Articles | Volume 13, issue 2
https://doi.org/10.5194/amt-13-373-2020
© Author(s) 2020. 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-13-373-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Feb 2020
Research article |
| 03 Feb 2020
| 03 Feb 2020
A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing
GESTAR, Universities Space Research Association, Columbia, MD, USA
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Yves Govaerts
Rayference, 1030 Brussels, Belgium
Pekka Kolmonen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Antti Lipponen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Marta Luffarelli
Rayference, 1030 Brussels, Belgium
Tero Mielonen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Falguni Patadia
GESTAR, Universities Space Research Association, Columbia, MD, USA
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Thomas Popp
Deutsches Zentrum für Luft-und Raumfahrt e. V. (DLR), Deutsches Fernerkundungsdatenzentrum (DFD), 82234 Oberpfaffenhofen, Germany
Adam C. Povey
National Centre for Earth Observation, University of Oxford, Oxford, OX1 3PU, UK
Kerstin Stebel
Atmosphere and Climate Department, NILU – Norwegian Institute for Air Research, 2007 Kjeller, Norway
Marcin L. Witek
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
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Mariya Petrenko, Ralph Kahn, Mian Chin, Susanne E. Bauer, Tommi Bergman, Huisheng Bian, Gabriele Curci, Ben Johnson, Johannes Kaiser, Zak Kipling, Harri Kokkola, Xiaohong Liu, Keren Mezuman, Tero Mielonen, Gunnar Myhre, Xiaohua Pan, Anna Protonotariou, Samuel Remy, Ragnhild Bieltvedt Skeie, Philip Stier, Toshihiko Takemura, Kostas Tsigaridis, Hailong Wang, Duncan Watson-Parris, and Kai Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1487, https://doi.org/10.5194/egusphere-2024-1487, 2024
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We compared smoke plume simulations from 11 global models to each other and to satellite smoke-amount observations, aimed at constraining smoke source strength. In regions where plumes are thick and background aerosol is low, models and satellites compare well. However, the input emission inventory tends to underestimate in many places, and particle property and loss-rate assumptions vary enormously among models, causing uncertainties that require systematic in-situ measurements to resolve.
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.
Timo H. Virtanen, Anu-Maija Sundström, Elli Suhonen, Antti Lipponen, Antti Arola, Christopher O'Dell, Robert R. Nelson, and Hannakaisa Lindqvist
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-77, https://doi.org/10.5194/amt-2024-77, 2024
Preprint under review for AMT
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Rui Song, Adam Povey, and Roy G. Grainger
Atmos. Meas. Tech., 17, 2521–2538, https://doi.org/10.5194/amt-17-2521-2024, https://doi.org/10.5194/amt-17-2521-2024, 2024
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Xiaoxia Shang, Antti Lipponen, Maria Filioglou, Anu-Maija Sundström, Mark Parrington, Virginie Buchard, Anton S. Darmenov, Ellsworth J. Welton, Eleni Marinou, Vassilis Amiridis, Michael Sicard, Alejandro Rodríguez-Gómez, Mika Komppula, and Tero Mielonen
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In June 2019, smoke particles from a Canadian wildfire event were transported to Europe. The long-range-transported smoke plumes were monitored with a spaceborne lidar and reanalysis models. Based on the aerosol mass concentrations estimated from the observations, the reanalysis models had difficulties in reproducing the amount and location of the smoke aerosols during the transport event. Consequently, more spaceborne lidar missions are needed for reliable monitoring of aerosol plumes.
Kalle Nordling, Jukka-Pekka Keskinen, Sami Romakkaniemi, Harri Kokkola, Petri Räisänen, Antti Lipponen, Antti-Ilari Partanen, Jaakko Ahola, Juha Tonttila, Muzaffer Ege Alper, Hannele Korhonen, and Tomi Raatikainen
Atmos. Chem. Phys., 24, 869–890, https://doi.org/10.5194/acp-24-869-2024, https://doi.org/10.5194/acp-24-869-2024, 2024
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Our results show that the global model is stable and it provides meaningful results. This way we can include a physics-based presentation of sub-grid physics (physics which happens on a 100 m scale) in the global model, whose resolution is on a 100 km scale.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
EGUsphere, https://doi.org/10.5194/egusphere-2023-2392, https://doi.org/10.5194/egusphere-2023-2392, 2024
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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.
Robert R. Nelson, Marcin L. Witek, Michael J. Garay, Michael A. Bull, James A. Limbacher, Ralph A. Kahn, and David J. Diner
Atmos. Meas. Tech., 16, 4947–4960, https://doi.org/10.5194/amt-16-4947-2023, https://doi.org/10.5194/amt-16-4947-2023, 2023
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Shallow and coastal waters are nutrient-rich and turbid due to runoff. They are also located in areas where the atmosphere has more aerosols than open-ocean waters. NASA's Multi-angle Imaging SpectroRadiometer (MISR) has been monitoring aerosols for over 23 years but does not report results over shallow waters. We developed a new algorithm that uses all four of MISR’s bands and considers light leaving water surfaces. This algorithm performs well and increases over-water measurements by over 7 %.
Edward Gryspeerdt, Adam C. Povey, Roy G. Grainger, Otto Hasekamp, N. Christina Hsu, Jane P. Mulcahy, Andrew M. Sayer, and Armin Sorooshian
Atmos. Chem. Phys., 23, 4115–4122, https://doi.org/10.5194/acp-23-4115-2023, https://doi.org/10.5194/acp-23-4115-2023, 2023
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The impact of aerosols on clouds is one of the largest uncertainties in the human forcing of the climate. Aerosol can increase the concentrations of droplets in clouds, but observational and model studies produce widely varying estimates of this effect. We show that these estimates can be reconciled if only polluted clouds are studied, but this is insufficient to constrain the climate impact of aerosol. The uncertainty in aerosol impact on clouds is currently driven by cases with little aerosol.
Tuuli Miinalainen, Harri Kokkola, Antti Lipponen, Antti-Pekka Hyvärinen, Vijay Kumar Soni, Kari E. J. Lehtinen, and Thomas Kühn
Atmos. Chem. Phys., 23, 3471–3491, https://doi.org/10.5194/acp-23-3471-2023, https://doi.org/10.5194/acp-23-3471-2023, 2023
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We simulated the effects of aerosol emission mitigation on both global and regional radiative forcing and city-level air quality with a global-scale climate model. We used a machine learning downscaling approach to bias-correct the PM2.5 values obtained from the global model for the Indian megacity New Delhi. Our results indicate that aerosol mitigation could result in both improved air quality and less radiative heating for India.
Andrew M. Sayer, Luca Lelli, Brian Cairns, Bastiaan van Diedenhoven, Amir Ibrahim, Kirk D. Knobelspiesse, Sergey Korkin, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 969–996, https://doi.org/10.5194/amt-16-969-2023, https://doi.org/10.5194/amt-16-969-2023, 2023
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This paper presents a method to estimate the height of the top of clouds above Earth's surface using satellite measurements. It is based on light absorption by oxygen in Earth's atmosphere, which darkens the signal that a satellite will see at certain wavelengths of light. Clouds "shield" the satellite from some of this darkening, dependent on cloud height (and other factors), because clouds scatter light at these wavelengths. The method will be applied to the future NASA PACE mission.
Andrew T. Prata, Roy G. Grainger, Isabelle A. Taylor, Adam C. Povey, Simon R. Proud, and Caroline A. Poulsen
Atmos. Meas. Tech., 15, 5985–6010, https://doi.org/10.5194/amt-15-5985-2022, https://doi.org/10.5194/amt-15-5985-2022, 2022
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Satellite observations are often used to track ash clouds and estimate their height, particle sizes and mass; however, satellite-based techniques are always associated with some uncertainty. We describe advances in a satellite-based technique that is used to estimate ash cloud properties for the June 2019 Raikoke (Russia) eruption. Our results are significant because ash warning centres increasingly require uncertainty information to correctly interpret,
aggregate and utilise the data.
Huan Yu, Claudia Emde, Arve Kylling, Ben Veihelmann, Bernhard Mayer, Kerstin Stebel, and Michel Van Roozendael
Atmos. Meas. Tech., 15, 5743–5768, https://doi.org/10.5194/amt-15-5743-2022, https://doi.org/10.5194/amt-15-5743-2022, 2022
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In this study, we have investigated the impact of 3D clouds on the tropospheric NO2 retrieval from UV–visible sensors. We applied standard NO2 retrieval methods including cloud corrections to synthetic data generated by the 3D radiative transfer model. A sensitivity study was done for synthetic data, and dependencies on various parameters were investigated. Possible mitigation strategies were investigated and compared based on 3D simulations and observed data.
Ville Leinonen, Harri Kokkola, Taina Yli-Juuti, Tero Mielonen, Thomas Kühn, Tuomo Nieminen, Simo Heikkinen, Tuuli Miinalainen, Tommi Bergman, Ken Carslaw, Stefano Decesari, Markus Fiebig, Tareq Hussein, Niku Kivekäs, Radovan Krejci, Markku Kulmala, Ari Leskinen, Andreas Massling, Nikos Mihalopoulos, Jane P. Mulcahy, Steffen M. Noe, Twan van Noije, Fiona M. O'Connor, Colin O'Dowd, Dirk Olivie, Jakob B. Pernov, Tuukka Petäjä, Øyvind Seland, Michael Schulz, Catherine E. Scott, Henrik Skov, Erik Swietlicki, Thomas Tuch, Alfred Wiedensohler, Annele Virtanen, and Santtu Mikkonen
Atmos. Chem. Phys., 22, 12873–12905, https://doi.org/10.5194/acp-22-12873-2022, https://doi.org/10.5194/acp-22-12873-2022, 2022
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We provide the first extensive comparison of detailed aerosol size distribution trends between in situ observations from Europe and five different earth system models. We investigated aerosol modes (nucleation, Aitken, and accumulation) separately and were able to show the differences between measured and modeled trends and especially their seasonal patterns. The differences in model results are likely due to complex effects of several processes instead of certain specific model features.
Chao Yan, Yicheng Shen, Dominik Stolzenburg, Lubna Dada, Ximeng Qi, Simo Hakala, Anu-Maija Sundström, Yishuo Guo, Antti Lipponen, Tom V. Kokkonen, Jenni Kontkanen, Runlong Cai, Jing Cai, Tommy Chan, Liangduo Chen, Biwu Chu, Chenjuan Deng, Wei Du, Xiaolong Fan, Xu-Cheng He, Juha Kangasluoma, Joni Kujansuu, Mona Kurppa, Chang Li, Yiran Li, Zhuohui Lin, Yiliang Liu, Yuliang Liu, Yiqun Lu, Wei Nie, Jouni Pulliainen, Xiaohui Qiao, Yonghong Wang, Yifan Wen, Ye Wu, Gan Yang, Lei Yao, Rujing Yin, Gen Zhang, Shaojun Zhang, Feixue Zheng, Ying Zhou, Antti Arola, Johanna Tamminen, Pauli Paasonen, Yele Sun, Lin Wang, Neil M. Donahue, Yongchun Liu, Federico Bianchi, Kaspar R. Daellenbach, Douglas R. Worsnop, Veli-Matti Kerminen, Tuukka Petäjä, Aijun Ding, Jingkun Jiang, and Markku Kulmala
Atmos. Chem. Phys., 22, 12207–12220, https://doi.org/10.5194/acp-22-12207-2022, https://doi.org/10.5194/acp-22-12207-2022, 2022
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Atmospheric new particle formation (NPF) is a dominant source of atmospheric ultrafine particles. In urban environments, traffic emissions are a major source of primary pollutants, but their contribution to NPF remains under debate. During the COVID-19 lockdown, traffic emissions were significantly reduced, providing a unique chance to examine their relevance to NPF. Based on our comprehensive measurements, we demonstrate that traffic emissions alone are not able to explain the NPF in Beijing.
Larisa Sogacheva, Matthieu Denisselle, Pekka Kolmonen, Timo H. Virtanen, Peter North, Claire Henocq, Silvia Scifoni, and Steffen Dransfeld
Atmos. Meas. Tech., 15, 5289–5322, https://doi.org/10.5194/amt-15-5289-2022, https://doi.org/10.5194/amt-15-5289-2022, 2022
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The aim of this study was to provide global characterisation of a new SYNERGY aerosol product derived from the data from the OLCI and SLSTR sensors aboard the Sentinel-3A and Sentinel-3B satellites. Over ocean, the performance of SYNERGY-retrieved AOD is good. Reduced performance over land was expected since the surface reflectance and angular distribution of scattering are more difficult to treat. Validation statistics are often slightly better for S3B and in the Southern Hemisphere.
Qirui Zhong, Nick Schutgens, Guido van der Werf, Twan van Noije, Kostas Tsigaridis, Susanne E. Bauer, Tero Mielonen, Alf Kirkevåg, Øyvind Seland, Harri Kokkola, Ramiro Checa-Garcia, David Neubauer, Zak Kipling, Hitoshi Matsui, Paul Ginoux, Toshihiko Takemura, Philippe Le Sager, Samuel Rémy, Huisheng Bian, Mian Chin, Kai Zhang, Jialei Zhu, Svetlana G. Tsyro, Gabriele Curci, Anna Protonotariou, Ben Johnson, Joyce E. Penner, Nicolas Bellouin, Ragnhild B. Skeie, and Gunnar Myhre
Atmos. Chem. Phys., 22, 11009–11032, https://doi.org/10.5194/acp-22-11009-2022, https://doi.org/10.5194/acp-22-11009-2022, 2022
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Aerosol optical depth (AOD) errors for biomass burning aerosol (BBA) are evaluated in 18 global models against satellite datasets. Notwithstanding biases in satellite products, they allow model evaluations. We observe large and diverse model biases due to errors in BBA. Further interpretations of AOD diversities suggest large biases exist in key processes for BBA which require better constraining. These results can contribute to further model improvement and development.
Meng Gao, Kirk Knobelspiesse, Bryan A. Franz, Peng-Wang Zhai, Andrew M. Sayer, Amir Ibrahim, Brian Cairns, Otto Hasekamp, Yongxiang Hu, Vanderlei Martins, P. Jeremy Werdell, and Xiaoguang Xu
Atmos. Meas. Tech., 15, 4859–4879, https://doi.org/10.5194/amt-15-4859-2022, https://doi.org/10.5194/amt-15-4859-2022, 2022
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In this work, we assessed the pixel-wise retrieval uncertainties on aerosol and ocean color derived from multi-angle polarimetric measurements. Standard error propagation methods are used to compute the uncertainties. A flexible framework is proposed to evaluate how representative these uncertainties are compared with real retrieval errors. Meanwhile, to assist operational data processing, we optimized the computational speed to evaluate the retrieval uncertainties based on neural networks.
Arve Kylling, Claudia Emde, Huan Yu, Michel van Roozendael, Kerstin Stebel, Ben Veihelmann, and Bernhard Mayer
Atmos. Meas. Tech., 15, 3481–3495, https://doi.org/10.5194/amt-15-3481-2022, https://doi.org/10.5194/amt-15-3481-2022, 2022
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Atmospheric trace gases such as nitrogen dioxide (NO2) may be measured by satellite instruments sensitive to solar ultraviolet–visible radiation reflected from Earth and its atmosphere. For a single pixel, clouds in neighbouring pixels may affect the radiation and hence the retrieved trace gas amount. We found that for a solar zenith angle less than about 40° this cloud-related NO2 bias is typically below 10 %, while for larger solar zenith angles the NO2 bias is on the order of tens of percent.
Jaakko Ahola, Tomi Raatikainen, Muzaffer Ege Alper, Jukka-Pekka Keskinen, Harri Kokkola, Antti Kukkurainen, Antti Lipponen, Jia Liu, Kalle Nordling, Antti-Ilari Partanen, Sami Romakkaniemi, Petri Räisänen, Juha Tonttila, and Hannele Korhonen
Atmos. Chem. Phys., 22, 4523–4537, https://doi.org/10.5194/acp-22-4523-2022, https://doi.org/10.5194/acp-22-4523-2022, 2022
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Clouds are important for the climate, and cloud droplets have a significant role in cloud properties. Cloud droplets form when air rises and cools and water vapour condenses on small particles that can be natural or of anthropogenic origin. Currently, the updraft velocity, meaning how fast the air rises, is poorly represented in global climate models. In our study, we show three methods that will improve the depiction of updraft velocity and which properties are vital to updrafts.
Christine D. Groot Zwaaftink, Wenche Aas, Sabine Eckhardt, Nikolaos Evangeliou, Paul Hamer, Mona Johnsrud, Arve Kylling, Stephen M. Platt, Kerstin Stebel, Hilde Uggerud, and Karl Espen Yttri
Atmos. Chem. Phys., 22, 3789–3810, https://doi.org/10.5194/acp-22-3789-2022, https://doi.org/10.5194/acp-22-3789-2022, 2022
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We investigate causes of a poor-air-quality episode in northern Europe in October 2020 during which EU health limits for air quality were vastly exceeded. Such episodes may trigger measures to improve air quality. Analysis based on satellite observations, transport simulations, and surface observations revealed two sources of pollution. Emissions of mineral dust in Central Asia and biomass burning in Ukraine arrived almost simultaneously in Norway, and transport continued into the Arctic.
Claudia Emde, Huan Yu, Arve Kylling, Michel van Roozendael, Kerstin Stebel, Ben Veihelmann, and Bernhard Mayer
Atmos. Meas. Tech., 15, 1587–1608, https://doi.org/10.5194/amt-15-1587-2022, https://doi.org/10.5194/amt-15-1587-2022, 2022
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Retrievals of trace gas concentrations from satellite observations can be affected by clouds in the vicinity, either by shadowing or by scattering of radiation from clouds in the clear region. We used a Monte Carlo radiative transfer model to generate synthetic satellite observations, which we used to test retrieval algorithms and to quantify the error of retrieved NO2 vertical column density due to cloud scattering.
Antti Lipponen, Jaakko Reinvall, Arttu Väisänen, Henri Taskinen, Timo Lähivaara, Larisa Sogacheva, Pekka Kolmonen, Kari Lehtinen, Antti Arola, and Ville Kolehmainen
Atmos. Meas. Tech., 15, 895–914, https://doi.org/10.5194/amt-15-895-2022, https://doi.org/10.5194/amt-15-895-2022, 2022
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We have developed a machine-learning-based model that can be used to correct the Sentinel-3 satellite-based aerosol parameter data of the Synergy data product. The strength of the model is that the original satellite data processing does not have to be carried out again but the correction can be carried out with the data already available. We show that the correction significantly improves the accuracy of the satellite aerosol parameters.
Matthew W. Christensen, Andrew Gettelman, Jan Cermak, Guy Dagan, Michael Diamond, Alyson Douglas, Graham Feingold, Franziska Glassmeier, Tom Goren, Daniel P. Grosvenor, Edward Gryspeerdt, Ralph Kahn, Zhanqing Li, Po-Lun Ma, Florent Malavelle, Isabel L. McCoy, Daniel T. McCoy, Greg McFarquhar, Johannes Mülmenstädt, Sandip Pal, Anna Possner, Adam Povey, Johannes Quaas, Daniel Rosenfeld, Anja Schmidt, Roland Schrödner, Armin Sorooshian, Philip Stier, Velle Toll, Duncan Watson-Parris, Robert Wood, Mingxi Yang, and Tianle Yuan
Atmos. Chem. Phys., 22, 641–674, https://doi.org/10.5194/acp-22-641-2022, https://doi.org/10.5194/acp-22-641-2022, 2022
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Trace gases and aerosols (tiny airborne particles) are released from a variety of point sources around the globe. Examples include volcanoes, industrial chimneys, forest fires, and ship stacks. These sources provide opportunistic experiments with which to quantify the role of aerosols in modifying cloud properties. We review the current state of understanding on the influence of aerosol on climate built from the wide range of natural and anthropogenic laboratories investigated in recent decades.
Stefan Kinne, Peter North, Kevin Pearson, and Thomas Popp
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-954, https://doi.org/10.5194/acp-2021-954, 2021
Publication in ACP not foreseen
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To monitor aerosol properties and quantify aerosol climate impacts, ESA's Climate Change Initiative (CCI) supported the retrieval development for their dual-view sensors. Global maps of monthly AOD and AODf data are presented for 4 years: 1998 using ATSR-2, 2008 using AATSR and 2019 and 2020 using SLSTR sensor data. Application goals of this paper are to address decadal aerosol trends, to identify possible Covid-19 impacts in 2020 and to associate retrieved AOD with climate impacts.
Anu Kauppi, Antti Kukkurainen, Antti Lipponen, Marko Laine, Antti Arola, Hannakaisa Lindqvist, and Johanna Tamminen
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-328, https://doi.org/10.5194/amt-2021-328, 2021
Revised manuscript not accepted
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We present a methodology in Bayesian framework for retrieving atmospheric aerosol optical depth and aerosol type from the pre-computed look-up tables (LUTs). Especially, we consider Bayesian model averaging and uncertainty originating from aerosol model selection and imperfect forward modelling. Our aim is to get more realistic uncertainty estimates. We have applied the methodology to TROPOMI/S5P satellite observations and evaluated the results against ground-based data from the AERONET.
Xiaoxia Shang, Tero Mielonen, Antti Lipponen, Elina Giannakaki, Ari Leskinen, Virginie Buchard, Anton S. Darmenov, Antti Kukkurainen, Antti Arola, Ewan O'Connor, Anne Hirsikko, and Mika Komppula
Atmos. Meas. Tech., 14, 6159–6179, https://doi.org/10.5194/amt-14-6159-2021, https://doi.org/10.5194/amt-14-6159-2021, 2021
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The long-range-transported smoke particles from a Canadian wildfire event were observed with a multi-wavelength Raman polarization lidar and a ceilometer over Kuopio, Finland, in June 2019. The optical properties and the mass concentration estimations were reported for such aged smoke aerosols over northern Europe.
Marcin L. Witek, Michael J. Garay, David J. Diner, Michael A. Bull, Felix C. Seidel, Abigail M. Nastan, and Earl G. Hansen
Atmos. Meas. Tech., 14, 5577–5591, https://doi.org/10.5194/amt-14-5577-2021, https://doi.org/10.5194/amt-14-5577-2021, 2021
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This article documents the development and testing of a new near real-time (NRT) aerosol product from the MISR instrument on NASA’s Terra platform. The NRT product capitalizes on the unique attributes of the MISR retrieval approach, which leads to a high-quality and reliable aerosol data product. Several modifications are described that allow for rapid product generation within a 3 h window following acquisition. Implications for the product quality and consistency are discussed.
Antti Arola, William Wandji Nyamsi, Antti Lipponen, Stelios Kazadzis, Nickolay A. Krotkov, and Johanna Tamminen
Atmos. Meas. Tech., 14, 4947–4957, https://doi.org/10.5194/amt-14-4947-2021, https://doi.org/10.5194/amt-14-4947-2021, 2021
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Methods to estimate surface UV radiation from satellite measurements offer the only means to obtain global coverage, and the development of satellite-based UV algorithms has been ongoing since the early 1990s. One of the main challenges in this development has been how to account for the overall effect of absorption by atmospheric aerosols. One such method was suggested roughly a decade ago, and in this study we propose further improvements for this kind of approach.
Nick Schutgens, Oleg Dubovik, Otto Hasekamp, Omar Torres, Hiren Jethva, Peter J. T. Leonard, Pavel Litvinov, Jens Redemann, Yohei Shinozuka, Gerrit de Leeuw, Stefan Kinne, Thomas Popp, Michael Schulz, and Philip Stier
Atmos. Chem. Phys., 21, 6895–6917, https://doi.org/10.5194/acp-21-6895-2021, https://doi.org/10.5194/acp-21-6895-2021, 2021
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Absorptive aerosol has a potentially large impact on climate change. We evaluate and intercompare four global satellite datasets of absorptive aerosol optical depth (AAOD) and single-scattering albedo (SSA). We show that these datasets show reasonable correlations with the AErosol RObotic NETwork (AERONET) reference, although significant biases remain. In a follow-up paper we show that these observations nevertheless can be used for model evaluation.
Antti Lipponen, Ville Kolehmainen, Pekka Kolmonen, Antti Kukkurainen, Tero Mielonen, Neus Sabater, Larisa Sogacheva, Timo H. Virtanen, and Antti Arola
Atmos. Meas. Tech., 14, 2981–2992, https://doi.org/10.5194/amt-14-2981-2021, https://doi.org/10.5194/amt-14-2981-2021, 2021
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We have developed a new computational method to post-process-correct the satellite aerosol retrievals. The proposed method combines the conventional satellite aerosol retrievals relying on physics-based models and machine learning. The results show significantly improved accuracy in the aerosol data over the operational satellite data products. The correction can be applied to the existing satellite aerosol datasets with no need to fully reprocess the much larger original radiance data.
Cheng Chen, Oleg Dubovik, David Fuertes, Pavel Litvinov, Tatyana Lapyonok, Anton Lopatin, Fabrice Ducos, Yevgeny Derimian, Maurice Herman, Didier Tanré, Lorraine A. Remer, Alexei Lyapustin, Andrew M. Sayer, Robert C. Levy, N. Christina Hsu, Jacques Descloitres, Lei Li, Benjamin Torres, Yana Karol, Milagros Herrera, Marcos Herreras, Michael Aspetsberger, Moritz Wanzenboeck, Lukas Bindreiter, Daniel Marth, Andreas Hangler, and Christian Federspiel
Earth Syst. Sci. Data, 12, 3573–3620, https://doi.org/10.5194/essd-12-3573-2020, https://doi.org/10.5194/essd-12-3573-2020, 2020
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Aerosol products obtained from POLDER/PARASOL processed by the GRASP algorithm have been released. The entire archive of PARASOL/GRASP aerosol products is evaluated against AERONET and compared with MODIS (DT, DB and MAIAC), as well as PARASOL/Operational products. PARASOL/GRASP aerosol products provide spectral 443–1020 nm AOD correlating well with AERONET with a maximum bias of 0.02. Finally, GRASP shows capability to derive detailed spectral properties, including aerosol absorption.
Jane P. Mulcahy, Colin Johnson, Colin G. Jones, Adam C. Povey, Catherine E. Scott, Alistair Sellar, Steven T. Turnock, Matthew T. Woodhouse, Nathan Luke Abraham, Martin B. Andrews, Nicolas Bellouin, Jo Browse, Ken S. Carslaw, Mohit Dalvi, Gerd A. Folberth, Matthew Glover, Daniel P. Grosvenor, Catherine Hardacre, Richard Hill, Ben Johnson, Andy Jones, Zak Kipling, Graham Mann, James Mollard, Fiona M. O'Connor, Julien Palmiéri, Carly Reddington, Steven T. Rumbold, Mark Richardson, Nick A. J. Schutgens, Philip Stier, Marc Stringer, Yongming Tang, Jeremy Walton, Stephanie Woodward, and Andrew Yool
Geosci. Model Dev., 13, 6383–6423, https://doi.org/10.5194/gmd-13-6383-2020, https://doi.org/10.5194/gmd-13-6383-2020, 2020
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Aerosols are an important component of the Earth system. Here, we comprehensively document and evaluate the aerosol schemes as implemented in the physical and Earth system models, HadGEM3-GC3.1 and UKESM1. This study provides a useful characterisation of the aerosol climatology in both models, facilitating the understanding of the numerous aerosol–climate interaction studies that will be conducted for CMIP6 and beyond.
Johannes Quaas, Antti Arola, Brian Cairns, Matthew Christensen, Hartwig Deneke, Annica M. L. Ekman, Graham Feingold, Ann Fridlind, Edward Gryspeerdt, Otto Hasekamp, Zhanqing Li, Antti Lipponen, Po-Lun Ma, Johannes Mülmenstädt, Athanasios Nenes, Joyce E. Penner, Daniel Rosenfeld, Roland Schrödner, Kenneth Sinclair, Odran Sourdeval, Philip Stier, Matthias Tesche, Bastiaan van Diedenhoven, and Manfred Wendisch
Atmos. Chem. Phys., 20, 15079–15099, https://doi.org/10.5194/acp-20-15079-2020, https://doi.org/10.5194/acp-20-15079-2020, 2020
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Anthropogenic pollution particles – aerosols – serve as cloud condensation nuclei and thus increase cloud droplet concentration and the clouds' reflection of sunlight (a cooling effect on climate). This Twomey effect is poorly constrained by models and requires satellite data for better quantification. The review summarizes the challenges in properly doing so and outlines avenues for progress towards a better use of aerosol retrievals and better retrievals of droplet concentrations.
Marc Mallet, Fabien Solmon, Pierre Nabat, Nellie Elguindi, Fabien Waquet, Dominique Bouniol, Andrew Mark Sayer, Kerry Meyer, Romain Roehrig, Martine Michou, Paquita Zuidema, Cyrille Flamant, Jens Redemann, and Paola Formenti
Atmos. Chem. Phys., 20, 13191–13216, https://doi.org/10.5194/acp-20-13191-2020, https://doi.org/10.5194/acp-20-13191-2020, 2020
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This paper presents numerical simulations using two regional climate models to study the impact of biomass fire plumes from central Africa on the radiative balance of this region. The results indicate that biomass fires can either warm the regional climate when they are located above low clouds or cool it when they are located above land. They can also alter sea and land surface temperatures by decreasing solar radiation at the surface. Finally, they can also modify the atmospheric dynamics.
Nick Schutgens, Andrew M. Sayer, Andreas Heckel, Christina Hsu, Hiren Jethva, Gerrit de Leeuw, Peter J. T. Leonard, Robert C. Levy, Antti Lipponen, Alexei Lyapustin, Peter North, Thomas Popp, Caroline Poulsen, Virginia Sawyer, Larisa Sogacheva, Gareth Thomas, Omar Torres, Yujie Wang, Stefan Kinne, Michael Schulz, and Philip Stier
Atmos. Chem. Phys., 20, 12431–12457, https://doi.org/10.5194/acp-20-12431-2020, https://doi.org/10.5194/acp-20-12431-2020, 2020
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We intercompare 14 different datasets of satellite observations of aerosol. Such measurements are challenging but also provide the best opportunity to globally observe an atmospheric component strongly related to air pollution and climate change. Our study shows that most datasets perform similarly well on a global scale but that locally errors can be quite different. We develop a technique to estimate satellite errors everywhere, even in the absence of surface reference data.
Alexis Merlaud, Livio Belegante, Daniel-Eduard Constantin, Mirjam Den Hoed, Andreas Carlos Meier, Marc Allaart, Magdalena Ardelean, Maxim Arseni, Tim Bösch, Hugues Brenot, Andreea Calcan, Emmanuel Dekemper, Sebastian Donner, Steffen Dörner, Mariana Carmelia Balanica Dragomir, Lucian Georgescu, Anca Nemuc, Doina Nicolae, Gaia Pinardi, Andreas Richter, Adrian Rosu, Thomas Ruhtz, Anja Schönhardt, Dirk Schuettemeyer, Reza Shaiganfar, Kerstin Stebel, Frederik Tack, Sorin Nicolae Vâjâiac, Jeni Vasilescu, Jurgen Vanhamel, Thomas Wagner, and Michel Van Roozendael
Atmos. Meas. Tech., 13, 5513–5535, https://doi.org/10.5194/amt-13-5513-2020, https://doi.org/10.5194/amt-13-5513-2020, 2020
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The AROMAT campaigns took place in Romania in 2014 and 2015. They aimed to test airborne observation systems dedicated to air quality studies and to verify the concept of such campaigns in support of the validation of space-borne atmospheric missions. We show that airborne measurements of NO2 can be valuable for the validation of air quality satellites. For H2CO and SO2, the validation should involve ground-based measurement systems at key locations that the AROMAT measurements help identify.
Caroline A. Poulsen, Gregory R. McGarragh, Gareth E. Thomas, Martin Stengel, Matthew W. Christensen, Adam C. Povey, Simon R. Proud, Elisa Carboni, Rainer Hollmann, and Roy G. Grainger
Earth Syst. Sci. Data, 12, 2121–2135, https://doi.org/10.5194/essd-12-2121-2020, https://doi.org/10.5194/essd-12-2121-2020, 2020
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We have created a satellite cloud and radiation climatology from the ATSR-2 and AATSR on board ERS-2 and Envisat, respectively, which spans the period 1995–2012. The data set was created using a combination of optimal estimation and neural net techniques. The data set was created as part of the ESA Climate Change Initiative program. The data set has been compared with active CALIOP lidar measurements and compared with MAC-LWP AND CERES-EBAF measurements and is shown to have good performance.
Arve Kylling, Hamidreza Ardeshiri, Massimo Cassiani, Anna Solvejg Dinger, Soon-Young Park, Ignacio Pisso, Norbert Schmidbauer, Kerstin Stebel, and Andreas Stohl
Atmos. Meas. Tech., 13, 3303–3318, https://doi.org/10.5194/amt-13-3303-2020, https://doi.org/10.5194/amt-13-3303-2020, 2020
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Atmospheric turbulence and its effect on tracer dispersion in particular may be measured by cameras sensitive to the absorption of ultraviolet (UV) sunlight by sulfur dioxide (SO2). Using large eddy simulation and 3D Monte Carlo radiative transfer modelling of a SO2 plume, we demonstrate that UV camera images of SO2 plumes may be used to derive plume statistics of relevance for the study of atmospheric turbulent dispersion.
William Wandji Nyamsi, Antti Lipponen, Arturo Sanchez-Lorenzo, Martin Wild, and Antti Arola
Atmos. Meas. Tech., 13, 3061–3079, https://doi.org/10.5194/amt-13-3061-2020, https://doi.org/10.5194/amt-13-3061-2020, 2020
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This paper proposes a novel and accurate method for estimating and reconstructing aerosol optical depth from sunshine duration measurements under cloud-free conditions at any place and time since the late 19th century. The method performs very well when compared to AErosol RObotic NETwork measurements and operates an efficient detection of signals from massive volcanic eruptions. Reconstructed long-term aerosol optical depths are in agreement with the dimming/brightening phenomenon.
Larisa Sogacheva, Thomas Popp, Andrew M. Sayer, Oleg Dubovik, Michael J. Garay, Andreas Heckel, N. Christina Hsu, Hiren Jethva, Ralph A. Kahn, Pekka Kolmonen, Miriam Kosmale, Gerrit de Leeuw, Robert C. Levy, Pavel Litvinov, Alexei Lyapustin, Peter North, Omar Torres, and Antti Arola
Atmos. Chem. Phys., 20, 2031–2056, https://doi.org/10.5194/acp-20-2031-2020, https://doi.org/10.5194/acp-20-2031-2020, 2020
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The typical lifetime of a single satellite platform is on the order of 5–15 years; thus, for climate studies the usage of multiple satellite sensors should be considered.
Here we introduce and evaluate a monthly AOD merged product and AOD global and regional time series for the period 1995–2017 created from 12 individual satellite AOD products, which provide a long-term perspective on AOD changes over different regions of the globe.
Giulia Saponaro, Moa K. Sporre, David Neubauer, Harri Kokkola, Pekka Kolmonen, Larisa Sogacheva, Antti Arola, Gerrit de Leeuw, Inger H. H. Karset, Ari Laaksonen, and Ulrike Lohmann
Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, https://doi.org/10.5194/acp-20-1607-2020, 2020
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The understanding of cloud processes is based on the quality of the representation of cloud properties. We compared cloud parameters from three models with satellite observations. We report on the performance of each data source, highlighting strengths and deficiencies, which should be considered when deriving the effect of aerosols on cloud properties.
Michael J. Garay, Marcin L. Witek, Ralph A. Kahn, Felix C. Seidel, James A. Limbacher, Michael A. Bull, David J. Diner, Earl G. Hansen, Olga V. Kalashnikova, Huikyo Lee, Abigail M. Nastan, and Yan Yu
Atmos. Meas. Tech., 13, 593–628, https://doi.org/10.5194/amt-13-593-2020, https://doi.org/10.5194/amt-13-593-2020, 2020
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The Multi-angle Imaging SpectroRadiometer (MISR) instrument has been operational since early 2000, creating an extensive data set of global Earth observations. Here we introduce the latest version (V23) of the MISR aerosol products, which is reported on a 4.4 km spatial grid and contains retrieved aerosol optical depth and aerosol particle property information derived over both land and water. The changes implemented in V23 have significant impacts on the data product and its interpretation.
Zachary Fasnacht, Alexander Vasilkov, David Haffner, Wenhan Qin, Joanna Joiner, Nickolay Krotkov, Andrew M. Sayer, and Robert Spurr
Atmos. Meas. Tech., 12, 6749–6769, https://doi.org/10.5194/amt-12-6749-2019, https://doi.org/10.5194/amt-12-6749-2019, 2019
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The anisotropy of Earth's surface reflection plays an important role in satellite-based retrievals of cloud, aerosol, and trace gases. Most current ultraviolet and visible satellite retrievals utilize climatological surface reflectivity databases that do not account for surface anisotropy. The GLER concept was introduced to account for such features. Here we evaluate GLER for water surfaces by comparing with OMI measurements and show that it captures these surface anisotropy features.
Andrew M. Sayer and Kirk D. Knobelspiesse
Atmos. Chem. Phys., 19, 15023–15048, https://doi.org/10.5194/acp-19-15023-2019, https://doi.org/10.5194/acp-19-15023-2019, 2019
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Data about the Earth are routinely obtained from satellite observations, model simulations, and ground-based or other measurements. These are at different space and timescales, and it is common to average them to reduce gaps and increase ease of use. The question of how the data should be averaged depends on the underlying distribution of the quantity. This study presents a method for determining how to appropriately aggregate data and applies it to data sets about atmospheric aerosol levels.
Santtu Mikkonen, Mikko R. A. Pitkänen, Tuomo Nieminen, Antti Lipponen, Sini Isokääntä, Antti Arola, and Kari E. J. Lehtinen
Atmos. Chem. Phys., 19, 12531–12543, https://doi.org/10.5194/acp-19-12531-2019, https://doi.org/10.5194/acp-19-12531-2019, 2019
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Atmospheric measurement data never come without measurement error. Too often, these errors are neglected when researchers make inferences from their data. We applied multiple line-fitting methods to simulated data mimicking two central variables in aerosol research. Our results show that an ordinary least squares fit, typically used to describe relationships, underestimates the slope of the fit and that methods taking the measurement uncertainty into account performed significantly better.
Olli-Pekka Tikkanen, Väinö Hämäläinen, Grazia Rovelli, Antti Lipponen, Manabu Shiraiwa, Jonathan P. Reid, Kari E. J. Lehtinen, and Taina Yli-Juuti
Atmos. Chem. Phys., 19, 9333–9350, https://doi.org/10.5194/acp-19-9333-2019, https://doi.org/10.5194/acp-19-9333-2019, 2019
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We assessed how well the organic aerosol particle composition and viscosity can be captured by optimizing process models to match particle evaporation data. We performed the analysis for both artificial and real evaporation data and tested two optimization algorithms. Our findings show that the optimization method yields a good estimate for the studied properties. The timescale of the evaporation data and particle size was found to be important in identifying the volatility of organic compounds.
Andrew M. Sayer, N. Christina Hsu, Jaehwa Lee, Woogyung V. Kim, Sharon Burton, Marta A. Fenn, Richard A. Ferrare, Meloë Kacenelenbogen, Samuel LeBlanc, Kristina Pistone, Jens Redemann, Michal Segal-Rozenhaimer, Yohei Shinozuka, and Si-Chee Tsay
Atmos. Meas. Tech., 12, 3595–3627, https://doi.org/10.5194/amt-12-3595-2019, https://doi.org/10.5194/amt-12-3595-2019, 2019
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Aerosols are small particles in the atmosphere such as dust or smoke. They are routinely monitored by satellites due to their importance for climate and air quality. However aerosols above clouds are more difficult to monitor. This study describes an improvement to a technique to monitor light-absorbing aerosols above clouds from four Earth-orbiting satellite instruments. The improved method is evaluated using data from the ORACLES field campaign, which measured these aerosols from aircraft.
Marc Mallet, Pierre Nabat, Paquita Zuidema, Jens Redemann, Andrew Mark Sayer, Martin Stengel, Sebastian Schmidt, Sabrina Cochrane, Sharon Burton, Richard Ferrare, Kerry Meyer, Pablo Saide, Hiren Jethva, Omar Torres, Robert Wood, David Saint Martin, Romain Roehrig, Christina Hsu, and Paola Formenti
Atmos. Chem. Phys., 19, 4963–4990, https://doi.org/10.5194/acp-19-4963-2019, https://doi.org/10.5194/acp-19-4963-2019, 2019
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The model is able to represent LWP but not the LCF. AOD is consistent over the continent but also over ocean (ACAOD). Differences are observed in SSA due to the absence of internal mixing in ALADIN-Climate. A significant regional gradient of the forcing at TOA is observed. An intense positive forcing is simulated over Gabon. Results highlight the significant effect of enhanced moisture on BBA extinction. The surface dimming modifies the energy budget.
Marta Luffarelli and Yves Govaerts
Atmos. Meas. Tech., 12, 791–809, https://doi.org/10.5194/amt-12-791-2019, https://doi.org/10.5194/amt-12-791-2019, 2019
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This paper presents the application of the CISAR (Combined Inversion of Surface and AeRosol) algorithm on both geostationary (SEVIRI) and polar-orbiting (Proba-V) satellite data. This paper analyses the information content associated with the observations acquired by these two instruments and its implication on the joint retrieval of surface and atmospheric properties. CISAR-retrieved aerosol properties and surface albedo are compared with the AERONET and MODIS data respectively.
Nikolaos Evangeliou, Arve Kylling, Sabine Eckhardt, Viktor Myroniuk, Kerstin Stebel, Ronan Paugam, Sergiy Zibtsev, and Andreas Stohl
Atmos. Chem. Phys., 19, 1393–1411, https://doi.org/10.5194/acp-19-1393-2019, https://doi.org/10.5194/acp-19-1393-2019, 2019
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We simulated the peatland fires that burned in Greenland in summer 2017. Using satellite data, we estimated that the total burned area was 2345 ha, the fuel amount consumed 117 kt C and the emissions of BC, OC and BrC 23.5, 731 and 141 t, respectively. About 30 % of the emissions were deposited on snow or ice surfaces. This caused a maximum albedo change of 0.007 and a surface radiative forcing of 0.03–0.04 W m−2, with local maxima of up to 0.63–0.77 W m−2. Overall, the fires had a small impact.
J. Christopher Kaiser, Johannes Hendricks, Mattia Righi, Patrick Jöckel, Holger Tost, Konrad Kandler, Bernadett Weinzierl, Daniel Sauer, Katharina Heimerl, Joshua P. Schwarz, Anne E. Perring, and Thomas Popp
Geosci. Model Dev., 12, 541–579, https://doi.org/10.5194/gmd-12-541-2019, https://doi.org/10.5194/gmd-12-541-2019, 2019
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The implementation of the aerosol microphysics submodel MADE3 into the global atmospheric chemistry model EMAC is described and evaluated against an extensive pool of observational data, focusing on aerosol mass and number concentrations, size distributions, composition, and optical properties. EMAC (MADE3) is able to reproduce main aerosol properties reasonably well, in line with the performance of other global aerosol models.
Yves Govaerts and Marta Luffarelli
Atmos. Meas. Tech., 11, 6589–6603, https://doi.org/10.5194/amt-11-6589-2018, https://doi.org/10.5194/amt-11-6589-2018, 2018
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This paper presents a new algorithm for the joint retrieval of surface reflectance and aerosol properties with continuous variations of the state variables in the solution space. This algorithm, named CISAR (Combined Inversion of Surface and AeRosol), relies on a simple atmospheric vertical structure composed of two layers and an underlying surface. Surface anisotropic reflectance effects are taken into account and radiatively coupled with atmospheric scattering.
Larisa Sogacheva, Edith Rodriguez, Pekka Kolmonen, Timo H. Virtanen, Giulia Saponaro, Gerrit de Leeuw, Aristeidis K. Georgoulias, Georgia Alexandri, Konstantinos Kourtidis, and Ronald J. van der A
Atmos. Chem. Phys., 18, 16631–16652, https://doi.org/10.5194/acp-18-16631-2018, https://doi.org/10.5194/acp-18-16631-2018, 2018
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Understanding long-term trends in aerosol optical density (AOD) is essential for evaluating health and climate effects and the effectiveness of pollution control policies. A method to construct a combined AOD long time series (1995-2017) using ATSR and MODIS spaceborne instruments is introduced. The effect of changes in the emission regulation policy in China is seen in a gradual AOD decrease after 2011. The effect is more visible in highly populated and industrialized areas in southeast China.
Anna Solvejg Dinger, Kerstin Stebel, Massimo Cassiani, Hamidreza Ardeshiri, Cirilo Bernardo, Arve Kylling, Soon-Young Park, Ignacio Pisso, Norbert Schmidbauer, Jan Wasseng, and Andreas Stohl
Atmos. Meas. Tech., 11, 6169–6188, https://doi.org/10.5194/amt-11-6169-2018, https://doi.org/10.5194/amt-11-6169-2018, 2018
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This study presents an artificial release experiment aimed to improve the understanding of turbulence in the atmospheric boundary layer. A new set of image processing methods was developed to analyse the turbulent dispersion of sulfur dioxide (SO2) puffs. For this a tomographic setup of six SO2 cameras was used to image artificially released SO2 gas.
Harri Kokkola, Thomas Kühn, Anton Laakso, Tommi Bergman, Kari E. J. Lehtinen, Tero Mielonen, Antti Arola, Scarlet Stadtler, Hannele Korhonen, Sylvaine Ferrachat, Ulrike Lohmann, David Neubauer, Ina Tegen, Colombe Siegenthaler-Le Drian, Martin G. Schultz, Isabelle Bey, Philip Stier, Nikos Daskalakis, Colette L. Heald, and Sami Romakkaniemi
Geosci. Model Dev., 11, 3833–3863, https://doi.org/10.5194/gmd-11-3833-2018, https://doi.org/10.5194/gmd-11-3833-2018, 2018
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In this paper we present a global aerosol–chemistry–climate model with the focus on its representation for atmospheric aerosol particles. In the model, aerosols are simulated using the aerosol module SALSA2.0, which in this paper is compared to satellite, ground, and aircraft-based observations of the properties of atmospheric aerosol. Based on this study, the model simulated aerosol properties compare well with the observations.
Larisa Sogacheva, Gerrit de Leeuw, Edith Rodriguez, Pekka Kolmonen, Aristeidis K. Georgoulias, Georgia Alexandri, Konstantinos Kourtidis, Emmanouil Proestakis, Eleni Marinou, Vassilis Amiridis, Yong Xue, and Ronald J. van der A
Atmos. Chem. Phys., 18, 11389–11407, https://doi.org/10.5194/acp-18-11389-2018, https://doi.org/10.5194/acp-18-11389-2018, 2018
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Using AATSR ADV (1995–2011) and MODIS C6.1 (2000–2017) annual and seasonal aerosol optical depth (AOD) aggregates, we obtained information regarding the occurrence of aerosols and their spatial and temporal variation over China. We specifically focused on regional differences in annual and seasonal AOD behavior for selected regions. AOD dataset comparisons, validation results and AOD tendencies during the overlapping period (2000–2011) are discussed.
Angela Benedetti, Jeffrey S. Reid, Peter Knippertz, John H. Marsham, Francesca Di Giuseppe, Samuel Rémy, Sara Basart, Olivier Boucher, Ian M. Brooks, Laurent Menut, Lucia Mona, Paolo Laj, Gelsomina Pappalardo, Alfred Wiedensohler, Alexander Baklanov, Malcolm Brooks, Peter R. Colarco, Emilio Cuevas, Arlindo da Silva, Jeronimo Escribano, Johannes Flemming, Nicolas Huneeus, Oriol Jorba, Stelios Kazadzis, Stefan Kinne, Thomas Popp, Patricia K. Quinn, Thomas T. Sekiyama, Taichu Tanaka, and Enric Terradellas
Atmos. Chem. Phys., 18, 10615–10643, https://doi.org/10.5194/acp-18-10615-2018, https://doi.org/10.5194/acp-18-10615-2018, 2018
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Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation authorities, solar energy plant managers, climate service providers, and health professionals. This paper describes the advances in the field and sets out requirements for observations for the sustainability of these activities.
Gregory R. McGarragh, Caroline A. Poulsen, Gareth E. Thomas, Adam C. Povey, Oliver Sus, Stefan Stapelberg, Cornelia Schlundt, Simon Proud, Matthew W. Christensen, Martin Stengel, Rainer Hollmann, and Roy G. Grainger
Atmos. Meas. Tech., 11, 3397–3431, https://doi.org/10.5194/amt-11-3397-2018, https://doi.org/10.5194/amt-11-3397-2018, 2018
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Satellites are vital for measuring cloud properties necessary for climate prediction studies. We present a method to retrieve cloud properties from satellite based radiometric measurements. The methodology employed is known as optimal estimation and belongs in the class of statistical inversion methods based on Bayes' theorem. We show, through theoretical retrieval simulations, that the solution is stable and accurate to within 10–20% depending on cloud thickness.
Oliver Sus, Martin Stengel, Stefan Stapelberg, Gregory McGarragh, Caroline Poulsen, Adam C. Povey, Cornelia Schlundt, Gareth Thomas, Matthew Christensen, Simon Proud, Matthias Jerg, Roy Grainger, and Rainer Hollmann
Atmos. Meas. Tech., 11, 3373–3396, https://doi.org/10.5194/amt-11-3373-2018, https://doi.org/10.5194/amt-11-3373-2018, 2018
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This paper presents a new cloud detection and classification framework, CC4CL. It applies a sophisticated optimal estimation method to derive cloud variables from satellite data of various polar-orbiting platforms and sensors (AVHRR, MODIS, AATSR). CC4CL provides explicit uncertainty quantification and long-term consistency for decadal timeseries at various spatial resolutions. We analysed 5 case studies to show that cloud height estimates are very realistic unless optically thin clouds overlap.
Falguni Patadia, Robert C. Levy, and Shana Mattoo
Atmos. Meas. Tech., 11, 3205–3219, https://doi.org/10.5194/amt-11-3205-2018, https://doi.org/10.5194/amt-11-3205-2018, 2018
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Satellite-measured radiance from an Earth scene comprises light scattered and absorbed by gases, clouds and aerosols in the atmosphere and by the Earth surface. To retrieve aerosol information, the signal from clouds, gases and the surface must be separated from the aerosol signal. This paper highlights the gas absorption correction method used by the MODIS dark-target aerosol retrieval algorithm and demonstrates that aerosol retrieval accuracy depends on accurate gas absorption correction.
Arve Kylling, Sophie Vandenbussche, Virginie Capelle, Juan Cuesta, Lars Klüser, Luca Lelli, Thomas Popp, Kerstin Stebel, and Pepijn Veefkind
Atmos. Meas. Tech., 11, 2911–2936, https://doi.org/10.5194/amt-11-2911-2018, https://doi.org/10.5194/amt-11-2911-2018, 2018
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The aerosol layer height is one of four aerosol parameters which is needed to enhance our understanding of aerosols' role in the climate system. Both active and passive measurement methods may be used to estimate the aerosol layer height. Aerosol height estimates made from passive infrared and solar satellite sensors measurements are compared with satellite-borne lidar estimates. There is considerable variation between the retrieved dust heights and how they compare with the lidar.
Antti Lipponen, Tero Mielonen, Mikko R. A. Pitkänen, Robert C. Levy, Virginia R. Sawyer, Sami Romakkaniemi, Ville Kolehmainen, and Antti Arola
Atmos. Meas. Tech., 11, 1529–1547, https://doi.org/10.5194/amt-11-1529-2018, https://doi.org/10.5194/amt-11-1529-2018, 2018
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Atmospheric aerosols are small solid or liquid particles suspended in the atmosphere and they have a significant effect on the climate. Satellite data are used to get global estimates of atmospheric aerosols. In this work, a statistics-based Bayesian aerosol retrieval algorithm was developed to improve the accuracy and quantify the uncertainties related to the aerosol estimates. The algorithm is tested with NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data.
Anders V. Lindfors, Jukka Kujanpää, Niilo Kalakoski, Anu Heikkilä, Kaisa Lakkala, Tero Mielonen, Maarten Sneep, Nickolay A. Krotkov, Antti Arola, and Johanna Tamminen
Atmos. Meas. Tech., 11, 997–1008, https://doi.org/10.5194/amt-11-997-2018, https://doi.org/10.5194/amt-11-997-2018, 2018
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This paper describes the algorithm that will be used for estimating surface UV radiation from TROPOMI (TROPOspheric Monitoring Instrument) measurements. TROPOMI is the only payload of the Sentinel-5 Precursor (S5P), which is a polar-orbiting satellite mission of the European Space Agency (ESA). The presented algorithm has been tested using input based on previous satellite measurements. These preliminary results indicate that the algorithm is functioning according to expectations.
Timo H. Virtanen, Pekka Kolmonen, Larisa Sogacheva, Edith Rodríguez, Giulia Saponaro, and Gerrit de Leeuw
Atmos. Meas. Tech., 11, 925–938, https://doi.org/10.5194/amt-11-925-2018, https://doi.org/10.5194/amt-11-925-2018, 2018
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We study the collocation mismatch uncertainty related to validating coarse-resolution satellite-based aerosol data against point-like ground based measurements. We use the spatial variability in the satellite data to estimate the upper limit for the uncertainty and study the effect of sampling parameters in the validation. We find that accounting for the collocation mismatch uncertainty increases the fraction of consistent data in the validation.
Jonas Gliß, Kerstin Stebel, Arve Kylling, and Aasmund Sudbø
Atmos. Meas. Tech., 11, 781–801, https://doi.org/10.5194/amt-11-781-2018, https://doi.org/10.5194/amt-11-781-2018, 2018
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The paper focusses on gas-velocity retrievals in emission plumes using optical flow (OF) algorithms applied to remote sensing imagery. OF algorithms can measure the velocities on a pixel level between consecutive images. An issue of OF algorithms is that they often fail to detect motion in contrast-poor image areas. A correction based on histograms of an OF vector field is proposed. The new method is applied to two example volcanic data sets from Mt Etna, Italy and Guallatiri, Chile.
Marcin L. Witek, Michael J. Garay, David J. Diner, Michael A. Bull, and Felix C. Seidel
Atmos. Meas. Tech., 11, 429–439, https://doi.org/10.5194/amt-11-429-2018, https://doi.org/10.5194/amt-11-429-2018, 2018
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This study outlines a new methodology for assessing air pollution from space using observations from Multi-angle Imaging SpectroRadiometer (MISR). Both air pollution amounts – as well as the uncertainties associated with space observations – are simultaneously and consistently obtained from MISR measurements over oceans. The new products have superior quality to the previous versions and will benefit the air pollution community.
Martin Stengel, Stefan Stapelberg, Oliver Sus, Cornelia Schlundt, Caroline Poulsen, Gareth Thomas, Matthew Christensen, Cintia Carbajal Henken, Rene Preusker, Jürgen Fischer, Abhay Devasthale, Ulrika Willén, Karl-Göran Karlsson, Gregory R. McGarragh, Simon Proud, Adam C. Povey, Roy G. Grainger, Jan Fokke Meirink, Artem Feofilov, Ralf Bennartz, Jedrzej S. Bojanowski, and Rainer Hollmann
Earth Syst. Sci. Data, 9, 881–904, https://doi.org/10.5194/essd-9-881-2017, https://doi.org/10.5194/essd-9-881-2017, 2017
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We present new cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS. Retrieval systems were developed that include cloud detection and cloud typing followed by optimal estimation retrievals of cloud properties (e.g. cloud-top pressure, effective radius, optical thickness, water path). Special features of all datasets are spectral consistency and rigorous uncertainty propagation from pixel-level data to monthly properties.
Maria Filioglou, Anna Nikandrova, Sami Niemelä, Holger Baars, Tero Mielonen, Ari Leskinen, David Brus, Sami Romakkaniemi, Elina Giannakaki, and Mika Komppula
Atmos. Meas. Tech., 10, 4303–4316, https://doi.org/10.5194/amt-10-4303-2017, https://doi.org/10.5194/amt-10-4303-2017, 2017
Matthew W. Christensen, David Neubauer, Caroline A. Poulsen, Gareth E. Thomas, Gregory R. McGarragh, Adam C. Povey, Simon R. Proud, and Roy G. Grainger
Atmos. Chem. Phys., 17, 13151–13164, https://doi.org/10.5194/acp-17-13151-2017, https://doi.org/10.5194/acp-17-13151-2017, 2017
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The cloud-aerosol pairing algorithm (CAPA) is developed to quantify the impact of near-cloud aerosol retrievals on satellite-based aerosol–cloud statistical relationships. We find that previous satellite-based radiative forcing estimates of aerosol–cloud interactions represented in key climate reports are likely exaggerated by up to 50 % due to including retrieval artefacts in the aerosols located near clouds. It is demonstrated that this retrieval artefact can be corrected in current products.
Anu Kauppi, Pekka Kolmonen, Marko Laine, and Johanna Tamminen
Atmos. Meas. Tech., 10, 4079–4098, https://doi.org/10.5194/amt-10-4079-2017, https://doi.org/10.5194/amt-10-4079-2017, 2017
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The paper focuses on the aerosol microphysical model selection and characterisation of uncertainty in the retrieved aerosol type and aerosol optical depth (AOD). The proposed method is based on Bayesian inference approach and can account for the model error and also include the model selection uncertainty in the total uncertainty budget. The method is applied to OMI measurements but is also applicable to other instruments. The retrieval was evaluated by comparison with ground-based measurements.
Christopher J. Merchant, Frank Paul, Thomas Popp, Michael Ablain, Sophie Bontemps, Pierre Defourny, Rainer Hollmann, Thomas Lavergne, Alexandra Laeng, Gerrit de Leeuw, Jonathan Mittaz, Caroline Poulsen, Adam C. Povey, Max Reuter, Shubha Sathyendranath, Stein Sandven, Viktoria F. Sofieva, and Wolfgang Wagner
Earth Syst. Sci. Data, 9, 511–527, https://doi.org/10.5194/essd-9-511-2017, https://doi.org/10.5194/essd-9-511-2017, 2017
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Climate data records (CDRs) contain data describing Earth's climate and should address uncertainty in the data to communicate what is known about climate variability or change and what range of doubt exists. This paper discusses good practice for including uncertainty information in CDRs for the essential climate variables (ECVs) derived from satellite data. Recommendations emerge from the shared experience of diverse ECV projects within the European Space Agency Climate Change Initiative.
Andrew M. Sayer, N. Christina Hsu, Corey Bettenhausen, Robert E. Holz, Jaehwa Lee, Greg Quinn, and Paolo Veglio
Atmos. Meas. Tech., 10, 1425–1444, https://doi.org/10.5194/amt-10-1425-2017, https://doi.org/10.5194/amt-10-1425-2017, 2017
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The satellite instrument VIIRS is being used to carry on observations of the Earth made by older satellites like MODIS. Data sets created from these satellite observations depend on the quality of the satellite instruments' calibration. This paper describes a comparison between the calibration of these two sensors. MODIS is believed to be more reliable and so VIIRS is corrected to bring it in line with MODIS. These corrections are shown to improve the quality of VIIRS aerosol data.
Giulia Saponaro, Pekka Kolmonen, Larisa Sogacheva, Edith Rodriguez, Timo Virtanen, and Gerrit de Leeuw
Atmos. Chem. Phys., 17, 3133–3143, https://doi.org/10.5194/acp-17-3133-2017, https://doi.org/10.5194/acp-17-3133-2017, 2017
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The effect of aerosol upon cloud properties is studied over the Baltic Sea region, which presents a distinct contrast of aerosol loading between the clean Fennoscandia and the polluted area of central–eastern Europe. Statistically significant positive values are found over the Baltic Sea and Fennoscandia, while negative values are found over central–eastern Europe, contradicting the theory of aerosol indirect effect on clouds.
Larisa Sogacheva, Pekka Kolmonen, Timo H. Virtanen, Edith Rodriguez, Giulia Saponaro, and Gerrit de Leeuw
Atmos. Meas. Tech., 10, 491–505, https://doi.org/10.5194/amt-10-491-2017, https://doi.org/10.5194/amt-10-491-2017, 2017
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Clouds reflect solar light much more strongly than aerosol particles. Therefore, the retrieval of aerosol optical depth is usually only attempted over cloud-free areas. A very strict cloud detection scheme has to be applied to remove all cloudy pixels. However, often not all clouds are detected. To remove possibly cloud-contaminated pixels, a cloud post-processing algorithm has been designed, which effectively solves the problem and results in smoother AOD maps and improved validation results.
Tero Mielonen, Anca Hienola, Thomas Kühn, Joonas Merikanto, Antti Lipponen, Tommi Bergman, Hannele Korhonen, Pekka Kolmonen, Larisa Sogacheva, Darren Ghent, Antti Arola, Gerrit de Leeuw, and Harri Kokkola
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-625, https://doi.org/10.5194/acp-2016-625, 2016
Revised manuscript not accepted
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We studied the temperature dependence of AOD and its radiative effects over the southeastern US. We used spaceborne observations of AOD, LST and tropospheric NO2 with simulations of ECHAM-HAMMOZ. The level of AOD in this region is governed by anthropogenic emissions but the temperature dependency is most likely caused by BVOC emissions. According to the observations and simulations, the regional clear-sky DRE for biogenic aerosols is −0.43 ± 0.88 W/m2/K and −0.86 ± 0.06 W/m2/K, respectively.
Jani Huttunen, Harri Kokkola, Tero Mielonen, Mika Esa Juhani Mononen, Antti Lipponen, Juha Reunanen, Anders Vilhelm Lindfors, Santtu Mikkonen, Kari Erkki Juhani Lehtinen, Natalia Kouremeti, Alkiviadis Bais, Harri Niska, and Antti Arola
Atmos. Chem. Phys., 16, 8181–8191, https://doi.org/10.5194/acp-16-8181-2016, https://doi.org/10.5194/acp-16-8181-2016, 2016
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For a good estimate of the current forcing by anthropogenic aerosols, knowledge in past is needed. One option to lengthen time series is to retrieve aerosol optical depth from solar radiation measurements. We have evaluated several methods for this task. Most of the methods produce aerosol optical depth estimates with a good accuracy. However, machine learning methods seem to be the most applicable not to produce any systematic biases, since they do not need constrain the aerosol properties.
Daan Hubert, Jean-Christopher Lambert, Tijl Verhoelst, José Granville, Arno Keppens, Jean-Luc Baray, Adam E. Bourassa, Ugo Cortesi, Doug A. Degenstein, Lucien Froidevaux, Sophie Godin-Beekmann, Karl W. Hoppel, Bryan J. Johnson, Erkki Kyrölä, Thierry Leblanc, Günter Lichtenberg, Marion Marchand, C. Thomas McElroy, Donal Murtagh, Hideaki Nakane, Thierry Portafaix, Richard Querel, James M. Russell III, Jacobo Salvador, Herman G. J. Smit, Kerstin Stebel, Wolfgang Steinbrecht, Kevin B. Strawbridge, René Stübi, Daan P. J. Swart, Ghassan Taha, David W. Tarasick, Anne M. Thompson, Joachim Urban, Joanna A. E. van Gijsel, Roeland Van Malderen, Peter von der Gathen, Kaley A. Walker, Elian Wolfram, and Joseph M. Zawodny
Atmos. Meas. Tech., 9, 2497–2534, https://doi.org/10.5194/amt-9-2497-2016, https://doi.org/10.5194/amt-9-2497-2016, 2016
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A more detailed understanding of satellite O3 profile data records is vital for further progress in O3 research. To this end, we made a comprehensive assessment of 14 limb/occultation profilers using ground-based reference data. The mutual consistency of satellite O3 in terms of bias, short-term variability and decadal stability is generally good over most of the stratosphere. However, we identified some exceptions that impact the quality of recently merged data sets and ozone trend assessments.
A. M. Sayer, N. C. Hsu, and C. Bettenhausen
Atmos. Meas. Tech., 8, 5277–5288, https://doi.org/10.5194/amt-8-5277-2015, https://doi.org/10.5194/amt-8-5277-2015, 2015
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MODIS is a satellite sensor widely used in Earth science. Its scanning geometry results in a distortion called the ‘bow-tie effect’, which means that, depending on the location of a pixel relative to the satellite ground track, the size and shape of the pixel may be distorted. This affects data such as aerosol optical depth (AOD) derived from the measurements. This paper illustrates the bow-tie disortion’s effect on AOD and presents techniques to restore AOD data products to a more uniform grid
I. B. Konovalov, M. Beekmann, E. V. Berezin, H. Petetin, T. Mielonen, I. N. Kuznetsova, and M. O. Andreae
Atmos. Chem. Phys., 15, 13269–13297, https://doi.org/10.5194/acp-15-13269-2015, https://doi.org/10.5194/acp-15-13269-2015, 2015
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(1) The mesoscale evolution of aerosol from open biomass burning (BB) has been successfully simulated using the volatility basis set (VBS) framework. (2) The simulations disregarding semivolatile nature of organic compounds forming BB aerosol are found to be inconsistent with measurements in the region and period affected by the Russian 2010 wildfires. (3) The VBS method enables one to improve the consistency of "top-down" and "bottom-up" estimates of BB aerosol emissions.
M. J. Wooster, G. Roberts, P. H. Freeborn, W. Xu, Y. Govaerts, R. Beeby, J. He, A. Lattanzio, D. Fisher, and R. Mullen
Atmos. Chem. Phys., 15, 13217–13239, https://doi.org/10.5194/acp-15-13217-2015, https://doi.org/10.5194/acp-15-13217-2015, 2015
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Landscape fires strongly influence atmospheric chemistry, composition, and climate. Characterizing such fires at very high temporal resolution is best achieved using thermal observations of actively burning fires made from geostationary Earth Observation satellites. Here we detail the Fire Radiative Power (FRP) products generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from data collected by the Meteosat geostationary satellites.
A. Arola, G. L. Schuster, M. R. A. Pitkänen, O. Dubovik, H. Kokkola, A. V. Lindfors, T. Mielonen, T. Raatikainen, S. Romakkaniemi, S. N. Tripathi, and H. Lihavainen
Atmos. Chem. Phys., 15, 12731–12740, https://doi.org/10.5194/acp-15-12731-2015, https://doi.org/10.5194/acp-15-12731-2015, 2015
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There have been relatively few measurement-based estimates for the direct radiative effect of brown carbon so far. This is first time that the direct radiative effect of brown carbon is estimated by exploiting the AERONET-retrieved imaginary indices. We estimated it for four sites in the Indo-Gangetic Plain: Karachi, Lahore,
Kanpur and Gandhi College.
A. C. Povey and R. G. Grainger
Atmos. Meas. Tech., 8, 4699–4718, https://doi.org/10.5194/amt-8-4699-2015, https://doi.org/10.5194/amt-8-4699-2015, 2015
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Clear communication of the uncertainty on data is necessary for users to make appropriate use of it. This paper discusses the representation of uncertainty in satellite observations of the environment, arguing that the dominant sources of error are assumptions made during data analysis. The resulting uncertainty may be more usefully represented using ensemble techniques (a set of analyses using different assumptions to illustrate their impact) than with traditional statistical metrics.
R. C. Levy, L. A. Munchak, S. Mattoo, F. Patadia, L. A. Remer, and R. E. Holz
Atmos. Meas. Tech., 8, 4083–4110, https://doi.org/10.5194/amt-8-4083-2015, https://doi.org/10.5194/amt-8-4083-2015, 2015
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Aerosol optical depth (AOD) is an essential climate variable, so we seek to create a long-term AOD record. From MODIS, we have 15+ years, which we want to continue with VIIRS. Accounting for instrumental difference, we have developed a MODIS-like algorithm for VIIRS, and applied it to overlapping 2-year time period. In general, the two data sets are similar, except for VIIRS being high-biased over ocean. We discuss the impacts of calibration, resolution, and sampling on the results.
M. Beekmann, A. S. H. Prévôt, F. Drewnick, J. Sciare, S. N. Pandis, H. A. C. Denier van der Gon, M. Crippa, F. Freutel, L. Poulain, V. Ghersi, E. Rodriguez, S. Beirle, P. Zotter, S.-L. von der Weiden-Reinmüller, M. Bressi, C. Fountoukis, H. Petetin, S. Szidat, J. Schneider, A. Rosso, I. El Haddad, A. Megaritis, Q. J. Zhang, V. Michoud, J. G. Slowik, S. Moukhtar, P. Kolmonen, A. Stohl, S. Eckhardt, A. Borbon, V. Gros, N. Marchand, J. L. Jaffrezo, A. Schwarzenboeck, A. Colomb, A. Wiedensohler, S. Borrmann, M. Lawrence, A. Baklanov, and U. Baltensperger
Atmos. Chem. Phys., 15, 9577–9591, https://doi.org/10.5194/acp-15-9577-2015, https://doi.org/10.5194/acp-15-9577-2015, 2015
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A detailed characterization of air quality in the Paris (France) agglomeration, a megacity, during two summer and winter intensive campaigns and from additional 1-year observations, revealed that about 70% of the fine particulate matter (PM) at urban background is transported into the megacity from upwind regions. Unexpectedly, a major part of organic PM is of modern origin (woodburning and cooking activities, secondary formation from biogenic VOC).
E. Rodríguez, P. Kolmonen, T. H. Virtanen, L. Sogacheva, A.-M. Sundström, and G. de Leeuw
Atmos. Meas. Tech., 8, 3075–3085, https://doi.org/10.5194/amt-8-3075-2015, https://doi.org/10.5194/amt-8-3075-2015, 2015
E. Giannakaki, A. Pfüller, K. Korhonen, T. Mielonen, L. Laakso, V. Vakkari, H. Baars, R. Engelmann, J. P. Beukes, P. G. Van Zyl, M. Josipovic, P. Tiitta, K. Chiloane, S. Piketh, H. Lihavainen, K. E. J. Lehtinen, and M. Komppula
Atmos. Chem. Phys., 15, 5429–5442, https://doi.org/10.5194/acp-15-5429-2015, https://doi.org/10.5194/acp-15-5429-2015, 2015
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In this study we summarize 1 year of Raman lidar observations over South Africa. The analyses of lidar measurements presented here could assist in bridging existing gaps in the knowledge of vertical distribution of aerosols above South Africa, since limited long-term data of this type are available for this region. For the first time, we have been able to cover the full seasonal cycle on geometrical characteristics and optical properties of free tropospheric aerosol layers in the region.
J. A. E. van Gijsel, R. Zurita-Milla, P. Stammes, S. Godin-Beekmann, T. Leblanc, M. Marchand, I. S. McDermid, K. Stebel, W. Steinbrecht, and D. P. J. Swart
Atmos. Meas. Tech., 8, 1951–1963, https://doi.org/10.5194/amt-8-1951-2015, https://doi.org/10.5194/amt-8-1951-2015, 2015
L. Sogacheva, P. Kolmonen, T. H. Virtanen, E. Rodriguez, A.-M. Sundström, and G. de Leeuw
Atmos. Meas. Tech., 8, 891–906, https://doi.org/10.5194/amt-8-891-2015, https://doi.org/10.5194/amt-8-891-2015, 2015
T. Mielonen, J. F. de Haan, J. C. A. van Peet, M. Eremenko, and J. P. Veefkind
Atmos. Meas. Tech., 8, 671–687, https://doi.org/10.5194/amt-8-671-2015, https://doi.org/10.5194/amt-8-671-2015, 2015
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In this paper, we have assessed the sensitivity of the operational Ozone Monitoring Instrument ozone profile retrieval algorithm to a number of a priori and radiative transfer assumptions. We then modified the algorithm to improve the retrieval of tropospheric ozone. We found that the modified retrieval unmasks systematic problems in the radiative transfer/instrument model and is more sensitive to tropospheric ozone variation: it is able to capture the tropospheric ozone morphology better.
A.-M. Sundström, A. Arola, P. Kolmonen, Y. Xue, G. de Leeuw, and M. Kulmala
Atmos. Chem. Phys., 15, 505–518, https://doi.org/10.5194/acp-15-505-2015, https://doi.org/10.5194/acp-15-505-2015, 2015
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In this work, a satellite-based approach to derive the aerosol direct shortwave (SW) radiative effect (ADRE) is studied. The method is based on using coincident satellite observations of SW fluxes and aerosol optical depths (AODs). The key findings of this study are that using normalized values of observed fluxes improves the estimates of ADRE and aerosol-free TOA fluxes as compared to a model. The method was applied over eastern China where the satellite-based mean ADRE of -5Wm-2 was obtained.
A. M. Sayer, N. C. Hsu, T. F. Eck, A. Smirnov, and B. N. Holben
Atmos. Chem. Phys., 14, 11493–11523, https://doi.org/10.5194/acp-14-11493-2014, https://doi.org/10.5194/acp-14-11493-2014, 2014
S. K. Ebmeier, A. M. Sayer, R. G. Grainger, T. A. Mather, and E. Carboni
Atmos. Chem. Phys., 14, 10601–10618, https://doi.org/10.5194/acp-14-10601-2014, https://doi.org/10.5194/acp-14-10601-2014, 2014
T. H. Virtanen, P. Kolmonen, E. Rodríguez, L. Sogacheva, A.-M. Sundström, and G. de Leeuw
Atmos. Meas. Tech., 7, 2437–2456, https://doi.org/10.5194/amt-7-2437-2014, https://doi.org/10.5194/amt-7-2437-2014, 2014
J. Huttunen, A. Arola, G. Myhre, A. V. Lindfors, T. Mielonen, S. Mikkonen, J. S. Schafer, S. N. Tripathi, M. Wild, M. Komppula, and K. E. J. Lehtinen
Atmos. Chem. Phys., 14, 6103–6110, https://doi.org/10.5194/acp-14-6103-2014, https://doi.org/10.5194/acp-14-6103-2014, 2014
K. Korhonen, E. Giannakaki, T. Mielonen, A. Pfüller, L. Laakso, V. Vakkari, H. Baars, R. Engelmann, J. P. Beukes, P. G. Van Zyl, A. Ramandh, L. Ntsangwane, M. Josipovic, P. Tiitta, G. Fourie, I. Ngwana, K. Chiloane, and M. Komppula
Atmos. Chem. Phys., 14, 4263–4278, https://doi.org/10.5194/acp-14-4263-2014, https://doi.org/10.5194/acp-14-4263-2014, 2014
M. Chin, T. Diehl, Q. Tan, J. M. Prospero, R. A. Kahn, L. A. Remer, H. Yu, A. M. Sayer, H. Bian, I. V. Geogdzhayev, B. N. Holben, S. G. Howell, B. J. Huebert, N. C. Hsu, D. Kim, T. L. Kucsera, R. C. Levy, M. I. Mishchenko, X. Pan, P. K. Quinn, G. L. Schuster, D. G. Streets, S. A. Strode, O. Torres, and X.-P. Zhao
Atmos. Chem. Phys., 14, 3657–3690, https://doi.org/10.5194/acp-14-3657-2014, https://doi.org/10.5194/acp-14-3657-2014, 2014
A. C. Povey, R. G. Grainger, D. M. Peters, and J. L. Agnew
Atmos. Meas. Tech., 7, 757–776, https://doi.org/10.5194/amt-7-757-2014, https://doi.org/10.5194/amt-7-757-2014, 2014
V. F. Sofieva, J. Tamminen, E. Kyrölä, T. Mielonen, P. Veefkind, B. Hassler, and G.E. Bodeker
Atmos. Chem. Phys., 14, 283–299, https://doi.org/10.5194/acp-14-283-2014, https://doi.org/10.5194/acp-14-283-2014, 2014
Y.-C. Chen, B. Hamre, Ø. Frette, S. Blindheim, K. Stebel, P. Sobolewski, C. Toledano, and J. J. Stamnes
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amtd-6-10761-2013, https://doi.org/10.5194/amtd-6-10761-2013, 2013
Preprint withdrawn
R. C. Levy, S. Mattoo, L. A. Munchak, L. A. Remer, A. M. Sayer, F. Patadia, and N. C. Hsu
Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013, https://doi.org/10.5194/amt-6-2989-2013, 2013
F. Patadia, R. A. Kahn, J. A. Limbacher, S. P. Burton, R. A. Ferrare, C. A. Hostetler, and J. W. Hair
Atmos. Chem. Phys., 13, 9525–9541, https://doi.org/10.5194/acp-13-9525-2013, https://doi.org/10.5194/acp-13-9525-2013, 2013
G. Saponaro, P. Kolmonen, J. Karhunen, J. Tamminen, and G. de Leeuw
Atmos. Meas. Tech., 6, 2301–2309, https://doi.org/10.5194/amt-6-2301-2013, https://doi.org/10.5194/amt-6-2301-2013, 2013
S. Eckhardt, O. Hermansen, H. Grythe, M. Fiebig, K. Stebel, M. Cassiani, A. Baecklund, and A. Stohl
Atmos. Chem. Phys., 13, 8401–8409, https://doi.org/10.5194/acp-13-8401-2013, https://doi.org/10.5194/acp-13-8401-2013, 2013
A. Petzold, J. A. Ogren, M. Fiebig, P. Laj, S.-M. Li, U. Baltensperger, T. Holzer-Popp, S. Kinne, G. Pappalardo, N. Sugimoto, C. Wehrli, A. Wiedensohler, and X.-Y. Zhang
Atmos. Chem. Phys., 13, 8365–8379, https://doi.org/10.5194/acp-13-8365-2013, https://doi.org/10.5194/acp-13-8365-2013, 2013
T. Holzer-Popp, G. de Leeuw, J. Griesfeller, D. Martynenko, L. Klüser, S. Bevan, W. Davies, F. Ducos, J. L. Deuzé, R. G. Graigner, A. Heckel, W. von Hoyningen-Hüne, P. Kolmonen, P. Litvinov, P. North, C. A. Poulsen, D. Ramon, R. Siddans, L. Sogacheva, D. Tanre, G. E. Thomas, M. Vountas, J. Descloitres, J. Griesfeller, S. Kinne, M. Schulz, and S. Pinnock
Atmos. Meas. Tech., 6, 1919–1957, https://doi.org/10.5194/amt-6-1919-2013, https://doi.org/10.5194/amt-6-1919-2013, 2013
J.-P. Pommereau, F. Goutail, F. Lefèvre, A. Pazmino, C. Adams, V. Dorokhov, P. Eriksen, R. Kivi, K. Stebel, X. Zhao, and M. van Roozendael
Atmos. Chem. Phys., 13, 5299–5308, https://doi.org/10.5194/acp-13-5299-2013, https://doi.org/10.5194/acp-13-5299-2013, 2013
G. Pappalardo, L. Mona, G. D'Amico, U. Wandinger, M. Adam, A. Amodeo, A. Ansmann, A. Apituley, L. Alados Arboledas, D. Balis, A. Boselli, J. A. Bravo-Aranda, A. Chaikovsky, A. Comeron, J. Cuesta, F. De Tomasi, V. Freudenthaler, M. Gausa, E. Giannakaki, H. Giehl, A. Giunta, I. Grigorov, S. Groß, M. Haeffelin, A. Hiebsch, M. Iarlori, D. Lange, H. Linné, F. Madonna, I. Mattis, R.-E. Mamouri, M. A. P. McAuliffe, V. Mitev, F. Molero, F. Navas-Guzman, D. Nicolae, A. Papayannis, M. R. Perrone, C. Pietras, A. Pietruczuk, G. Pisani, J. Preißler, M. Pujadas, V. Rizi, A. A. Ruth, J. Schmidt, F. Schnell, P. Seifert, I. Serikov, M. Sicard, V. Simeonov, N. Spinelli, K. Stebel, M. Tesche, T. Trickl, X. Wang, F. Wagner, M. Wiegner, and K. M. Wilson
Atmos. Chem. Phys., 13, 4429–4450, https://doi.org/10.5194/acp-13-4429-2013, https://doi.org/10.5194/acp-13-4429-2013, 2013
P. Kolmonen, A.-M. Sundström, L. Sogacheva, E. Rodriguez, T. Virtanen, and G. de Leeuw
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amtd-6-4039-2013, https://doi.org/10.5194/amtd-6-4039-2013, 2013
Revised manuscript has not been submitted
T. Viskari, E. Asmi, P. Kolmonen, H. Vuollekoski, T. Petäjä, and H. Järvinen
Atmos. Chem. Phys., 12, 11767–11779, https://doi.org/10.5194/acp-12-11767-2012, https://doi.org/10.5194/acp-12-11767-2012, 2012
T. Viskari, E. Asmi, A. Virkkula, P. Kolmonen, T. Petäjä, and H. Järvinen
Atmos. Chem. Phys., 12, 11781–11793, https://doi.org/10.5194/acp-12-11781-2012, https://doi.org/10.5194/acp-12-11781-2012, 2012
Related subject area
Subject: Aerosols | Technique: Remote Sensing | Topic: Validation and Intercomparisons
Intercomparison of aerosol optical depth retrievals from GAW-PFR and SKYNET sun photometer networks and the effect of calibration
Evaluation of Aeolus feature mask and particle extinction coefficient profile products using CALIPSO data
Assessment of the impact of NO2 contribution on aerosol-optical-depth measurements at several sites worldwide
Improved mean field estimates from the Geostationary Environment Monitoring Spectrometer (GEMS) Level-3 aerosol optical depth (L3 AOD) product: using spatiotemporal variability
Evaluation of on-site calibration procedures for SKYNET Prede POM sun–sky photometers
Aerosol optical property measurement using the orbiting high-spectral-resolution lidar on board the DQ-1 satellite: retrieval and validation
Regional validation of the solar irradiance tool SolaRes in clear-sky conditions, with a focus on the aerosol module
An empirical characterization of the aerosol Ångström exponent interpolation bias using SAGE III/ISS data
Retrievals of aerosol optical depth over the western North Atlantic Ocean during ACTIVATE
Characterization of dust aerosols from ALADIN and CALIOP measurements
Lidar depolarization characterization using a reference system
Algorithm evaluation for polarimetric remote sensing of atmospheric aerosols
Validation of initial observation from the first spaceborne high-spectral-resolution lidar with a ground-based lidar network
Ozone and aerosol optical depth retrievals using the ultraviolet multi-filter rotating shadow-band radiometer
Expanding the coverage of Multi-angle Imaging SpectroRadiometer (MISR) aerosol retrievals over shallow, turbid, and eutrophic waters
Aerosol properties derived from ground-based Fourier transform spectra within the COllaborative Carbon Column Observing Network
Spectral aerosol optical depth from SI-traceable spectral solar irradiance measurements
Quality assessment of aerosol lidars at 1064 nm in the framework of the MEMO campaign
Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product and the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol optical depth (AOD) data fusion product and its proxy
Assessment of severe aerosol events from NASA MODIS and VIIRS aerosol products for data assimilation and climate continuity
First assessment of Aeolus Standard Correct Algorithm particle backscatter coefficient retrievals in the eastern Mediterranean
Remote sensing of aerosol water fraction, dry size distribution and soluble fraction using multi-angle, multi-spectral polarimetry
Estimates of remote sensing retrieval errors by the GRASP algorithm: application to ground-based observations, concept and validation
Sensitivity of aerosol optical depth trends using long-term measurements of different sun photometers
Extended validation and evaluation of the OLCI–SLSTR SYNERGY aerosol product (SY_2_AOD) on Sentinel-3
Performance evaluation for retrieving aerosol optical depth from the Directional Polarimetric Camera (DPC) based on the GRASP algorithm
Assessment of tropospheric CALIPSO Version 4.2 aerosol types over the ocean using independent CALIPSO–SODA lidar ratios
Real-time UV index retrieval in Europe using Earth observation-based techniques: system description and quality assessment
Evaluation of UV–visible MAX-DOAS aerosol profiling products by comparison with ceilometer, sun photometer, and in situ observations in Vienna, Austria
Experimental assessment of a micro-pulse lidar system in comparison with reference lidar measurements for aerosol optical properties retrieval
Characterization of aerosol size properties from measurements of spectral optical depth: a global validation of the GRASP-AOD code using long-term AERONET data
Retrieval of aerosol fine-mode fraction over China from satellite multiangle polarized observations: validation and comparison
Retrieval and evaluation of tropospheric-aerosol extinction profiles using multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements over Athens, Greece
Empirically derived parameterizations of the direct aerosol radiative effect based on ORACLES aircraft observations
TROPOMI aerosol products: evaluation and observations of synoptic-scale carbonaceous aerosol plumes during 2018–2020
Combining low-cost, surface-based aerosol monitors with size-resolved satellite data for air quality applications
Interannual and seasonal variations in the aerosol optical depth of the atmosphere in two regions of Spitsbergen (2002–2018)
Evaluation of UV aerosol retrievals from an ozone lidar
Aerosol data assimilation in the MOCAGE chemical transport model during the TRAQA/ChArMEx campaign: lidar observations
Application of low-cost fine particulate mass monitors to convert satellite aerosol optical depth to surface concentrations in North America and Africa
Evaluation of the OMPS/LP stratospheric aerosol extinction product using SAGE III/ISS observations
A fast visible-wavelength 3D radiative transfer model for numerical weather prediction visualization and forward modeling
A first comparison of TROPOMI aerosol layer height (ALH) to CALIOP data
The 2018 fire season in North America as seen by TROPOMI: aerosol layer height intercomparisons and evaluation of model-derived plume heights
Evaluation of satellite-based aerosol datasets and the CAMS reanalysis over the ocean utilizing shipborne reference observations
Aerosol and cloud top height information of Envisat MIPAS measurements
Assessment of urban aerosol pollution over the Moscow megacity by the MAIAC aerosol product
Aerosol retrievals from different polarimeters during the ACEPOL campaign using a common retrieval algorithm
Analysis of global three-dimensional aerosol structure with spectral radiance matching
A comparative evaluation of Aura-OMI and SKYNET near-UV single-scattering albedo products
Angelos Karanikolas, Natalia Kouremeti, Monica Campanelli, Victor Estellés, Masahiro Momoi, Gaurav Kumar, Stephan Nyeki, and Stelios Kazadzis
Atmos. Meas. Tech., 17, 6085–6105, https://doi.org/10.5194/amt-17-6085-2024, https://doi.org/10.5194/amt-17-6085-2024, 2024
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Different sun photometer networks use different instruments, post-processing algorithms and calibration protocols for aerosol optical depth (AOD) retrieval. Such differences can affect the homogeneity and comparability of their measurements. In this study, we assess the homogeneity between the sun photometer networks GAW-PFR and SKYNET, analysing common measurements during three campaigns between 2017–2021, and investigate the main cause of the differences.
Ping Wang, David Patrick Donovan, Gerd-Jan van Zadelhoff, Jos de Kloe, Dorit Huber, and Katja Reissig
Atmos. Meas. Tech., 17, 5935–5955, https://doi.org/10.5194/amt-17-5935-2024, https://doi.org/10.5194/amt-17-5935-2024, 2024
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We describe the new feature mask (AEL-FM) and aerosol profile retrieval (AEL-PRO) algorithms developed for Aeolus lidar and present the evaluation of the Aeolus products using CALIPSO data for dust aerosols over Africa. We have found that Aeolus and CALIPSO show similar aerosol patterns in the collocated orbits and have good agreement for the extinction coefficients for the dust aerosols, especially for the cloud-free scenes. The finding is applicable to Aeolus L2A product Baseline 17.
Akriti Masoom, Stelios Kazadzis, Masimo Valeri, Ioannis-Panagiotis Raptis, Gabrielle Brizzi, Kyriakoula Papachristopoulou, Francesca Barnaba, Stefano Casadio, Axel Kreuter, and Fabrizio Niro
Atmos. Meas. Tech., 17, 5525–5549, https://doi.org/10.5194/amt-17-5525-2024, https://doi.org/10.5194/amt-17-5525-2024, 2024
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Aerosols, which have a wide impact on climate, radiative forcing, and human health, are widely represented by aerosol optical depth (AOD). AOD retrievals require Rayleigh scattering and atmospheric absorption (ozone, NO2, etc.) corrections. We analysed the NO2 (which has a high spatiotemporal variation) uncertainty impact on AOD retrievals using the synergy of co-located ground-based instruments with a long-term dataset at worldwide sites and found significant AOD over- or underestimations.
Sooyon Kim, Yeseul Cho, Hanjeong Ki, Seyoung Park, Dagun Oh, Seungjun Lee, Yeonghye Cho, Jhoon Kim, Wonjin Lee, Jaewoo Park, Ick Hoon Jin, and Sangwook Kang
Atmos. Meas. Tech., 17, 5221–5241, https://doi.org/10.5194/amt-17-5221-2024, https://doi.org/10.5194/amt-17-5221-2024, 2024
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This paper describes new work that improves the processing of GEMS AOD data. First, we enhance the inverse-distance-weighting algorithm by incorporating quality flag information, assigning weights that are inversely proportional to the number of unreliable grids. Second, we leverage a spatiotemporal merging method to address both spatial and temporal variability. Finally, we estimate the mean field values for GEMS AOD data, enhancing our understanding of the impact of aerosols on climate change.
Monica Campanelli, Victor Estellés, Gaurav Kumar, Teruyuki Nakajima, Masahiro Momoi, Julian Gröbner, Stelios Kazadzis, Natalia Kouremeti, Angelos Karanikolas, Africa Barreto, Saulius Nevas, Kerstin Schwind, Philipp Schneider, Iiro Harju, Petri Kärhä, Henri Diémoz, Rei Kudo, Akihiro Uchiyama, Akihiro Yamazaki, Anna Maria Iannarelli, Gabriele Mevi, Annalisa Di Bernardino, and Stefano Casadio
Atmos. Meas. Tech., 17, 5029–5050, https://doi.org/10.5194/amt-17-5029-2024, https://doi.org/10.5194/amt-17-5029-2024, 2024
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To retrieve columnar aerosol properties from sun photometers, some calibration factors are needed. The on-site calibrations, performed as frequently as possible to monitor changes in the machine conditions, allow operators to track and evaluate the calibration status on a continuous basis, reducing the data gaps incurred by the periodic shipments for performing centralized calibrations. The performance of the on-site calibration procedures was evaluated, providing very good results.
Chenxing Zha, Lingbing Bu, Zhi Li, Qin Wang, Ahmad Mubarak, Pasindu Liyanage, Jiqiao Liu, and Weibiao Chen
Atmos. Meas. Tech., 17, 4425–4443, https://doi.org/10.5194/amt-17-4425-2024, https://doi.org/10.5194/amt-17-4425-2024, 2024
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China has launched the atmospheric environment monitoring satellite DQ-1, which consists of an advanced lidar system. Our research presents a retrieval algorithm of the DQ-1 lidar system, and the retrieval results are consistent with other datasets. We also use the DQ-1 dataset to investigate dust and volcanic aerosols. This research shows that the DQ-1 lidar system can accurately measure the Earth's atmosphere and has potential for scientific applications.
Thierry Elias, Nicolas Ferlay, Gabriel Chesnoiu, Isabelle Chiapello, and Mustapha Moulana
Atmos. Meas. Tech., 17, 4041–4063, https://doi.org/10.5194/amt-17-4041-2024, https://doi.org/10.5194/amt-17-4041-2024, 2024
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In the solar energy application field, it is key to simulate solar resources anywhere on the globe. We conceived the Solar Resource estimate (SolaRes) tool to provide precise and accurate estimates of solar resources for any solar plant technology. We present the validation of SolaRes by comparing estimates with measurements made on two ground-based platforms in northern France for 2 years at 1 min resolution. Validation is done in clear-sky conditions where aerosols are the main factors.
Robert P. Damadeo, Viktoria F. Sofieva, Alexei Rozanov, and Larry W. Thomason
Atmos. Meas. Tech., 17, 3669–3678, https://doi.org/10.5194/amt-17-3669-2024, https://doi.org/10.5194/amt-17-3669-2024, 2024
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Comparing different aerosol data sets for scientific studies often requires converting aerosol extinction data between different wavelengths. A common approximation for the spectral behavior of aerosol is the Ångström formula; however, this introduces biases. Using measurements across many different wavelengths from a single instrument, we derive an empirical relationship to both characterize this bias and offer a correction for other studies that may employ this analysis approach.
Leong Wai Siu, Joseph S. Schlosser, David Painemal, Brian Cairns, Marta A. Fenn, Richard A. Ferrare, Johnathan W. Hair, Chris A. Hostetler, Longlei Li, Mary M. Kleb, Amy Jo Scarino, Taylor J. Shingler, Armin Sorooshian, Snorre A. Stamnes, and Xubin Zeng
Atmos. Meas. Tech., 17, 2739–2759, https://doi.org/10.5194/amt-17-2739-2024, https://doi.org/10.5194/amt-17-2739-2024, 2024
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An unprecedented 3-year aerosol dataset was collected from a recent NASA field campaign over the western North Atlantic Ocean, which offers a special opportunity to evaluate two state-of-the-art remote sensing instruments, one lidar and the other polarimeter, on the same aircraft. Special attention has been paid to validate aerosol optical depth data and their uncertainties when no reference dataset is available. Physical reasons for the disagreement between two instruments are discussed.
Rui Song, Adam Povey, and Roy G. Grainger
Atmos. Meas. Tech., 17, 2521–2538, https://doi.org/10.5194/amt-17-2521-2024, https://doi.org/10.5194/amt-17-2521-2024, 2024
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In our study, we explored aerosols, tiny atmospheric particles affecting the Earth's climate. Using data from two lidar-equipped satellites, ALADIN and CALIOP, we examined a 2020 Saharan dust event. The newer ALADIN's results aligned with CALIOP's. By merging their data, we corrected CALIOP's discrepancies, enhancing the dust event depiction. This underscores the significance of advanced satellite instruments in aerosol research. Our findings pave the way for upcoming satellite missions.
Alkistis Papetta, Franco Marenco, Maria Kezoudi, Rodanthi-Elisavet Mamouri, Argyro Nisantzi, Holger Baars, Ioana Elisabeta Popovici, Philippe Goloub, Stéphane Victori, and Jean Sciare
Atmos. Meas. Tech., 17, 1721–1738, https://doi.org/10.5194/amt-17-1721-2024, https://doi.org/10.5194/amt-17-1721-2024, 2024
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We propose a method to determine depolarization parameters using observations from a reference instrument at a nearby location, needed for systems where a priori knowledge of cross-talk parameters is not available. It uses three-parameter equations to compare VDR between two co-located lidars at dust and molecular layers. It can be applied retrospectively to existing data acquired during campaigns. Its application to Cimel CE376 corrected VDR bias at high- and low-depolarizing layers.
Otto Hasekamp, Pavel Litvinov, Guangliang Fu, Cheng Chen, and Oleg Dubovik
Atmos. Meas. Tech., 17, 1497–1525, https://doi.org/10.5194/amt-17-1497-2024, https://doi.org/10.5194/amt-17-1497-2024, 2024
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Aerosols are particles in the atmosphere that cool the climate by reflecting and absorbing sunlight (direct effect) and changing cloud properties (indirect effect). The scale of aerosol cooling is uncertain, hampering accurate climate predictions. We compare two algorithms for the retrieval of aerosol properties from multi-angle polarimetric measurements: Generalized Retrieval of Atmosphere and Surface Properties (GRASP) and Remote sensing of Trace gas and Aerosol Products (RemoTAP).
Qiantao Liu, Zhongwei Huang, Jiqiao Liu, Weibiao Chen, Qingqing Dong, Songhua Wu, Guangyao Dai, Meishi Li, Wuren Li, Ze Li, Xiaodong Song, and Yuan Xie
Atmos. Meas. Tech., 17, 1403–1417, https://doi.org/10.5194/amt-17-1403-2024, https://doi.org/10.5194/amt-17-1403-2024, 2024
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The achieved results revealed that the ACDL observations were in good agreement with the ground-based lidar measurements during dust events. The heights of cloud top and bottom from these two measurements were well matched and comparable. This study proves that the ACDL provides reliable observations of aerosol and cloud in the presence of various climatic conditions, which helps to further evaluate the impacts of aerosol on climate and the environment, as well as on the ecosystem in the future.
Joseph Michalsky and Glen McConville
Atmos. Meas. Tech., 17, 1017–1022, https://doi.org/10.5194/amt-17-1017-2024, https://doi.org/10.5194/amt-17-1017-2024, 2024
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The ozone in the atmosphere is measured by looking at the sun and measuring how diminished the light in the ultraviolet is relative to how bright it is above the Earth's atmosphere. This typically uses spectral instruments that are either costly or no longer manufactured. This paper uses a relatively inexpensive interference filter instrument to perform the same task. Daily ozone measurements with the latter and this filter instrument are compared. Aerosols are calculated as a by-product.
Robert R. Nelson, Marcin L. Witek, Michael J. Garay, Michael A. Bull, James A. Limbacher, Ralph A. Kahn, and David J. Diner
Atmos. Meas. Tech., 16, 4947–4960, https://doi.org/10.5194/amt-16-4947-2023, https://doi.org/10.5194/amt-16-4947-2023, 2023
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Shallow and coastal waters are nutrient-rich and turbid due to runoff. They are also located in areas where the atmosphere has more aerosols than open-ocean waters. NASA's Multi-angle Imaging SpectroRadiometer (MISR) has been monitoring aerosols for over 23 years but does not report results over shallow waters. We developed a new algorithm that uses all four of MISR’s bands and considers light leaving water surfaces. This algorithm performs well and increases over-water measurements by over 7 %.
Óscar Alvárez, África Barreto, Omaira E. García, Frank Hase, Rosa D. García, Julian Gröbner, Sergio F. León-Luis, Eliezer Sepúlveda, Virgilio Carreño, Antonio Alcántara, Ramón Ramos, A. Fernando Almansa, Stelios Kazadzis, Noémie Taquet, Carlos Toledano, and Emilio Cuevas
Atmos. Meas. Tech., 16, 4861–4884, https://doi.org/10.5194/amt-16-4861-2023, https://doi.org/10.5194/amt-16-4861-2023, 2023
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In this work, we have extended the capabilities of a portable Fourier transform infrared (FTIR) instrument, which was originally designed to provide high-quality greenhouse gas monitoring within COCCON (COllaborative Carbon Column Observing Network). The extension allows the spectrometer to now also provide coincidentally column-integrated aerosol information. This addition of a reference instrument to a global network will be utilised to enhance our understanding of atmospheric chemistry.
Julian Gröbner, Natalia Kouremeti, Gregor Hülsen, Ralf Zuber, Mario Ribnitzky, Saulius Nevas, Peter Sperfeld, Kerstin Schwind, Philipp Schneider, Stelios Kazadzis, África Barreto, Tom Gardiner, Kavitha Mottungan, David Medland, and Marc Coleman
Atmos. Meas. Tech., 16, 4667–4680, https://doi.org/10.5194/amt-16-4667-2023, https://doi.org/10.5194/amt-16-4667-2023, 2023
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Spectral solar irradiance measurements traceable to the International System of Units (SI) allow for intercomparability between instruments and for their validation according to metrological standards. Here we also validate and reduce the uncertainties of the top-of-atmosphere TSIS-1 Hybrid Solar Reference Spectrum (HSRS). The management of large networks, e.g. AERONET or GAW-PFR, will benefit from reducing logistical overhead, improving their resilience and achieving metrological traceability.
Longlong Wang, Zhenping Yin, Zhichao Bu, Anzhou Wang, Song Mao, Yang Yi, Detlef Müller, Yubao Chen, and Xuan Wang
Atmos. Meas. Tech., 16, 4307–4318, https://doi.org/10.5194/amt-16-4307-2023, https://doi.org/10.5194/amt-16-4307-2023, 2023
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We report the lidar inter-comparison results with a reference lidar at 1064 nm, in order to homogenize the signals provided by different lidar systems for establishing a lidar network in China. The profiles of relative deviation of lidar signals are less than 5 % within 500–2000 m and 10 % within 2000–5000 m, increasing confidence in the reliability of the signals provided by each lidar system in the channels at 1064 nm for a future lidar network in China.
Jincheol Park, Jia Jung, Yunsoo Choi, Hyunkwang Lim, Minseok Kim, Kyunghwa Lee, Yun Gon Lee, and Jhoon Kim
Atmos. Meas. Tech., 16, 3039–3057, https://doi.org/10.5194/amt-16-3039-2023, https://doi.org/10.5194/amt-16-3039-2023, 2023
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In response to the recent release of new geostationary platform-derived observational data generated by the Geostationary Environment Monitoring Spectrometer (GEMS) and its sister instruments, this study utilized the GEMS data fusion product and its proxy data in adjusting aerosol precursor emissions over East Asia. The use of spatiotemporally more complete observation references in updating the emissions resulted in more promising model performances in estimating aerosol loadings in East Asia.
Amanda Gumber, Jeffrey S. Reid, Robert E. Holz, Thomas F. Eck, N. Christina Hsu, Robert C. Levy, Jianglong Zhang, and Paolo Veglio
Atmos. Meas. Tech., 16, 2547–2573, https://doi.org/10.5194/amt-16-2547-2023, https://doi.org/10.5194/amt-16-2547-2023, 2023
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The purpose of this study is to create and evaluate a gridded dataset composed of multiple satellite instruments and algorithms to be used for data assimilation. An important part of aerosol data assimilation is having consistent measurements, especially for severe aerosol events. This study evaluates 4 years of data from MODIS, VIIRS, and AERONET with a focus on aerosol severe event detection from a regional and global perspective.
Antonis Gkikas, Anna Gialitaki, Ioannis Binietoglou, Eleni Marinou, Maria Tsichla, Nikolaos Siomos, Peristera Paschou, Anna Kampouri, Kalliopi Artemis Voudouri, Emmanouil Proestakis, Maria Mylonaki, Christina-Anna Papanikolaou, Konstantinos Michailidis, Holger Baars, Anne Grete Straume, Dimitris Balis, Alexandros Papayannis, Tomasso Parrinello, and Vassilis Amiridis
Atmos. Meas. Tech., 16, 1017–1042, https://doi.org/10.5194/amt-16-1017-2023, https://doi.org/10.5194/amt-16-1017-2023, 2023
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We perform an assessment analysis of the Aeolus Standard Correct Algorithm (SCA) backscatter coefficient retrievals against reference observations acquired at three Greek lidar stations (Athens, Thessaloniki and Antikythera) of the PANACEA network. Overall, 43 cases are analysed, whereas specific aerosol scenarios in the vicinity of Antikythera island (SW Greece) are emphasised. All key Cal/Val aspects and recommendations, and the ongoing related activities, are thoroughly discussed.
Bastiaan van Diedenhoven, Otto P. Hasekamp, Brian Cairns, Gregory L. Schuster, Snorre Stamnes, Michael Shook, and Luke Ziemba
Atmos. Meas. Tech., 15, 7411–7434, https://doi.org/10.5194/amt-15-7411-2022, https://doi.org/10.5194/amt-15-7411-2022, 2022
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The strong variability in the chemistry of atmospheric particulate matter affects the amount of water aerosols absorb and their effect on climate. We present a remote sensing method to determine the amount of water in particulate matter. Its application to airborne instruments indicates that the observed aerosols have rather low water contents and low fractions of soluble particles. Future satellites will be able to yield global aerosol water uptake data.
Milagros E. Herrera, Oleg Dubovik, Benjamin Torres, Tatyana Lapyonok, David Fuertes, Anton Lopatin, Pavel Litvinov, Cheng Chen, Jose Antonio Benavent-Oltra, Juan L. Bali, and Pablo R. Ristori
Atmos. Meas. Tech., 15, 6075–6126, https://doi.org/10.5194/amt-15-6075-2022, https://doi.org/10.5194/amt-15-6075-2022, 2022
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This study deals with the dynamic error estimates of the aerosol-retrieved properties by the GRASP algorithm, which are provided for directly retrieved and derived parameters. Moreover, GRASP provides full covariance matrices that appear to be a useful approach for optimizing observation schemes and retrieval set-ups. The validation of the retrieved dynamic error estimates is done through real and synthetic measurements using sun photometer and lidar observations.
Angelos Karanikolas, Natalia Kouremeti, Julian Gröbner, Luca Egli, and Stelios Kazadzis
Atmos. Meas. Tech., 15, 5667–5680, https://doi.org/10.5194/amt-15-5667-2022, https://doi.org/10.5194/amt-15-5667-2022, 2022
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The aim of this work is to investigate the limitations of calculating long-term trends of a parameter that quantifies the overall effect of atmospheric aerosols on the solar radiation. A main finding is that even instruments with good agreement between their observations can show significantly different linear trends. By calculating time-varying trends, the trend agreement is shown to improve. We also show that different methods of trend estimation can result in significant trend differences.
Larisa Sogacheva, Matthieu Denisselle, Pekka Kolmonen, Timo H. Virtanen, Peter North, Claire Henocq, Silvia Scifoni, and Steffen Dransfeld
Atmos. Meas. Tech., 15, 5289–5322, https://doi.org/10.5194/amt-15-5289-2022, https://doi.org/10.5194/amt-15-5289-2022, 2022
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The aim of this study was to provide global characterisation of a new SYNERGY aerosol product derived from the data from the OLCI and SLSTR sensors aboard the Sentinel-3A and Sentinel-3B satellites. Over ocean, the performance of SYNERGY-retrieved AOD is good. Reduced performance over land was expected since the surface reflectance and angular distribution of scattering are more difficult to treat. Validation statistics are often slightly better for S3B and in the Southern Hemisphere.
Shikuan Jin, Yingying Ma, Cheng Chen, Oleg Dubovik, Jin Hong, Boming Liu, and Wei Gong
Atmos. Meas. Tech., 15, 4323–4337, https://doi.org/10.5194/amt-15-4323-2022, https://doi.org/10.5194/amt-15-4323-2022, 2022
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Aerosol parameter retrievals have always been a research focus. In this study, we used an advanced aerosol algorithms (GRASP, developed by Oleg Dubovik) to test the ability of DPC/Gaofen-5 (the first polarized multi-angle payload developed in China) images to obtain aerosol parameters. The results show that DPC/GRASP achieves good results (R > 0.9). This research will contribute to the development of hardware and algorithms for aerosols
Zhujun Li, David Painemal, Gregory Schuster, Marian Clayton, Richard Ferrare, Mark Vaughan, Damien Josset, Jayanta Kar, and Charles Trepte
Atmos. Meas. Tech., 15, 2745–2766, https://doi.org/10.5194/amt-15-2745-2022, https://doi.org/10.5194/amt-15-2745-2022, 2022
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For more than 15 years, CALIPSO has revolutionized our understanding of the role of aerosols in climate. Here we evaluate CALIPSO aerosol typing over the ocean using an independent CALIPSO–CloudSat product. The analysis suggests that CALIPSO correctly categorizes clean marine aerosol over the open ocean, elevated smoke over the SE Atlantic, and dust over the tropical Atlantic. Similarities between clean and dusty marine over the open ocean implies that algorithm modifications are warranted.
Panagiotis G. Kosmopoulos, Stelios Kazadzis, Alois W. Schmalwieser, Panagiotis I. Raptis, Kyriakoula Papachristopoulou, Ilias Fountoulakis, Akriti Masoom, Alkiviadis F. Bais, Julia Bilbao, Mario Blumthaler, Axel Kreuter, Anna Maria Siani, Kostas Eleftheratos, Chrysanthi Topaloglou, Julian Gröbner, Bjørn Johnsen, Tove M. Svendby, Jose Manuel Vilaplana, Lionel Doppler, Ann R. Webb, Marina Khazova, Hugo De Backer, Anu Heikkilä, Kaisa Lakkala, Janusz Jaroslawski, Charikleia Meleti, Henri Diémoz, Gregor Hülsen, Barbara Klotz, John Rimmer, and Charalampos Kontoes
Atmos. Meas. Tech., 14, 5657–5699, https://doi.org/10.5194/amt-14-5657-2021, https://doi.org/10.5194/amt-14-5657-2021, 2021
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Large-scale retrievals of the ultraviolet index (UVI) in real time by exploiting the modern Earth observation data and techniques are capable of forming operational early warning systems that raise awareness among citizens of the health implications of high UVI doses. In this direction a novel UVI operating system, the so-called UVIOS, was introduced for massive outputs, while its performance was tested against ground-based measurements revealing a dependence on the input quality and resolution.
Stefan F. Schreier, Tim Bösch, Andreas Richter, Kezia Lange, Michael Revesz, Philipp Weihs, Mihalis Vrekoussis, and Christoph Lotteraner
Atmos. Meas. Tech., 14, 5299–5318, https://doi.org/10.5194/amt-14-5299-2021, https://doi.org/10.5194/amt-14-5299-2021, 2021
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This paper reports on the evaluation of aerosol profiling products retrieved from ground-based MAX-DOAS instruments using the BOREAS algorithm. Aerosol extinction profiles, near-surface aerosol extinction, and aerosol optical depth are compared to measurements collected with ceilometer, sun photometer, and in situ instruments. We show that these MAX-DOAS aerosol profiling products provide useful information to study spatial and temporal variations above the urban area of Vienna.
Carmen Córdoba-Jabonero, Albert Ansmann, Cristofer Jiménez, Holger Baars, María-Ángeles López-Cayuela, and Ronny Engelmann
Atmos. Meas. Tech., 14, 5225–5239, https://doi.org/10.5194/amt-14-5225-2021, https://doi.org/10.5194/amt-14-5225-2021, 2021
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An experimental assessment of a polarized micro-pulse lidar (P-MPL) in comparison to reference lidars is presented regarding the retrieval of aerosol optical properties. The evaluation is focused on both the optimally determined overlap function and volume linear depolarization ratio. A P-MPL overlap must be regularly estimated to derive suitable aerosol products (backscatter, extinction, and particle depolarization ratio). This methodology can be easily applied to other P-MPL systems.
Benjamin Torres and David Fuertes
Atmos. Meas. Tech., 14, 4471–4506, https://doi.org/10.5194/amt-14-4471-2021, https://doi.org/10.5194/amt-14-4471-2021, 2021
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The article shows the capacity of the new GRASP-AOD approach to be used for large datasets of aerosol optical depth from ground-based observations, through a comparison with standard AERONET codes. This new approach reduces the requirements in terms of measurements (no need of scattering information) to derive some basic aerosol size and optical properties. A broad use of this algorithm would increase the datasets of aerosol properties from ground-based observations.
Yang Zhang, Zhengqiang Li, Zhihong Liu, Yongqian Wang, Lili Qie, Yisong Xie, Weizhen Hou, and Lu Leng
Atmos. Meas. Tech., 14, 1655–1672, https://doi.org/10.5194/amt-14-1655-2021, https://doi.org/10.5194/amt-14-1655-2021, 2021
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The aerosol fine-mode fraction (FMF) is an important parameter reflecting the content of man-made aerosols. This study carried out the retrieval of FMF in China based on multi-angle polarization data and validated the results. The results of this study can contribute to the FMF retrieval algorithm of multi-angle polarization sensors. At the same time, a high-precision FMF dataset of China was obtained, which can provide basic data for atmospheric environment research.
Myrto Gratsea, Tim Bösch, Panagiotis Kokkalis, Andreas Richter, Mihalis Vrekoussis, Stelios Kazadzis, Alexandra Tsekeri, Alexandros Papayannis, Maria Mylonaki, Vassilis Amiridis, Nikos Mihalopoulos, and Evangelos Gerasopoulos
Atmos. Meas. Tech., 14, 749–767, https://doi.org/10.5194/amt-14-749-2021, https://doi.org/10.5194/amt-14-749-2021, 2021
Sabrina P. Cochrane, K. Sebastian Schmidt, Hong Chen, Peter Pilewskie, Scott Kittelman, Jens Redemann, Samuel LeBlanc, Kristina Pistone, Meloë Kacenelenbogen, Michal Segal Rozenhaimer, Yohei Shinozuka, Connor Flynn, Amie Dobracki, Paquita Zuidema, Steven Howell, Steffen Freitag, and Sarah Doherty
Atmos. Meas. Tech., 14, 567–593, https://doi.org/10.5194/amt-14-567-2021, https://doi.org/10.5194/amt-14-567-2021, 2021
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Based on observations from the 2016 and 2017 field campaigns of ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS), this work establishes an observationally driven link from mid-visible aerosol optical depth (AOD) and other scene parameters to broadband shortwave irradiance (and by extension the direct aerosol radiative effect, DARE). The majority of the case-to-case DARE variability within the ORACLES dataset is attributable to the dependence on AOD and scene albedo.
Omar Torres, Hiren Jethva, Changwoo Ahn, Glen Jaross, and Diego G. Loyola
Atmos. Meas. Tech., 13, 6789–6806, https://doi.org/10.5194/amt-13-6789-2020, https://doi.org/10.5194/amt-13-6789-2020, 2020
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TROPOMI measures the quantity of small suspended particles (aerosols). We describe initial results of aerosol measurements using a NASA algorithm that retrieves the UV aerosol index, aerosol optical depth, and single-scattering albedo. An evaluation of derived products using sun-photometer observations shows close agreement. We also use these results to discuss important biomass burning and wildfire events around the world that got the attention of scientists and news media alike.
Priyanka deSouza, Ralph A. Kahn, James A. Limbacher, Eloise A. Marais, Fábio Duarte, and Carlo Ratti
Atmos. Meas. Tech., 13, 5319–5334, https://doi.org/10.5194/amt-13-5319-2020, https://doi.org/10.5194/amt-13-5319-2020, 2020
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This paper presents a novel method to constrain the size distribution derived from low-cost optical particle counters (OPCs) using satellite data to develop higher-quality particulate matter (PM) estimates. Such estimates can enable cities that do not have access to expensive reference air quality monitors, especially those in the global south, to develop effective air quality management plans.
Dmitry M. Kabanov, Christoph Ritter, and Sergey M. Sakerin
Atmos. Meas. Tech., 13, 5303–5317, https://doi.org/10.5194/amt-13-5303-2020, https://doi.org/10.5194/amt-13-5303-2020, 2020
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Long-term photometer measurements of two sites on Spitsbergen, Barentsburg and Ny-Ålesund, in the European Arctic are presented and compared. We find slightly higher aerosol optical depths at Barentsburg and attribute this to a higher concentration of small particles.
Shi Kuang, Bo Wang, Michael J. Newchurch, Kevin Knupp, Paula Tucker, Edwin W. Eloranta, Joseph P. Garcia, Ilya Razenkov, John T. Sullivan, Timothy A. Berkoff, Guillaume Gronoff, Liqiao Lei, Christoph J. Senff, Andrew O. Langford, Thierry Leblanc, and Vijay Natraj
Atmos. Meas. Tech., 13, 5277–5292, https://doi.org/10.5194/amt-13-5277-2020, https://doi.org/10.5194/amt-13-5277-2020, 2020
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Ozone lidar is a state-of-the-art remote-sensing instrument to measure atmospheric ozone concentrations with high spatiotemporal resolution. In this study, we show that an ozone lidar can also provide reliable aerosol measurements through intercomparison with colocated aerosol lidar observations.
Laaziz El Amraoui, Bojan Sič, Andrea Piacentini, Virginie Marécal, Nicolas Frebourg, and Jean-Luc Attié
Atmos. Meas. Tech., 13, 4645–4667, https://doi.org/10.5194/amt-13-4645-2020, https://doi.org/10.5194/amt-13-4645-2020, 2020
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The aim of this paper is to present the assimilation of lidar observations from the CALIOP instrument onboard the CALIPSO satellite in the chemistry-transport model of Météo-France, MOCAGE. We presented the first results of the assimilation of the extinction coefficient observations of the CALIOP lidar instrument during the pre-ChArMEx-TRAQA field campaign. We evaluated the added value of the assimilation product to better document a desert dust transport event compared to the model free run.
Carl Malings, Daniel M. Westervelt, Aliaksei Hauryliuk, Albert A. Presto, Andrew Grieshop, Ashley Bittner, Matthias Beekmann, and R. Subramanian
Atmos. Meas. Tech., 13, 3873–3892, https://doi.org/10.5194/amt-13-3873-2020, https://doi.org/10.5194/amt-13-3873-2020, 2020
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Most air quality information comes from accurate but expensive instruments. These can be supplemented by lower-cost sensors to increase the density of ground data and expand monitoring into less well-instrumented areas, like sub-Saharan Africa. In this paper, we look at how low-cost sensor data can be combined with satellite information on air quality (which requires ground data to properly calibrate measurements) and assess the benefits these low-cost sensors provide in this context.
Zhong Chen, Pawan K. Bhartia, Omar Torres, Glen Jaross, Robert Loughman, Matthew DeLand, Peter Colarco, Robert Damadeo, and Ghassan Taha
Atmos. Meas. Tech., 13, 3471–3485, https://doi.org/10.5194/amt-13-3471-2020, https://doi.org/10.5194/amt-13-3471-2020, 2020
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The scope of the paper is the evaluation of stratospheric aerosols derived from the OMPS/LP instrument via comparison with independent datasets from the SAGE III/ISS instrument. Results show very good agreement for extinction profiles between an altitude of 19 and 27 km, to within ±25 %, and show systematic differences (LP-SAGE III/ISS) above 28 km and below 19 km (greater than ±25 %).
Steven Albers, Stephen M. Saleeby, Sonia Kreidenweis, Qijing Bian, Peng Xian, Zoltan Toth, Ravan Ahmadov, Eric James, and Steven D. Miller
Atmos. Meas. Tech., 13, 3235–3261, https://doi.org/10.5194/amt-13-3235-2020, https://doi.org/10.5194/amt-13-3235-2020, 2020
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A fast 3D visible-light forward operator is used to realistically visualize, validate, and potentially assimilate ground- and space-based camera and satellite imagery with NWP models. Three-dimensional fields of hydrometeors, aerosols, and 2D land surface variables are considered in the generation of radiance fields and RGB imagery from a variety of vantage points.
Swadhin Nanda, Martin de Graaf, J. Pepijn Veefkind, Maarten Sneep, Mark ter Linden, Jiyunting Sun, and Pieternel F. Levelt
Atmos. Meas. Tech., 13, 3043–3059, https://doi.org/10.5194/amt-13-3043-2020, https://doi.org/10.5194/amt-13-3043-2020, 2020
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This paper presents a first validation of the TROPOspheric Monitoring Instrument (TROPOMI) aerosol layer height (ALH) product, which is an estimate of the height of an aerosol layer using a spectrometer on board ESA's Sentinel-5 Precursor satellite mission. Comparison between the TROPOMI ALH product and co-located aerosol extinction heights from the CALIOP instrument on board NASA's CALIPSO mission show good agreement for selected cases over the ocean and large differences over land.
Debora Griffin, Christopher Sioris, Jack Chen, Nolan Dickson, Andrew Kovachik, Martin de Graaf, Swadhin Nanda, Pepijn Veefkind, Enrico Dammers, Chris A. McLinden, Paul Makar, and Ayodeji Akingunola
Atmos. Meas. Tech., 13, 1427–1445, https://doi.org/10.5194/amt-13-1427-2020, https://doi.org/10.5194/amt-13-1427-2020, 2020
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This study looks into validating the aerosol layer height product from the recently launched TROPOspheric Monitoring Instrument (TROPOMI) for forest fire plume through comparisons with two other satellite products, and interpreting differences due to the individual measurement techniques. These satellite observations are compared to predicted plume heights from Environment and Climate Change's air quality forecast model.
Jonas Witthuhn, Anja Hünerbein, and Hartwig Deneke
Atmos. Meas. Tech., 13, 1387–1412, https://doi.org/10.5194/amt-13-1387-2020, https://doi.org/10.5194/amt-13-1387-2020, 2020
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Reliable reference measurements over ocean are essential for the evaluation and improvement of satellite- and model-based aerosol datasets. Here, a uniqe set of shipborne reference aerosol products obtained from Microtops sunphotometer and GUVis-3511 shadowband radiometer observations are compared to aerosol products from the MODIS and SEVIRI satellite sensors, and the CAMS reanalysis over the Atlantic Ocean. The present evaluation highlights the importance of an aerosol-type based analysis.
Sabine Griessbach, Lars Hoffmann, Reinhold Spang, Peggy Achtert, Marc von Hobe, Nina Mateshvili, Rolf Müller, Martin Riese, Christian Rolf, Patric Seifert, and Jean-Paul Vernier
Atmos. Meas. Tech., 13, 1243–1271, https://doi.org/10.5194/amt-13-1243-2020, https://doi.org/10.5194/amt-13-1243-2020, 2020
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In this paper we study the cloud top height derived from MIPAS measurements. Previous studies showed contradictory results with respect to MIPAS, both underestimating and overestimating cloud top height. We used simulations and found that overestimation and/or underestimation depend on cloud extinction. To support our findings we compared MIPAS cloud top heights of volcanic sulfate aerosol with measurements from CALIOP, ground-based lidar, and ground-based twilight measurements.
Ekaterina Y. Zhdanova, Natalia Y. Chubarova, and Alexei I. Lyapustin
Atmos. Meas. Tech., 13, 877–891, https://doi.org/10.5194/amt-13-877-2020, https://doi.org/10.5194/amt-13-877-2020, 2020
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We estimated the distribution of aerosol optical thickness (AOT) with a spatial resolution of 1 km over the Moscow megacity using the MAIAC satellite aerosol product from May to September over the years 2000–2017. We revealed that the MAIAC product is a reliable instrument for assessing the spatial features of urban aerosol pollution and its temporal dynamics. The local aerosol effect is about 0.02–0.04 in AOT in the visible spectral range over the Moscow megacity.
Guangliang Fu, Otto Hasekamp, Jeroen Rietjens, Martijn Smit, Antonio Di Noia, Brian Cairns, Andrzej Wasilewski, David Diner, Felix Seidel, Feng Xu, Kirk Knobelspiesse, Meng Gao, Arlindo da Silva, Sharon Burton, Chris Hostetler, John Hair, and Richard Ferrare
Atmos. Meas. Tech., 13, 553–573, https://doi.org/10.5194/amt-13-553-2020, https://doi.org/10.5194/amt-13-553-2020, 2020
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In this paper, we present aerosol retrieval results from the ACEPOL (Aerosol Characterization from Polarimeter and Lidar) campaign, which was a joint initiative between NASA and SRON (the Netherlands Institute for Space Research). We perform aerosol retrievals from different multi-angle polarimeters employed during the ACEPOL campaign and evaluate them against ground-based AERONET measurements and High Spectral Resolution Lidar-2 (HSRL-2) measurements.
Dong Liu, Sijie Chen, Chonghui Cheng, Howard W. Barker, Changzhe Dong, Ju Ke, Shuaibo Wang, and Zhuofan Zheng
Atmos. Meas. Tech., 12, 6541–6556, https://doi.org/10.5194/amt-12-6541-2019, https://doi.org/10.5194/amt-12-6541-2019, 2019
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Aerosols are one of the drivers of climate change, and more information about aerosol vertical distribution is needed to analyze the role of aerosols in the atmosphere. In this work, we match and substitute a pixel along the lidar ground track for every pixel that is not on the track based on the radiance measured by a passive imager, therefore expanding the atmosphere profiles to a nearby region. The accuracy of the construction is confirmed through a procedure mimicking the construction.
Hiren Jethva and Omar Torres
Atmos. Meas. Tech., 12, 6489–6503, https://doi.org/10.5194/amt-12-6489-2019, https://doi.org/10.5194/amt-12-6489-2019, 2019
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Short summary
The intercomparison of satellite- and ground-measured aerosol absorption properties, such as presented here using Aura-OMI and SKYNET sensors, constitutes an important exercise to evaluate relative performance, track algorithm changes, and to diagnose retrieval accuracy and issues. The two datasets are found to agree reasonably well under moderate to higher aerosol loading but show disagreement under lower aerosol amounts due to retrieval issues in both techniques.
Cited articles
Ahn, C., Torres, O., and Jethva, H.: Assessment of OMI near‐UV aerosol
optical depth over land, J. Geophys. Res.-Atmos., 119, 2457–2473,
https://doi.org/10.1002/2013JD020188, 2013. a
Baum, B. A., Menzel, W. P., Frey, R. A., Tobin, D. C., Holz, R. E., Ackerman,
S. A., Heidinger, A. K., and Yang, P.: MODIS Cloud-Top Property Refinements
for Collection 6, J. Appl. Meteorol. Clim., 51, 1145–1163,
https://doi.org/10.1175/JAMC-D-11-0203.1, 2012. a
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., 114, D13205,
https://doi.org/10.1029/2008JD011115, 2009. a
Benedetti, A., Reid, J. S., Knippertz, P., Marsham, J. H., Di Giuseppe, F., Rémy, S., Basart, S., Boucher, O., Brooks, I. M., Menut, L., Mona, L., Laj, P., Pappalardo, G., Wiedensohler, A., Baklanov, A., Brooks, M., Colarco, P. R., Cuevas, E., da Silva, A., Escribano, J., Flemming, J., Huneeus, N., Jorba, O., Kazadzis, S., Kinne, S., Popp, T., Quinn, P. K., Sekiyama, T. T., Tanaka, T., and Terradellas, E.: Status and future of numerical atmospheric aerosol prediction with a focus on data requirements, Atmos. Chem. Phys., 18, 10615–10643, https://doi.org/10.5194/acp-18-10615-2018, 2018. a
Bevan, S. L., North, P. R. J., Los, S. O., and Grey, W. M. F.: A global dataset
of atmospheric aerosol optical depth and surface reflectance from AATSR,
Remote Sens. Environ., 116, 199–210, https://doi.org/10.1016/j.rse.2011.05.024, 2012. a
Bourassa, A. E., Degenstein, D. A., Gattinger, R. L., and Llewellyn, E. J.:
Stratospheric aerosol retrieval with optical spectrograph and infrared
imaging system limb scatter measurements, J. Geophys. Res., 112,
D10217, https://doi.org/10.1029/2006JD008079, 2007. a
Capelle, V., Chédin, A., Siméon, M., Tsamalis, C., Pierangelo, C., Pondrom, M., Crevoisier, C., Crepeau, L., and Scott, N. A.: Evaluation of IASI-derived dust aerosol characteristics over the tropical belt, Atmos. Chem. Phys., 14, 9343–9362, https://doi.org/10.5194/acp-14-9343-2014, 2014. a, b
Carroll, M. L., DiMiceli, C. M., Townshend, J. R. G., Sohlberg, R. A., Elders,
A. I., Sevadiga, S., Sayer, A. M., and Levy, R. C.: Development of an
operational land water mask for MODIS Collection 6, and influence on
downstream data products, Int. J. Digit. Earth, 10, 207–218,
https://doi.org/10.1080/17538947.2016.1232756, 2017. a
Choi, M., Kim, J., Lee, J., Kim, M., Park, Y.-J., Holben, B., Eck, T. F., Li, Z., and Song, C. H.: GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia, Atmos. Meas. Tech., 11, 385–408, https://doi.org/10.5194/amt-11-385-2018, 2018. a, b, c
Clarisse, L., Coheur, P.-F., Prata, F., Hadji-Lazaro, J., Hurtmans, D., and Clerbaux, C.: A unified approach to infrared aerosol remote sensing and type specification, Atmos. Chem. Phys., 13, 2195–2221, https://doi.org/10.5194/acp-13-2195-2013, 2013. a
Colarco, P. R., Kahn, R. A., Remer, L. A., and Levy, R. C.: Impact of satellite viewing-swath width on global and regional aerosol optical thickness statistics and trends, Atmos. Meas. Tech., 7, 2313–2335, https://doi.org/10.5194/amt-7-2313-2014, 2014. a
Collins, W. D., Rasch, P. J., Eaton, B. E., Khattatov, B. V., Lamarque, J.-F.,
and Zender, C. S.: Simulating aerosols using a chemical transport model with
assimilation of satellite aerosol retrievals: Methodology for INDOEX, J.
Geophys. Res., 106, 7313–7336, https://doi.org/10.1029/2000JD900507, 2001. a
Coppo, P., Ricciarelli, B., Brandani, F., Delderfield, J., Ferlet, M., Mutlow, C., Munro, G., Nightingale, T., Smith, D., Bianchi, S., Nicol, P., Kirschstein, S., Hennig, T., Engel, W., Frerick, J., and Nieke, J.: a high accuracy dual scan temperature radiometer for
sea and land surface monitoring from space, J. Mod. Optic., 57,
1815–1830, https://doi.org/10.1080/09500340.2010.503010, 2010. a
Cox, C. and Munk, W.: Measurement of the roughness of the sea surface from
photographs of the Sun's glitter, J. Opt. Soc. Am., 44, 838–850,
https://doi.org/10.1364/JOSA.44.000838, 1954a. a
Cox, C. and Munk, W.: Statistics of the sea surface derived from Sun glitter,
J. Mar. Res., 13, 198–227, 1954b. a
Curier, R. L., Veefkind, J. P., Braak, R., Veihelmann, B., Torres, O., and
de Leeuw, G.: Retrieval of aerosol optical properties from OMI radiances
using a multiwavelength algorithm: Application to western Europe, J.
Geophys. Res., 113, D17S90, https://doi.org/10.1029/2007JD008738, 2008. a
Dawid, A. P.: The Well-Calibrated Bayesian,
J. Am. Stat. Assoc., 77, 605–610, https://doi.org/10.1080/01621459.1982.10477856,
1982. a, b
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, https://doi.org/10.1109/36.700992, 1998. a
Dubovik, O. and King, M. D.: A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements, J. Geophys. Res., 105, 20673–20696, https://doi.org/10.1029/2000JD900282, 2000. a
Dubovik, O., Smirnov, A., Holben, B. N., King, M. D., Kaufman, Y. J., Eck,
T. F., and Slutsker, I.: Accuracy assessments of aerosol optical properties
retrieved from Aerosol Robotic Network (AERONET) Sun and sky radiance
measurements, J. Geophys. Res., 105, 9791–9806, 2000. a
Dubovik, O., Holben, B., Eck, T. F., Smirnov, A., Kaufman, Y. J., King, M. D.,
Tanré, D., and I., S.: 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. a
Dubovik, O., Herman, M., Holdak, A., Lapyonok, T., Tanré, D., Deuzé, J. L., Ducos, F., Sinyuk, A., and Lopatin, A.: Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations, Atmos. Meas. Tech., 4, 975–1018, https://doi.org/10.5194/amt-4-975-2011, 2011. a, b, c, d
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. a
Eck, T. F., B. N. Holben, A. Sinyuk, R. T. Pinker, P. Goloub, H. Chen, B. Chatenet, Z. Li, R. P. Singh, S. N. Tripathi, J. S. Reid, D. M. Giles, O. Dubovik, N. T. O'Neill, A. Smirnov, P. Wang, and X. Xia: Climatological aspects of the optical properties of
fine/coarse mode aerosol mixtures, J. Geophys. Res., 115,
D19205, https://doi.org/10.1029/2010JD014002, 2010. a, b
Eck, T. F., Holben, B. N., Reid, J. S., Mukelabai, M. M., Piketh, S. J., Torres, O., Jethva, H. T., Hyer, E. J., Ward, D. E., Dubovik, O., Sinyuk, A., Schafer, J. S., Giles, D. M., Sorokin, M., Smirnov, A., and Slutsker, I.: A seasonal trend of single scattering albedo in southern African biomass‐burning particles: Implications for satellite products and estimates of emissions for the world's largest biomass‐burning source, J. Geophys. Res.-Atmos., 118, 6414–6432, https://doi.org/10.1002/jgrd.50500, 2013. a
ESA Aerosol CCI team: Climate Change Initiative aerosol portal, available at: https://www.esa-aerosol-cci.org, last access: 29 January 2020. a
EUMETSAT: Polar Multi-Sensor Aerosol Product: ATBD, Tech. rep., EUMETSAT,
Darmstadt, Germany, available at: https://www.eumetsat.int/ (last access: 29 January 2020), report
EUM/TSS/SPE/14/739904, v3B, 2015. a
Fawole, O. G., Cai, X., Levine, J. G., Pinker, R. T., and MacKenzie, A. R.:
Detection of a gas flaring signature in the AERONET optical properties of
aerosols at a tropical station in West Africa, J. Geophys. Res.-Atmos.,
121, 14513–14524, https://doi.org/10.1002/2016JD025584, 2016. a, b
Flamant, P., Cuesta, J., Denneulin, M., Dabas, A., and Huber, D.:
ADM‐Aeolus retrieval algorithms for aerosol and cloud products, Tellus A,
60, 273–288, https://doi.org/10.1111/j.1600-0870.2007.00287.x, 2008. a
Flament, T., Stieglitz, H., Dabas, A., and Huber, D.: Aeolus L2A aerosol
products: principle and first glimpse on performances, in: ESA Living Planet
Symposium, Milan, Italy, available at: https://lps19.esa.int (last access: 29 January 2020), 2019. a
Franz, B. A., Bailey, S. W., Werdell, P. J., and McClain, C. R.:
Sensor-independent approach to the vicarious calibration of satellite ocean
color radiometry, Appl. Opt., 46, 5068–5082, https://doi.org/10.1364/AO.46.005068,
2007. a
Frey, R. A., Ackerman, S. A., Liu, Y., Strabala, K. I., Zhang, H., Key, J. R.,
and Wang, X.: Cloud Detection with MODIS. Part I: Improvements in the
MODIS Cloud Mask for Collection 5, J. Atmos. Ocean. Tech., 25,
1057–1072, https://doi.org/10.1175/2008JTECHA1052.1, 2008. a
Fukuda, S., Nakajima, T., Takanaka, H., Higurashi, A., Kikuchi, N., Nakajima,
T. Y., and Ishida, H.: New approaches to removing cloud shadows and
evaluating the 380 nm surface reflectance for improved aerosol optical
thickness retrievals from the GOSAT/TANSO‐Cloud and Aerosol Imager,
J. Geophys. Res.-Atmos., 118, 13520–13531, https://doi.org/10.1002/2013JD020090,
2013. a
Garay, M. J., Kalashnikova, O. V., and Bull, M. A.: Development and assessment of a higher-spatial-resolution (4.4 km) MISR aerosol optical depth product using AERONET-DRAGON data, Atmos. Chem. Phys., 17, 5095–5106, https://doi.org/10.5194/acp-17-5095-2017, 2017. a
Geogdzhayev, I., Cairns, B., Mishchenko, M. I., Tsigaridis, K., and van Noije,
T.: Model‐based estimation of sampling‐caused uncertainty in aerosol
remote sensing for climate research applications, Q. J. Roy. Meteor. Soc., 140,
2353–2363, https://doi.org/10.1002/qj.2305, 2014. a
Geogdzhayev, I. V. and Mishchenko, M. I.: Validation of long-term Global
Aerosol Climatology Project optical thickness retrievals using
AERONET and MODIS data, Remote Sens., 7, 12588–12605,
https://doi.org/10.3390/rs71012588, 2015. a
Giles, D. M., Holben, B. N., Eck, T. F., Sinyuk, A., Smirnov, A., Slutsker, I.,
Dickerson, R. R., Thompson, A. M., and Schafer, J. S.: An analysis of
AERONET aerosol absorption properties and classifications representative of
aerosol source regions, J. Geophys. Res., 117, D17203, https://doi.org/10.1029/2012JD018127,
2012. a, b, c
Giles, D. M., Sinyuk, A., Sorokin, M. G., Schafer, J. S., Smirnov, A., Slutsker, I., Eck, T. F., Holben, B. N., Lewis, J. R., Campbell, J. R., Welton, E. J., Korkin, S. V., and Lyapustin, A. I.: Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements, Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, 2019. a
Govaerts, Y. and Luffarelli, M.: Joint retrieval of surface reflectance and aerosol properties with continuous variation of the state variables in the solution space – Part 1: theoretical concept, Atmos. Meas. Tech., 11, 6589–6603, https://doi.org/10.5194/amt-11-6589-2018, 2018. a, b, c, d
Govaerts, Y. M., Wagner, S., Lattanzio, A., and Watts, P.: Joint retrieval of
surface reflectance and aerosol optical depth from MSG/SEVIRI observations
with an optimal estimation approach: 1. Theory, J. Geophys. Res., 115,
D0220, https://doi.org/10.1029/2009JD011779, 2010. a
Griggs, M.: Measurements of Atmospheric Aerosol Optical Thickness over Water
Using ERTS-1 Data, JAPCA J. Air Waste Ma., 25,
622–626, https://doi.org/10.1016/0019-1035(72)90009-7, 1975. a
Gupta, P., Levy, R. C., Mattoo, S., Remer, L. A., and Munchak, L. A.: A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS Dark Target retrieval algorithm, Atmos. Meas. Tech., 9, 3293–3308, https://doi.org/10.5194/amt-9-3293-2016, 2016. a
Günther, A., Höpfner, M., Sinnhuber, B.-M., Griessbach, S., Deshler, T., von Clarmann, T., and Stiller, G.: MIPAS observations of volcanic sulfate aerosol and sulfur dioxide in the stratosphere, Atmos. Chem. Phys., 18, 1217–1239, https://doi.org/10.5194/acp-18-1217-2018, 2018. a
Hanel, R., Conrath, B., Hovis, W., Kunde, V., Lowman, P., Maguire, W., Pearl,
J., Pirraglia, J., Prabhakara, C., and Schlachman, B.: Investigation of the
Martian environment by infrared spectroscopy on Mariner 9, Icarus, 17,
423–442, https://doi.org/10.1016/0019-1035(72)90009-7, 1972. a
Hasekamp, O. P. and Landgraf, J.: Retrieval of aerosol properties over land
surfaces: capabilities of multi-viewing-angle intensity and polarization
measurements, Appl. Optics, 46, 3332–3344, https://doi.org/10.1364/AO.46.003332, 2007. a
Heidinger, A. K., Straka III, W. C., Molling, C. C., Sullivan, J. T., and Wu,
X.: Deriving an inter-sensor consistent calibration for the AVHRR solar
reflectance data record, Int. J. Remote Sens., 31, 6493–6517,
https://doi.org/10.1080/01431161.2010.496472, 2010. a
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. a
Holzer-Popp, T., Schroedter, M., and Gesell, G.: Retrieving aerosol optical
depth and type in the boundary layer over land and ocean from simultaneous
GOME spectrometer and ATSR‐2 radiometer measurements, 1, Method
description, J. Geophys. Res., 107, 4578, https://doi.org/10.1029/2001JD002013, 2002. a
Holzer-Popp, T., Schroedter-Homscheidt, M., Breitkreuz, H., Martynenko, D., and Klüser, L.: Improvements of synergetic aerosol retrieval for ENVISAT, Atmos. Chem. Phys., 8, 7651–7672, https://doi.org/10.5194/acp-8-7651-2008, 2008. a
Holzer-Popp, T., de Leeuw, G., Griesfeller, J., Martynenko, D., Klüser, L., Bevan, S., Davies, W., Ducos, F., Deuzé, J. L., Graigner, R. G., Heckel, A., von Hoyningen-Hüne, W., Kolmonen, P., Litvinov, P., North, P., Poulsen, C. A., Ramon, D., Siddans, R., Sogacheva, L., Tanre, D., Thomas, G. E., Vountas, M., Descloitres, J., Griesfeller, J., Kinne, S., Schulz, M., and Pinnock, S.: Aerosol retrieval experiments in the ESA Aerosol_cci project, Atmos. Meas. Tech., 6, 1919–1957, https://doi.org/10.5194/amt-6-1919-2013, 2013. a, b
Hsu, N. C., Lee, J., Sayer, A. M., Kim, W., Bettenhausen, C., and Tsay, S.-C.:
VIIRS Deep Blue aerosol products over land: Extending the EOS
long‐term aerosol data record, J. Geophys. Res.-Atmos., 124,
4026–4053, https://doi.org/10.1029/2018JD029688, 2019. a, b, c
Huang, J., Kondragunta, S., Laszlo, I., Liu, H., Remer, L. A., Zhang, H.,
Superczynski, S., Ciren, P., Holben, B. N., and Petrenko, M.: Validation and
expected error estimation of Suomi‐NPP VIIRS aerosol optical thickness
and Ångström exponent with AERONET, J. Geophys. Res.-Atmos., 121,
7139–7160, https://doi.org/10.1002/2016JD024834, 2016. a, b, c
Hyer, E. J., Reid, J. S., and Zhang, J.: An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals, Atmos. Meas. Tech., 4, 379–408, https://doi.org/10.5194/amt-4-379-2011, 2011. a
ICARE archive team: ICARE Data and Services Center, University of Lille, France, available at https://www.icare.unive-lille1.fr/archive, last access: 29 January 2020. a
Ichoku, C., Chu, D. A., Mattoo, S., Kaufman, Y. J., Remer, L. A., Tanré,
D., Slutsker, I., and Holben, B. N.: A spatio-temporal approach for global
validation and analysis of MODIS aerosol products, Geophys. Res. Lett., 29, 1616,
https://doi.org/10.1029/2001GL013206, 2002. a, b
Ignatov, A. and Stowe, L.: Physical Basis, Premises, and Self-Consistency
Checks of Aerosol Retrievals from TRMM VIRS, J. Appl. Meteorol., 39,
2259–2277,
https://doi.org/10.1175/1520-0450(2001)040<2259:PBPASC>2.0.CO;2,
2000. a
Ignatov, A. and Stowe, L.: Aerosol Retrievals from Individual AVHRR Channels.
Part I: Retrieval Algorithm and Transition from Dave to 6S Radiative
Transfer Model, J. Atmos. Sci., 59, 313–334,
https://doi.org/10.1175/1520-0469(2002)059<0313:ARFIAC>2.0.CO;2,
2002a. a
Ignatov, A. and Stowe, L.: Aerosol Retrievals from Individual AVHRR Channels.
Part II: Quality Control, Probability Distribution Functions, Information
Content, and Consistency Checks of Retrievals, J. Atmos. Sci., 59, 335–362,
https://doi.org/10.1175/1520-0469(2002)059<0335:ARFIAC>2.0.CO;2,
2002b. a
Jethva, H., Torres, O., and Ahn, C.: A 12-year long global record of optical depth of absorbing aerosols above the clouds derived from the OMI/OMACA algorithm, Atmos. Meas. Tech., 11, 5837–5864, https://doi.org/10.5194/amt-11-5837-2018, 2018. a
Johnson, B. T., Osborne, S. R., Haywood, J. M., and Harrison, M. A. J.:
Aircraft measurements of biomass burning aerosol over West Africa during
DABEX, J. Geophys. Res., 113, D00C06, https://doi.org/10.1029/2007JD009451, 2008. a
Kahn, R. A., Gaitley, B. J., Garay, M. J., Diner, D. J., Eck, T. F., Smirnov,
A., and Holben, B. N.: Multiangle Imaging SpectroRadiometer global
aerosol product assessment by comparison with the Aerosol Robotic Network,
J. Geophys. Res., 115, D2320, https://doi.org/10.1029/2010JD014601, 2010. a, b, c
Kahn, R. A., Sayer, A. M., Ahmad, Z., and Franz, B. A.: The Sensitivity of
SeaWiFS Ocean Color Retrievals to Aerosol Amount and Type, J. Atmos. Ocean. Tech., 33, 1185–1209, https://doi.org/10.1175/JTECH-D-15-0121.1, 2016. a, b
Kalashnikova, O. V., Kahn, R., Sokolik, I. N., and Li, W.-H.: Ability of
multiangle remote sensing observations to identify and distinguish mineral
dust types: Optical models and retrievals of optically thick plumes, J.
Geophys. Res., 110, D18S14, https://doi.org/10.1029/2004JD004550, 2005. a, b
Kaufman, Y. J. and Sendra, C.: Algorithm for automatic atmospheric corrections
to visible and near-IR satellite imagery, Int. J. Remote Sens., 9,
1358–1381, https://doi.org/10.1080/01431168808954942, 1988. a
Kaufman, Y. J., Tanré, D., Remer, L. A., Vermote, E. F., Chu, A., and
Holben, B. N.: Operational remote sensing of tropospheric aerosol over land
from EOS moderate resolution imaging spectroradiometer, J. Geophys. Res.,
102, 17051–17067, https://doi.org/10.1029/96JD03988, 1997. a
Kauppi, A., Kolmonen, P., Laine, M., and Tamminen, J.: Aerosol-type retrieval and uncertainty quantification from OMI data, Atmos. Meas. Tech., 10, 4079–4098, https://doi.org/10.5194/amt-10-4079-2017, 2017. a
Kinne, S., O'Donnel, D., Stier, P., Kloster, S., Zhang, K., Schmidt, H., Rast, S., Giorgetta, M., Eck, T. F., and Stevens, B.: MAC‐v1: A new global aerosol climatology for climate studies, J. Adv. Model. Earth Sy., 5, 704–740, https://doi.org/10.1002/jame.20035, 2013. a
Kinne, S., Lytvynov, P., Griesfeller, J., Schulz, M., Stebel, K., Bruehl, C.,
and Xue, Y.: ESA Climate Change Initiative aerosol_cci2
product validation and intercomparidon report (D4.1b), Tech. rep., ESA,
available at: http://cci.esa.int/sites/default/files/Aerosol_cci2_PVIR_v3.41.pdf (last access: 29 January 2020),
ESA Climate Change Initiative, version 3.41, 2017. a, b, c
Klüser, L., Martynenko, D., and Holzer-Popp, T.: Thermal infrared remote sensing of mineral dust over land and ocean: a spectral SVD based retrieval approach for IASI, Atmos. Meas. Tech., 4, 757–773, https://doi.org/10.5194/amt-4-757-2011, 2011. a
Kokhanovsky, A. A. and de Leeuw, G. (Eds.): Satellite Aerosol Remote Sensing
Over Land, Springer (Berlin), https://doi.org/10.1007/978-3-540-69397-0, 2009. a
Kolmonen, P., Sogacheva, L., Virtanen, T. H., de Leeuw, G., and Kulmala, M.:
The ADV/ASV AATSR aerosol retrieval algorithm: current status and
presentation of a full-mission AOD dataset, Int. J. Digit. Earth, 9,
545–561, https://doi.org/10.1080/17538947.2015.1111450, 2016. a, b
Laszlo, I. and Liu, H.: EPS Aerosol Optical Depth (AOD) Algorithm
Theoretical Basis Document, Tech. rep., NOAA, College Park, MD,
USA,
available at: https://www.star.nesdis.noaa.gov/smcd/spb/aq/eidea/subs/desc/VIIRS_EPS_AOD_ATBD.pdf (last access: 29 January 2020),
version 3.0.4, 2017. a
Lenoble, J., Remer, L. A., and Tanré, D. (Eds.): Aerosol Remote Sensing,
Springer (Berlin), https://doi.org/10.1007/978-3-642-17725-5, 2013. a
Levy, R. C., Remer, L. A., and Dubovik, O.: Global aerosol optical properties
and application to Moderate Resolution Imaging Spectroradiometer
aerosol retrieval over land, J. Geophys. Res., 112, D13210,
https://doi.org/10.1029/2006JD007815, 2007. a
Levy, R. C., Leptoukh, G. G., Kahn, R., Zubko, V., Gopalan, A., and Remer,
L. A.: A Critical Look at Deriving Monthly Aerosol Optical Depth From
Satellite Data, IEEE T. Geosci. Remote, 47, 2942–2956,
https://doi.org/10.1109/TGRS.2009.2013842, 2009. a
Li, J., Li, X., Carlson, B. E., Kahn, R. A., Lacis, A. A., Dubovik, O., and Nakajima, T.: Reducing multisensor satellite monthly mean aerosol optical depth uncertainty: 1. Objective assessment of current AERONET locations, J. Geophys. Res.-Atmos., 121, 13609–13627, https://doi.org/10.1002/2016JD025469, 2016. a
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. a, b
Loew, A., W. Bell, L. Brocca, C. E. Bulgin, J. Burdanowitz, X. Calbet, R. V. Donner, D. Ghent, A. Gruber, T. Kaminski, J. Kinzel, C. Klepp, J.‐C. Lambert, G. Schaepman‐Strub, M. Schröder, and T. Verhoelst: Validation practices for satellite‐based Earth observation
data across communities, Rev. Geophys., 55, 779–817,
https://doi.org/10.1002/2017RG000562, 2017. a, b, c
Loughman, R., Bhartia, P. K., Chen, Z., Xu, P., Nyaku, E., and Taha, G.: The Ozone Mapping and Profiler Suite (OMPS) Limb Profiler (LP) Version 1 aerosol extinction retrieval algorithm: theoretical basis, Atmos. Meas. Tech., 11, 2633–2651, https://doi.org/10.5194/amt-11-2633-2018, 2018. a
Luffarelli, M. and Govaerts, Y.: Joint retrieval of surface reflectance and aerosol properties with continuous variation of the state variables in the solution space – Part 2: application to geostationary and polar-orbiting satellite observations, Atmos. Meas. Tech., 12, 791–809, https://doi.org/10.5194/amt-12-791-2019, 2019. a, b, c
Lyapustin, A., Wang, Y., Xiong, X., Meister, G., Platnick, S., Levy, R., Franz, B., Korkin, S., Hilker, T., Tucker, J., Hall, F., Sellers, P., Wu, A., and Angal, A.: Scientific impact of MODIS C5 calibration degradation and C6+ improvements, Atmos. Meas. Tech., 7, 4353–4365, https://doi.org/10.5194/amt-7-4353-2014, 2014. a
Lyapustin, A., Wang, Y., Korkin, S., and Huang, D.: MODIS Collection 6 MAIAC algorithm, Atmos. Meas. Tech., 11, 5741–5765, https://doi.org/10.5194/amt-11-5741-2018, 2018. a, b
Martonchik, J. V., Diner, D. J., Kahn, R. A., Ackerman, T. P., Verstraete,
M. M., Pinty, B., and Gordon, H. R.: Techniques for the retrieval of aerosol
properties over land and ocean using multiangle imaging, IEEE T. Geosci.
Remote, 36, 1212–1227, https://doi.org/10.1109/36.701027, 1998. a, b, c, d
Matsui, T., Kreidenweis, S. M., Pielke, R. A., Schichtel, B., Yu, H., Chin, M.,
Chu, D. A., and Niyogi, D.: Regional comparison and assimilation of GOCART
and MODIS aerosol optical depth across the eastern, Geophys. Res. Lett.,
31, L21101, https://doi.org/10.1029/2004GL021017, 2004. a
McCormick, M. P., Hamill, P., Pepin, T. J., Chu, W. P., Swissler, T. J., and
McMaster, L. R.: Satellite Studies of the Stratospheric Aerosol, B. Am.
Meteorol. Soc., 60, 1038–1047,
https://doi.org/10.1175/1520-0477(1979)060<1038:SSOTSA>2.0.CO;2,
1979. a
McCormick, M. P., C. Brogniez, E.-W. Chiou, W. P. Chu, A. A. Chernikov, D. M. Cunnold, J. DeLuisi, P. A. Durkee, N. F. Elansky, B. M. Herman, P. V. Hobbs, G. S. Kent, J. Lenoble, H. A. Michelsen, A. J. Miller, V. Mohnen, R. Moore, M. C. Pitts, L. R. Poole, V. Ramaswamy, D. Rinds, D. Risley, M. W. Rowland, P. B. Russel, V. K. Saxena, E. P. Shettle, V. E. Sothcott, G. Taha, L. W. Thomason, C. R. Trepte, G. Vali, L. B. Vann, R. E. Veiga, P.-H. Wang, S. C. Wofsy, D. C. Woods, and J. M. Zawodny: SAGE III Algorithm Theoretical Basis Document (ATBD)
Solar and Lunar Algorithm, Tech. rep., NASA Langley Research Center, Hampton,
VA, USA, available at:
https://eospso.gsfc.nasa.gov/sites/default/files/atbd/atbd-sage-solar-lunar.pdf (last access:
29 January 2020),
LaRC 475-00-109 version 2.1, 2002. a
Mei, L., Rozanov, V. V., Vountas, M., Burrows, J. P., Levy, R. C., and Lotz,
W. A.: Retrieval of aerosol optical properties using MERIS observations:
algorithm and some first results, Remote Sens. Environ., 197, 125–140,
https://doi.org/10.1016/j.rse.2016.11.015, 2017. a
Merchant, C. J., Paul, F., Popp, T., Ablain, M., Bontemps, S., Defourny, P., Hollmann, R., Lavergne, T., Laeng, A., de Leeuw, G., Mittaz, J., Poulsen, C., Povey, A. C., Reuter, M., Sathyendranath, S., Sandven, S., Sofieva, V. F., and Wagner, W.: Uncertainty information in climate data records from Earth observation, Earth Syst. Sci. Data, 9, 511–527, https://doi.org/10.5194/essd-9-511-2017, 2017. a, b
Meyer, K., Platnick, S., and Zhang, Z.: Simultaneously inferring above‐cloud
absorbing aerosol optical thickness and underlying liquid phase cloud optical
and microphysical properties using MODIS, J. Geophys. Res.-Atmos., 120,
5524–5547, https://doi.org/10.1002/2015JD023128, 2015. a
Mishchenko, M. I., Travis, L. D., Kahn, R. A., and West, R. A.: Modeling phase
functions for dustlike tropospheric aerosols using a shape mixture of
randomly oriented polydisperse spheroids, J. Geophys. Res., 102,
16831–16847, https://doi.org/10.1029/96JD02110, 1997. a, b
Mishchenko, M. I., Geogdzhayez, I. V., Cairns, B., Rossow, W. B., and Lacis,
A. A.: Aerosol retrievals over the ocean by use of channels 1 and 2 AVHRR
data: sensitivity analysis and preliminary results, Appl. Optics, 38,
7325–7341, https://doi.org/10.1364/AO.38.007325, 1999. a
Mittaz, J., Merchant, C. J., and Wooliams, E. R.: Applying principles of
metrology to historical Earth observations from satellites, Metrologia,
https://doi.org/10.1088/1681-7575/ab1705, 2019. a, b
Mukai, S. and Sano, I.: Algorithm Theoretical Basis Document: Aerosol Retrieval
by Polarization with GCOM-C/SGLI, Tech. rep., JAXA,
available at: https://suzaku.eorc.jaxa.jp/GCOM_C/data/files/ATBD-POL-aerosol-2018.pdf (last access: 29 January 2020),
2018. a
Murphy, A. H.: Skill Scores Based on the Mean Square Error and Their
Relationships to the Correlation Coefficient, Mon. Weather Rev., 116,
2417–2424,
https://doi.org/10.1175/1520-0493(1988)116<2417:SSBOTM>2.0.CO;2,
1988. a
Nakajima, T. Y., Higurashi, A., Hakajima, T., Fukuda, S., and Katagiri, S.:
Development of Cloud and Aerosol Retrieval Algorithms for ADEOS-II/GLI
Mission, Journal of The Remote Sensing Society of Japan, 29, 60–69,
https://doi.org/10.11440/rssj.29.60, 2009. a
NASA AERONET team: AERONET web portal, available at: https://aeronet.gsfc.nasa.gov, last access: 29 January 2020. a
NASA Earthdata team: AERONET web portal, available at: https://earthdata.nasa.gov, last access: 29 January 2020. a
North, P., Heckel, A., Davies, W., S., B., and Grey, W.: Algorithm Theoretical
Basis Document (ATBD), ATSR-2 and AATSR, algorithm ATSR-SU, Tech. rep.,
ESA, available at: http://www.esa-aerosol-cci.org (last access: 29 January 2020), ESA Climate Change
Initiative, version 4.3, 2017. a
North, P. R., Briggs, S. A., Plummer, S. E., and Settle, J. J.: Retrieval of
Land Surface Bidirectional Reflectance and Aerosol Opacity from ATSR-2
Multiangle Imagery, IEEE T. Geosci. Remote, 37, 526–537,
https://doi.org/10.1109/36.739106, 1999. a, b
North, P. R. J., Grey, W., Heckel, A., Fischer, J., Preusker, R., and
Brockmann, C.: MERIS/AATSR Synergy Algorithms for Cloud Screening, Aerosol
Retrieval, and Atmospheric Correction, Tech. rep., Swansea University,
Swansea, UK,
available at: http://ggluck.swansea.ac.uk/ftp/ATSR_SYN/synergy-aerosol-atbd-20100316-v42.doc (last access: 29 January 2020),
ESRIN Contract No. 21090/07/I-LG, 2010. a
O'Neill, N., Eck, T., Smirnov, A., Holben, B., and Thulasiraman, S.: Spectral
Deconvolution algorithm technical memo, Tech. rep., NASA Goddard Space Flight
Center, revision April 26, 2006, version 4, available at:
http://aeronet.gsfc.nasa.gov/new_web/PDF/tauf_tauc_technical_memo1.pdf
(last access: 29 January 2020), 2006. a
O'Neill, N. T., Ignatov, A., Holben, B. N., and Eck, T. F.: The lognormal
distribution as a reference for reporting aerosol optical depth statistics;
Empirical tests using multi-year, multi-site AERONET Sunphotometer
data, Geophys. Res. Lett., 27, 3333–3336, https://doi.org/10.1029/2000GL011581, 2000. a, b
O'Neill, N. T., Eck, T. F., Smirnov, A., Holben, B. N., and Thulasiraman, S.:
Spectral discrimination of coarse and fine mode optical depth, J. Geophys.
Res., 108, 4559–4573, https://doi.org/10.1029/2002JD002975, 2003. a
Patadia, F., Levy, R. C., and Mattoo, S.: Correcting for trace gas absorption when retrieving aerosol optical depth from satellite observations of reflected shortwave radiation, Atmos. Meas. Tech., 11, 3205–3219, https://doi.org/10.5194/amt-11-3205-2018, 2018. a
Pierangelo, C., Chédin, A., Heilliette, S., Jacquinet-Husson, N., and Armante, R.: Dust altitude and infrared optical depth from AIRS, Atmos. Chem. Phys., 4, 1813–1822, https://doi.org/10.5194/acp-4-1813-2004, 2004. a
Popp, T., G. de Leeuw, C. Bingen, C. Brühl, V. Capelle, A. Chedin, L. Clarisse, O. Dubovik, R. Grainger, J. Griesfeller, A. Heckel, S. Kinne, L. Klüser, M. Kosmale, P. Kolmonen, L. Lelli, P. Litvinov, L. Mei, P. North, S. Pinnock, A. Povey, C. Robert, M. Schulz, L. Sogacheva, K. Stebel, D. Stein Zweers, G. Thomas, L. G. Tilstra, S. Vandenbussche, P. Veefkind, M. Vountas, and Y. Xue: Development, Production and Evaluation of Aerosol Climate Data
Records from European Satellite Observations
(Aerosol_cci), Remote Sens., 8, 421, https://doi.org/10.3390/rs8050421,
2016. a, b
Povey, A. C. and Grainger, R. G.: Known and unknown unknowns: uncertainty estimation in satellite remote sensing, Atmos. Meas. Tech., 8, 4699–4718, https://doi.org/10.5194/amt-8-4699-2015, 2015. a
Povey, A. C. and Grainger, R. G.: Toward More Representative Gridded Satellite
Products, IEEE Geosci. Remote S., 672–676, https://doi.org/10.1109/LGRS.2018.2881762,
2019. a
Povey, A. C., Grainger, R. G., Peters, D. M., and Agnew, J. L.: Retrieval of aerosol backscatter, extinction, and lidar ratio from Raman lidar with optimal estimation, Atmos. Meas. Tech., 7, 757–776, https://doi.org/10.5194/amt-7-757-2014, 2014. a
Rahman, H., Pinty, B., and Verstraete, M. M.: Coupled surface-atmosphere
reflectance (CSAR) model, 2. Semiempirical surface model usable with NOAA
Advanced Very High Resolution Radiometer Data, J. Geophys. Res.,
98, 20791–20801, https://doi.org/10.1029/93JD02072, 1993. a
Rieger, L. A., Zawada, D. J., Bourassa, A. E., and Degenstein, D. A.: A
multi‐wavelength retrieval approach for improved OSIRIS aerosol
extinction retrievals, J. Geophys. Res.-Atmos., 124,
7287–7407, https://doi.org/10.1029/2018JD029897, 2019. a
Sayer, A. M. and Knobelspiesse, K. D.: How should we aggregate data? Methods accounting for the numerical distributions, with an assessment of aerosol optical depth, Atmos. Chem. Phys., 19, 15023–15048, https://doi.org/10.5194/acp-19-15023-2019, 2019. a, b
Sayer, A. M., Thomas, G. E., and Grainger, R. G.: A sea surface reflectance model for (A)ATSR, and application to aerosol retrievals, Atmos. Meas. Tech., 3, 813–838, https://doi.org/10.5194/amt-3-813-2010, 2010a. a, b, c, d
Sayer, A. M., Thomas, G. E., Palmer, P. I., and Grainger, R. G.: Some implications of sampling choices on comparisons between satellite and model aerosol optical depth fields, Atmos. Chem. Phys., 10, 10705–10716, https://doi.org/10.5194/acp-10-10705-2010,
https://doi.org/10.5194/acp-10-10705-2010, 2010b. a
Sayer, A. M., Hsu, N. C., Bettenhausen, C., Ahmad, Z., Holben, B. N., Smirnov,
A., Thomas, G. E., and Zhang, J.: SeaWiFS Ocean Aerosol Retrieval (SOAR):
Algorithm, validation, and comparison with other data sets, J. Geophys.
Res., 117, D03206, https://doi.org/10.1029/2011JD016599, 2012a. a
Sayer, A. M., Hsu, N. C., Bettenhausen, C., Jeong, M.-J., Holben, B. N., and Zhang, J.: Global and regional evaluation of over-land spectral aerosol optical depth retrievals from SeaWiFS, Atmos. Meas. Tech., 5, 1761–1778, https://doi.org/10.5194/amt-5-1761-2012, 2012b. a
Sayer, A. M., Thomas, G. E., Grainger, R. G., Carboni, E., Poulsen, C., and
Siddans, R.: Use of MODIS-derived surface reflectance data in the
ORAC-AATSR aerosol retrieval algorithm: Impact of differences between
sensor spectral response functions, Remote Sens. Environ., 116, 177–188,
https://doi.org/10.1016/j.rse.2011.02.029, 2012c. a
Sayer, A. M., Hsu, N. C., Bettenhausen, C., Lee, J., Redemann, J., Schmid, B.,
and Shinozuka, Y.: Extending “Deep Blue” aerosol retrieval coverage to
cases of absorbing aerosols above clouds: Sensitivity analysis and first case
studies, J. Geophys. Res.-Atmos., 121, 4830–4854,
https://doi.org/10.1002/2015JD024729, 2016. a, b
Sayer, A. M., Hsu, N. C., Lee, J., Carletta, N., Chen, S.-H., and Smirnov, A.:
Evaluation of NASA Deep Blue/SOAR aerosol retrieval algorithms applied to
AVHRR measurements, J. Geophys. Res.-Atmos., 122, 9945–9967,
https://doi.org/10.1002/2017JD026934, 2017. a, b
Sayer, A. M., N. C. Hsu, J. Lee, W. V. Kim, O. Dubovik, S. T. Dutcher, D. Huang, P. Litvinov, A. Lyapustin, J. L. Tackett, and D. M. Winker: Validation of SOAR VIIRS over‐water aerosol retrievals
and context within the global satellite aerosol data record, J. Geophys. Res.-Atmos., 123, 13496–13526, https://doi.org/10.1029/2018JD029465, 2018. a
Sayer, A. M., Hsu, N. C., Lee, J., Kim, W., and Dutcher, S.: Validation,
stability, and consistency of MODIS Collection 6.1 and VIIRS Version 1
Deep Blue aerosol data over land, J. Geophys. Res.-Atmos., 124,
4658–4688, https://doi.org/10.1029/2018JD029598, 2019a. a
Sayer, A. M., Hsu, N. C., Lee, J., Kim, W. V., Burton, S., Fenn, M. A., Ferrare, R. A., Kacenelenbogen, M., LeBlanc, S., Pistone, K., Redemann, J., Segal-Rozenhaimer, M., Shinozuka, Y., and Tsay, S.-C.: Two decades observing smoke above clouds in the south-eastern Atlantic Ocean: Deep Blue algorithm updates and validation with ORACLES field campaign data, Atmos. Meas. Tech., 12, 3595–3627, https://doi.org/10.5194/amt-12-3595-2019,
2019b. a, b
Schuster, G. L., Dubovik, O., and Holben, B. N.: Angstrom exponent and bimodal
aerosol size distributions, J. Geophys. Res., 111, D07207,
https://doi.org/10.1029/2005JD006328, 2006. a
Schutgens, N., Tsyro, S., Gryspeerdt, E., Goto, D., Weigum, N., Schulz, M., and Stier, P.: On the spatio-temporal representativeness of observations, Atmos. Chem. Phys., 17, 9761–9780, https://doi.org/10.5194/acp-17-9761-2017, 2017. a, b
Schutgens, N. A. J.: Site representativity of AERONET and GAW remotely sensed AOT and AAOT observations, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-767, in review, 2019. a
Schutgens, N. A. J., Gryspeerdt, E., Weigum, N., Tsyro, S., Goto, D., Schulz, M., and Stier, P.: Will a perfect model agree with perfect observations? The impact of spatial sampling, Atmos. Chem. Phys., 16, 6335–6353, https://doi.org/10.5194/acp-16-6335-2016, 2016. a, b
Shi, C., Hashimoto, M., and Nakajima, T.: Remote sensing of aerosol properties from multi-wavelength and multi-pixel information over the ocean, Atmos. Chem. Phys., 19, 2461–2475, https://doi.org/10.5194/acp-19-2461-2019, 2019. a
Shi, Y., Zhang, J., Reid, J. S., Hyer, E. J., and Hsu, N. C.: Critical evaluation of the MODIS Deep Blue aerosol optical depth product for data assimilation over North Africa, Atmos. Meas. Tech., 6, 949–969, https://doi.org/10.5194/amt-6-949-2013, 2013. a
Sica, R. J. and Haefele, A.: Retrieval of temperature from a multiple-channel
Rayleigh-scatter lidar using an optimal estimation method, Appl. Optics, 54,
1872–1889, https://doi.org/10.1364/AO.54.001872, 2015. a
Smirnov, A., B. N. Holben, I. Slutsker, D. M. Giles, C. R. McClain, T. F. Eck, S. M. Sakerin, A. Macke, P. Croot, G. Zibordi, P. K. Quinn, J. Sciare, S. Kinne, M. Harvey, T. J. Smyth, S. Piketh, T. Zielinski, A. Proshutinsky, J. I. Goes, N. B. Nelson, P. Larouche, V. F. Radionov, P. Goloub, K. Krishna Moorthy, R. Matarrese, E. J. Robertson, and F. Jourdin: Maritime Aerosol Network as a component of Aerosol Robotic
Network, J. Geophys. Res., 112,
D06204, https://doi.org/10.1029/2008JD011257, 2009. a
Smirnov, A., Holben, B. N., Giles, D. M., Slutsker, I., O'Neill, N. T., Eck, T. F., Macke, A., Croot, P., Courcoux, Y., Sakerin, S. M., Smyth, T. J., Zielinski, T., Zibordi, G., Goes, J. I., Harvey, M. J., Quinn, P. K., Nelson, N. B., Radionov, V. F., Duarte, C. M., Losno, R., Sciare, J., Voss, K. J., Kinne, S., Nalli, N. R., Joseph, E., Krishna Moorthy, K., Covert, D. S., Gulev, S. K., Milinevsky, G., Larouche, P., Belanger, S., Horne, E., Chin, M., Remer, L. A., Kahn, R. A., Reid, J. S., Schulz, M., Heald, C. L., Zhang, J., Lapina, K., Kleidman, R. G., Griesfeller, J., Gaitley, B. J., Tan, Q., and Diehl, T. L.: Maritime aerosol network as a component of AERONET – first results and comparison with global aerosol models and satellite retrievals, Atmos. Meas. Tech., 4, 583–597, https://doi.org/10.5194/amt-4-583-2011, 2011. a
Tanré, D., Herman, M., and Kaufman, Y. J.: Information on aerosol size
distribution contained in solar reflected spectral radiances, J. Geophys.
Res., 101, 19043–19060, https://doi.org/10.1029/96JD00333, 1996. a, b
Tanré, D., Kaufman, Y. J., Herman, M., and Mattoo, S.: Remote sensing of
aerosol properties over oceans using the MODIS/EOS spectral radiances, J.
Geophys. Res., 102, 16971–16988, https://doi.org/10.1029/96JD03437, 1997. a, b
Thomas, G. E., Poulsen, C. A., Sayer, A. M., Marsh, S. H., Dean, S. M., Carboni, E., Siddans, R., Grainger, R. G., and Lawrence, B. N.: The GRAPE aerosol retrieval algorithm, Atmos. Meas. Tech., 2, 679–701, https://doi.org/10.5194/amt-2-679-2009, 2009. a, b, c
Thomas, G. E., Poulsen, C. A., Siddans, R., Sayer, A. M., Carboni, E., Marsh, S. H., Dean, S. M., Grainger, R. G., and Lawrence, B. N.: Validation of the GRAPE single view aerosol retrieval for ATSR-2 and insights into the long term global AOD trend over the ocean, Atmos. Chem. Phys., 10, 4849–4866, https://doi.org/10.5194/acp-10-4849-2010, 2010. a, b
Torres, O., Bhartia, P. K., Herman, J. R., Ahmad, Z., and Gleason, J.:
Derivation of aerosol properties from satellite measurements of backscattered
ultraviolet radiation: Theoretical basis, J. Geophys. Res., 103,
17099–17110, https://doi.org/10.1029/98JD00900, 1998. a, b
Torres, O., Bhartia, P. K., Herman, J. R., Sinyuk, A., Ginoux, P., and Holben,
B.: A Long-Term Record of Aerosol Optical Depth from TOMS Observations and
Comparison to AERONET Measurements, J. Atmos. Sci., 59, 398–413,
https://doi.org/10.1175/1520-0469(2002)059<0398:ALTROA>2.0.CO;2,
2002. a
Torres, O., Jethva, J., and Bhartia, P. K.: Retrieval of Aerosol Optical Depth
above Clouds from OMI Observations: Sensitivity Analysis and Case Studies,
J. Atmos. Sci., 69, 1037–1053, https://doi.org/10.1175/JAS-D-11-0130.1, 2012. a
von Clarmann, T., Degenstein, D. A., Livesey, N. J., Bender, S., Braverman, A., Butz, A., Compernolle, S., Damadeo, R., Dueck, S., Eriksson, P., Funke, B., Johnson, M. C., Kasai, Y., Keppens, A., Kleinert, A., Kramarova, N. A., Laeng, A., Payne, V. H., Rozanov, A., Sato, T. O., Schneider, M., Sheese, P., Sofieva, V., Stiller, G. P., von Savigny, C., and Zawada, D.: Estimating and Reporting Uncertainties in Remotely Sensed Atmospheric Composition and Temperature, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-350, in review, 2019. a, b, c
Vandenbussche, S., Kochenova, S., Vandaele, A. C., Kumps, N., and De Mazière, M.: Retrieval of desert dust aerosol vertical profiles from IASI measurements in the TIR atmospheric window, Atmos. Meas. Tech., 6, 2577–2591, https://doi.org/10.5194/amt-6-2577-2013, 2013. a
Vanhellemont, F., Mateshvili, N., Blanot, L., Robert, C. É., Bingen, C., Sofieva, V., Dalaudier, F., Tétard, C., Fussen, D., Dekemper, E., Kyrölä, E., Laine, M., Tamminen, J., and Zehner, C.: AerGOM, an improved algorithm for stratospheric aerosol extinction retrieval from GOMOS observations – Part 1: Algorithm description, Atmos. Meas. Tech., 9, 4687–4700, https://doi.org/10.5194/amt-9-4687-2016, 2016. a
Veihelmann, B., Levelt, P. F., Stammes, P., and Veefkind, J. P.: Simulation study of the aerosol information content in OMI spectral reflectance measurements, Atmos. Chem. Phys., 7, 3115–3127, https://doi.org/10.5194/acp-7-3115-2007, 2007. a
Virtanen, T. H., Kolmonen, P., Sogacheva, L., Rodríguez, E., Saponaro, G., and de Leeuw, G.: Collocation mismatch uncertainties in satellite aerosol retrieval validation, Atmos. Meas. Tech., 11, 925–938, https://doi.org/10.5194/amt-11-925-2018, 2018. a, b, c
Wagner, F. and Silva, A. M.: Some considerations about Ångström exponent distributions, Atmos. Chem. Phys., 8, 481–489, https://doi.org/10.5194/acp-8-481-2008, 2008. a
Weaver, C. J., Joiner, J., and Ginoux, P.: Mineral aerosol contamination of
TIROS Operational Vertical Sounder (TOVS) temperature and moisture
retrievals, J. Geophys. Res., 108, 4246, https://doi.org/10.1029/2002JD002571, 2003. a
Witek, M. L., Diner, D. J., Garay, M. J., Xu, F., Bull, M. A., and Seidel,
F. C.: Improving MISR AOD Retrievals With Low-Light-Level Corrections for
Veiling Light, IEEE T. Geosci. Remote, 56, 1251–1268,
https://doi.org/10.1109/TGRS.2017.2727342, 2018a. a
Witek, M. L., Garay, M. J., Diner, D. J., and Smirnov, A.: Oceanic aerosol loading derived from MISR's 4.4 km (V23) Aerosol Product, J. Geophys. Res.-Atmos., 124, 10154–10174, https://doi.org/10.1029/2019JD031065, 2019. a
Working Group 1: Evaluation of measurement data – Guide to the expression
of uncertainty in measurement, Tech. rep., Joint Committee for Guides in
Metrology,
available at: http://www.iso.org/sites/JCGM/GUM-introduction.htm (last access: 29 January 2020), Tech.
Rep. JCGM 100:2008, 2008. a
Xu, F., Diner, D. J., Dubovik, O., and Schechner, Y.: A Correlated Multi-Pixel Inversion Approach for Aerosol Remote Sensing, Remote Sens., 11, 746, https://doi.org/10.3390/rs11070746, 2019. a
Yoshida, M., Kikuchi, M., Nagao, T. M., Muramaki, H., Nomaki, T., and
Higurashi, A.: Common retrieval of aerosol properties for imaging satellite
sensors, J. Meteorol. Soc. Jpn., 96B, 193–209,
https://doi.org/10.2151/jmsj.2018-039, 2018. a
Young, S. A. and Vaughan, M. A.: The retrieval of profiles of particulate
extinction from Cloud Aerosol Lidar Infrared Pathfinder Satellite
Observations (CALIPSO) data: Algorithm description, J. Atmos. Ocean. Tech., 26, 1105–1119, https://doi.org/10.1175/2008JTECHA1221.1, 2009. a
Young, S. A., Vaughan, M. A., Kuehn, R. E., and Winker, D. M.: The Retrieval of
Profiles of Particulate Extinction from Cloud–Aerosol Lidar and
Infrared Pathfinder Satellite Observations (CALIPSO) Data:
Uncertainty and Error Sensitivity Analyses, J. Atmos. Ocean. Tech., 30,
395–428, https://doi.org/10.1175/JTECH-D-12-00046.1, 2013. a, b, c
Young, S. A., Vaughan, M. A., Garnier, A., Tackett, J. L., Lambeth, J. D., and Powell, K. A.: Extinction and optical depth retrievals for CALIPSO's Version 4 data release, Atmos. Meas. Tech., 11, 5701–5727, https://doi.org/10.5194/amt-11-5701-2018, 2018. a
Zhang, J. and Reid, J. S.: MODIS Aerosol Product Analysis for Data
Assimilation: Assessment of Over-Ocean level 2 Aerosol Optical Thickness
Retrievals, J. Geophys. Res., 111, D22207, https://doi.org/10.1029/2005JD006898, 2006. a
Zhao, T. X.‐P., Chan, P. K., and Heidinger, A. K.: A global survey of the effect of cloud contamination on the aerosol optical thickness and its long‐term trend derived from operational AVHRR satellite observations, J. Geophys. Res.-Atmos., 118, 2849–2857, https://doi.org/10.1002/jgrd.50278, 2013. a
Zhao, X.: Climate Data Record (CDR) Program Climate Algorithm Theoretical
Basis Document (C‐ATBD) AVHRR Aerosol Optical Thickness (AOT), Tech.
rep., NOAA, College Park, MD, USA,
available at: https://www.ncei.noaa.gov/data/avhrr-aerosol-optical-thickness/access/doc/ (last access: 29 January 2020),
tech. Rep. CDRP‐ATBD‐0096 revision 3, 2016. a
Short summary
Satellite measurements of the Earth are routinely processed to estimate useful quantities; one example is the amount of atmospheric aerosols (which are particles such as mineral dust, smoke, volcanic ash, or sea spray). As with all measurements and inferred quantities, there is some degree of uncertainty in this process.
There are various methods to estimate these uncertainties. A related question is the following: how reliable are these estimates? This paper presents a method to assess them.
Satellite measurements of the Earth are routinely processed to estimate useful quantities; one...
