik, O., Li, Z., Mishchenko, M. I., Tanré, D., Karol, Y., Bojkov,
B., Cairns, B., Diner, D. J., Espinosa, W. R., Goloub, P., Gu, X., Hasekamp, O., Hong, J., Hou, W., Knobelspiesse, K. D., Landgraf, J., Li, L., Litvinov, P., Liu, Y., Lopatin, A., Marbach, T., Maring, H., Martins, V., Meijer, Y., Milinevsky, G., Mukai, S., Parol, F., Qiao, Y., Remer, L., Rietjens, J., Sano, I., Stammes, P., Stamnes, S., Sun, X., Tabary, P., Travis, L. D., Waquet, F., Xu, F., Yan, C., and Yin, D.:
Polarimetric remote sensing of atmospheric aerosols: instruments,
methodologies, results, and perspectives, J. Quant. Spectrosc. Ra., 224, 474–511, https://doi.org/10.1016/j.jqsrt.2018.11.024,
2019.
Dubuisson, P., Frouin, R., Dessailly, D., Duforêt, L., Léon, J.-F.,
Voss, K., and Antoine, D.: Estimating the altitude of aerosol plumes over the
ocean from reflectance ratio measurements in the O2 A-band, Remote Sens.
Environ., 113, 1899–1911, 2009.
Eck, T. F., Holben, B. N., Reid, J. S., Dubovik, O., Smirnov, A., O'neill,
N. T., Slutsker, I., and Kinne, S.: Wavelength dependence of the optical
depth of biomass burning, urban, and desert dust aerosols, J. Geophys. Res.-Atmos., 104, 31333–31349, 1999.
Fenn, R. W., Clough, S. A., Gallery, W. O., Good, R. E., Kneizys, F. X.,
Mill, J. D., Rothman, L. S., Shettle, E. P., and Volz, F. E.: Optical and Infrared Properties of the Atmosphere, in: Handbook of Geophysics and the Space Environment, US Air Force Geophysics Laboratory, Hanscom Air Force Base, Bedford, MA, 1985.
Folkman, M. A., Pearlman, J., Liao, L. B., and Jarecke, P. J.: EO-1/Hyperion
hyperspectral imager design, development, characterization, and calibration,
P. Soc. Photo-Opt. Ins., 4151,
40–51, 2001.
Gao, B.-C., Heidebrecht, K. B., and Goetz, A. F. H.: Derivation of scaled
surface reflectances from AVIRIS data, Remote Sens. Environ., 44,
165–178, 1993.
Gordon, I. E., Rothman, L. S., Hill, C., Kochanov, R. V., Tan, Y., Bernath, P. F., Birk, M., Boudon, V., Campargue, A., Chance, K. V., Drouin, B. J., Flaud, J. M., Gamache, R. R., Hodges, J. T., Jacquemart, D., Perevalov, V. I., Perrin, A., Shine, K. P., Smith, M. A.H., Tennyson, J., Toon, G. C., Tran, H., Tyuterev, V. G., Barbe, A., Császár, A. G., Devi, V. M., Furtenbacher, T., Harrison, J. J., Hartmann, J. M., Jolly, A., Johnson, T. J., Karman, T., Kleiner, I., Kyuberis, A. A., Loos, J., Lyulin, O. M., Massie, S. T., Mikhailenko, S. N., Moazzen-Ahmadi, N., Müller, H. S.P., Naumenko, O. V., Nikitin, A. V., Polyansky, O. L., Rey, M., Rotger, M., Sharpe, S. W., Sung, K., Starikova, E., Tashkun, S. A., Auwera, J. Vander, Wagner, G., Wilzewski, J., Wcisło, P., Yu, S., and Zak, E. J.: The
HITRAN2016 molecular spectroscopic database, J. Quant. Spectrosc. Ra., 203, 3–69, 2017.
Green, R. O., Eastwood, M. L., Sarture, C. M., Chrien, T. G., Aronsson, M.,
Chippendale, B. J., Faust, J. A., Pavri, B. E., Chovit, C. J., Solis, M., and Olah, M. R., and Williams, O.: Imaging spectroscopy and the airborne visible/infrared imaging
spectrometer (AVIRIS), Remote Sens. Environ., 65, 227–248, 1998.
Grimm, J. M. A.: Hawaii Volcanoes National Park February 2017 Spectra, Data
set, available at: http://ecosis.org (last access: 8 November 2019) from the Ecological Spectral Information System (EcoSIS), 2017.
Gupta, P., Remer, L. A., Levy, R. C., and Mattoo, S.: Validation of MODIS 3 km land aerosol optical depth from NASA's EOS Terra and Aqua missions, Atmos. Meas. Tech., 11, 3145–3159, https://doi.org/10.5194/amt-11-3145-2018, 2018.
Hamlin, L., Green, R. O., Mouroulis, P., Eastwood, M., Wilson, D., Dudik, M.,
and Paine, C.: Imaging spectrometer science measurements for terrestrial
ecology: AVIRIS and new developments, in: Aerospace Conference, 2011 IEEE,
1–7, 2011.
Haywood, J. and Boucher, O.: Estimates of the direct and indirect radiative
forcing due to tropospheric aerosols: A review, Rev. Geophys., 38,
513–543, 2000.
Heidinger, A. K. and Stephens, G. L.: Molecular line absorption in a
scattering atmosphere. Part II: Application to remote sensing in the O2 A
band, J. Atmos. Sci., 57, 1615–1634, 2000.
Herold, M., Roberts, D. A., Gardner, M. E., and Dennison, P. E.: Spectrometry
for urban area remote sensing–Development and analysis of a spectral
library from 350 to 2400 nm, Remote Sens. Environ., 91, 304–319,
2004a.
Herold, M., Roberts, D. A., Gardner, M., and Dennison, P. E.: Urban
Reflectance Spectra from Santa Barbara, CA, Data set, available at: http://ecosis.org (last access: 8 November 2019) from the Ecological Spectral Information System (EcoSIS) 2004b.
Holben, B. N., Eck, T. F., Slutsker, I., Tanré, D., Buis, J. P., Setzer,
A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F.,
Jankowiak, I., and Smirnov, A.: AERONET–A Federated Instrument Network and
Data Archive for Aerosol Characterization, Remote Sens. Environ., 66,
1–16, https://doi.org/10.1016/S0034-4257(98)00031-5, 1998.
Hornik, K., Stinchcombe, M., and White, H.: Multilayer feedforward networks
are universal approximators, Neural Networks, 2, 359–366,
https://doi.org/10.1016/0893-6080(89)90020-8, 1989.
Hsu, N. C., Tsay, S.-C., King, M. D., and Herman, J. R.: Aerosol properties
over bright-reflecting source regions, IEEE T. Geosci. Remote,
42, 557–569, 2004.
Hsu, N. C., Tsay, S.-C., King, M. D., and Herman, J. R.: Deep blue retrievals
of Asian aerosol properties during ACE-Asia, IEEE T. Geosci. Remote, 44, 3180–3195, 2006.
IPCC: Anthropogenic and Natural Radiative Forcing, Climate Change 2013 – The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, 659–740, https://doi.org/10.1017/CBO9781107415324.018, 2014.
Japkowicz, N., Myers, C., and Gluck, M.: A novelty detection approach
to classification, Proceedings of the 14th International Conference on Artificial Intelligence (IJCAI-95), 1, 518–523, 1995.
Kaufman, Y. J., Wald, A. E., Remer, L. A., Gao, B.-C., Li, R.-R., and Flynn,
L.: The MODIS 2.1-/spl mu/m channel-correlation with visible reflectance for
use in remote sensing of aerosol, IEEE T. Geosci. Remote, 35,
1286–1298, 1997.
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, arXiv
Prepr. arXiv1412.6980, 1–15,
https://doi.org/10.1145/1830483.1830503, 2014.
Kokhanovsky, A. A., Davis, A. B., Cairns, B., Dubovik, O., Hasekamp, O. P., Sano, I., Mukai, S., Rozanov, V. V., Litvinov, P., Lapyonok, T., Kolomiets, I. S., Oberemok, Y. A., Savenkov, S., Martin, W., Wasilewski, A., Di Noia, A., Stap, F. A., Rietjens, J., Xu, F., Natraj, V., Duan, M., Cheng, T., and Munro, R.:
Space-based remote sensing of atmospheric aerosols: The multi-angle
spectro-polarimetric frontier, Earth-Sci. Rev., 145, 85–116,
https://doi.org/10.1016/j.earscirev.2015.01.012, 2015.
Kramer, M. A.: Autoassociative neural networks, Comput. Chem. Eng., 16,
313–328, 1992.
Labate, D., Ceccherini, M., Cisbani, A., De Cosmo, V., Galeazzi, C., Giunti,
L., Melozzi, M., Pieraccini, S., and Stagi, M.: The PRISMA payload
optomechanical design, a high performance instrument for a new hyperspectral
mission, Acta Astronaut., 65, 1429–1436, 2009.
Lary, D. J., Remer, L. A., MacNeill, D., Roscoe, B., and Paradise, S.:
Machine learning and bias correction of MODIS aerosol optical depth, IEEE
Geosci. Remote S., 6, 694–698, 2009.
Lee, C. M., Cable, M. L., Hook, S. J., Green, R. O., Ustin, S. L., Mandl, D.
J., and Middleton, E. M.: An introduction to the NASA Hyperspectral InfraRed
Imager (HyspIRI) mission and preparatory activities, Remote Sens. Environ.,
167, 6–19, https://doi.org/10.1016/j.rse.2015.06.012, 2015.
Levy, R., Hsu, C., Sayer, A., Mattoo, S., and Lee, J.: MODIS Atmosphere L2
Aerosol Product, NASA MODIS Adapt. Process. Syst.,
https://doi.org/10.5067/MODIS/MOD04_L2.006,
2015a.
Levy, R., Hsu, C., Sayer, A., Mattoo, S., and Lee, J.: MODIS Atmosphere L2
Aerosol Product, NASA MODIS Adapt. Process. Syst.,
https://doi.org/10.5067/MODIS/MYD04_L2.006, 2015b.
Levy, R. C., Remer, L. A., Martins, J. V, Kaufman, Y. J., Plana-Fattori, A.,
Redemann, J., and Wenny, B.: Evaluation of the MODIS aerosol retrievals over
ocean and land during CLAMS, J. Atmos. Sci., 62, 974–992, 2005.
Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia, F., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013, 2013.
Licciardi, G. A. and Del Frate, F.: Pixel unmixing in hyperspectral data by
means of neural networks, IEEE T. Geosci. Remote, 49,
4163–4172, https://doi.org/10.1109/TGRS.2011.2160950, 2011.
Massie, S. T. and Hervig, M.: HITRAN 2012 refractive indices, J. Quant.
Spectrosc. Ra., 130, 373–380, 2013.
Meerdink, S. K., Hook, S. J., Grove, C. I., Abbott, E. A., and Roberts, D.
A.: The ECOSTRESS Spectral Library version 1.0., Remote Sens. Environ.,
230, 111196, https://doi.org/10.1016/j.rse.2019.05.015, 2019.
Min, Q.-L., Harrison, L. C., Kiedron, P., Berndt, J., and Joseph, E.: A
high-resolution oxygen A-band and water vapor band spectrometer, J. Geophys.
Res.-Atmos., 109, 2, https://doi.org/10.1029/2003jd003540, 2004.
Minnis, P., Hong, G., Sun-Mack, S., Smith Jr., W. L., Chen, Y., and Miller, S.
D.: Estimating nocturnal opaque ice cloud optical depth from MODIS
multispectral infrared radiances using a neural network method, J. Geophys.
Res.-Atmos., 121, 4907–4932, 2016.
Mishchenko, M. I. and Travis, L. D.: Capabilities and limitations of a
current FORTRAN implementation of the T-matrix method for randomly oriented,
rotationally symmetric scatterers, J. Quant. Spectrosc. Ra.,
60, 309–324, 1998.
Morcrette, J.-J., Boucher, O., Jones, L., Salmond, D., Bechtold, P., Beljaars, A., Benedetti, A., Bonet, A., Kaiser, J. W., Razinger, M., Schulz, M., Serrar, S., Simmons, A. J., Sofiev, M., Suttie, M., Tompkins, A. M., and Untch, A.:
Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: Forward modeling,
J. Geophys. Res.-Atmos.,
114,
D6,
https://doi.org/10.1029/2008JD011235, 2009.
Mouroulis, P., Green, R. O., and Chrien, T. G.: Design of pushbroom imaging
spectrometers for optimum recovery of spectroscopic and spatial information,
Appl. Optics, 39, 2210–2220, 2000.
Mouroulis, P., Hartley, F. T., Wilson, D. W., White, V. E., Shori, A.,
Nguyen, S., Zhang, M., and Feldman, M.: Blazed grating fabrication through
gray-scale X-ray lithography, Opt. Express, 11, 270–281, 2003.
Nan, F. and A., C. S.: Measurement-based estimates of direct radiative
effects of absorbing aerosols above clouds, J. Geophys. Res.-Atmos.,
120, 6908–6921, https://doi.org/10.1002/2015JD023252, 2015.
Okada, K., Heintzenberg, J., Kai, K., and Qin, Y.: Shape of atmospheric
mineral particles collected in three Chinese arid-regions, Geophys. Res.
Lett., 28, 3123–3126, 2001.
Palsson, B., Sigurdsson, J., Sveinsson, J. R., and Ulfarsson, M. O.:
Hyperspectral Unmixing Using a Neural Network Autoencoder, IEEE Access, 6,
25646–25656, 2018.
Pincus, R. and Baker, M. B.: Effect of precipitation on the albedo
susceptibility of clouds in the marine boundary layer, Nature, 372,
250–252, https://doi.org/10.1038/372250a0, 1994.
Pope III, C. A., Ezzati, M., and Dockery, D. W.: Fine-particulate air
pollution and life expectancy in the United States, N. Engl. J. Med.,
360, 376–386, 2009.
Prasad, A. K. and Singh, R. P.: Comparison of MISR-MODIS aerosol optical
depth over the Indo-Gangetic basin during the winter and summer seasons
(2000–2005), Remote Sens. Environ., 107, 109–119, 2007.
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, 2010.
Remer, L. A., Kaufman, Y. J., Tanré, D., Mattoo, S., Chu, D. A., Martins, J. V., Li, R. R., Ichoku, C., Levy, R. C., Kleidman, R. G., Eck, T. F., Vermote, E., and Holben, B. N.: The MODIS aerosol algorithm, products, and validation, J. Atmos.
Sci., 62, 947–973, https://doi.org/10.1175/JAS3385.1, 2005.
Rencz, A. N. and Ryerson, R. A.: Manual of remote sensing, remote sensing
for the earth sciences, John Wiley & Sons Incorporated, 1999.
Salomonson, V. V, Barnes, W. L., Maymon, P. W., Montgomery, H. E., and
Ostrow, H.: MODIS: Advanced facility instrument for studies of the Earth as
a system, IEEE T. Geosci. Remote, 27, 145–153, 1989.
Stamnes, K., Tsay, S.-C., Wiscombe, W., and Jayaweera, K.: Numerically stable
algorithm for discrete-ordinate-method radiative transfer in multiple
scattering and emitting layered media, Appl. Optics, 27, 2502,
https://doi.org/10.1364/AO.27.002502, 1988.
Taravat, A., Del Frate, F., Cornaro, C., and Vergari, S.: Neural networks and
support vector machine algorithms for automatic cloud classification of
whole-sky ground-based images, IEEE Geosci. Remote S., 12,
666–670, 2015.
Tennant, W. E., Lee, D., Zandian, M., Piquette, E., and Carmody, M.: MBE
HgCdTe Technology: A very general solution to IR Detection, Described by
“Rule 07”, a very convenient Heuristic, J. Electron. Mater., 37,
1406–1410, 2008.
Thompson, D. R., Gao, B. C., Green, R. O., Roberts, D. A., Dennison, P. E.,
and Lundeen, S. R.: Atmospheric correction for global mapping spectroscopy:
ATREM advances for the HyspIRI preparatory campaign, Remote Sens. Environ.,
167, 64–77, https://doi.org/10.1016/j.rse.2015.02.010, 2015.
Thompson, D. R., Natraj, V., Green, R. O., Helmlinger, M. C., Gao, B.-C., and
Eastwood, M. L.: Optimal estimation for imaging spectrometer atmospheric
correction, Remote Sens. Environ., 216, 355–373, 2018.
Twomey, S.: The influence of pollution on the shortwave albedo of clouds, J.
Atmos. Sci., 34, 1149–1152, 1977.
Van Donkelaar, A., Martin, R. V, Brauer, M., Kahn, R., Levy, R., Verduzco,
C., and Villeneuve, P. J.: Global estimates of ambient fine particulate
matter concentrations from satellite-based aerosol optical depth:
development and application, National Institute of Environmental Health
Science, 2010.
Verger, A., Baret, F., and Camacho, F.: Optimal modalities for radiative
transfer-neural network estimation of canopy biophysical characteristics:
Evaluation over an agricultural area with CHRIS/PROBA observations, Remote
Sens. Environ., 115, 415–426, https://doi.org/10.1016/j.rse.2010.09.012, 2011.
Verhoef, W. and Bach, H.: Simulation of hyperspectral and directional
radiance images using coupled biophysical and atmospheric radiative transfer
models, Remote Sens. Environ., 87, 23–41, 2003.
Wang, Y., Yuan, Q., Li, T., Shen, H., Zheng, L., and Zhang, L.: Evaluation
and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET
over regions in China with multifarious underlying surfaces, Atmos.
Environ., 200, 280–301, https://doi.org/10.1016/j.atmosenv.2018.12.023,
2019.
Werbos, P.: Beyond Regression: New Tools for Prediction and Analysis in the
Behavioral Sciences, PhD Diss., Harvard Univ., 1974.
Wielicki, B. A., Young, D. F., Mlynczak, M. G., Thome, K. J., Leroy, S.,
Corliss, J., Anderson, J. G., Ao, C. O., Bantges, R., Best, F., Bowman, K.,
Brindley, H., Butler, J. J., Collins, W., Dykema, J. A., Doelling, D. R.,
Feldman, D. R., Fox, N., Huang, X., Holz, R., Huang, Y., Jin, Z., Jennings,
D., Johnson, D. G., Jucks, K., Kato, S., Kirk-Davidoff, D. B., Knuteson, R.,
Kopp, G., Kratz, D. P., Liu, X., Lukashin, C., Mannucci, A. J.,
Phojanamongkolkij, N., Pilewskie, P., Ramaswamy, V., Revercomb, H., Rice,
J., Roberts, Y., Roithmayr, C. M., Rose, F., Sandford, S., Shirley, E. L.,
Smith, W. L., Soden, B., Speth, P. W., Sun, W., Taylor, P. C., Tobin, D., and
Xiong, X.: Achieving Climate Change Absolute Accuracy in Orbit, B. Am.
Meteorol. Soc., 94, 1519–1539, https://doi.org/10.1175/BAMS-D-12-00149.1, 2013.
Wold, S., Esbensen, K., and Geladi, P.: Principal component analysis, Chemometr.
Intell. Lab., 2, 37–52, https://doi.org/10.1016/0169-7439(87)80084-9, 1987.
World Health Organization: Air quality guidelines: global update 2005, World
Health Organization, 2006.
World Health Organization: Burden of disease from the joint effects of
household and ambient Air pollution for 2016, 2018.
Xiao, Z., Liang, S., Wang, J., Chen, P., Yin, X., Zhang, L., and Song, J.:
Use of general regression neural networks for generating the GLASS leaf area
index product from time-series MODIS surface reflectance, IEEE T.
Geosci. Remote, 52, 209–223, 2014.
Short summary
Aerosols are fine particles that are suspended in Earth’s atmosphere. A better understanding of aerosols is important to lower uncertainties in climate predictions. We propose measuring aerosols from satellites and airplanes equipped with hyperspectral cameras using an artificial neural network, a form of machine learning. We applied our neural network to hyperspectral observations from a recent airplane flight over India and find general agreement with independent aerosol measurements.
Aerosols are fine particles that are suspended in Earth’s atmosphere. A better understanding...