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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Sara Herrero-Anta, Sabine Eckhardt, Nikolaos Evangeliou, Stefania Gilardoni, Sandra Graßl, Dominic Heslin-Rees, Stelios Kazadzis, Natalia Kouremeti, Radovan Krejci, David Mateos, Mauro Mazzola, Christoph Ritter, Roberto Román, Kerstin Stebel, and Tymon Zielinski
Atmos. Chem. Phys., 26, 1435–1457, https://doi.org/10.5194/acp-26-1435-2026, https://doi.org/10.5194/acp-26-1435-2026, 2026
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In summer 2019, unusually high aerosol concentrations were measured in the Arctic, extending through the troposphere and stratosphere up to 16 km. Using multiple instruments and models, aerosol particles were linked to wildfires, volcanic eruptions, and anthropogenic pollution. The aerosol consisted of spherical, fine-mode, weakly absorbing particles, which significantly reduced direct solar radiation. This combined approach improves understanding of Arctic aerosol events and climate processes.
Andrew M. Sayer, Brian Cairns, Kirk D. Knobelspiesse, Luca Lelli, Chamara Rajapakshe, Scott E. Giangrande, Gareth E. Thomas, and Damao Zhang
Atmos. Meas. Tech., 18, 6681–6703, https://doi.org/10.5194/amt-18-6681-2025, https://doi.org/10.5194/amt-18-6681-2025, 2025
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Rona L. Thompson, Nalini Krishnankutty, Ignacio Pisso, Philipp Schneider, Kerstin Stebel, Motoki Sasakawa, Andreas Stohl, and Stephen M. Platt
Atmos. Chem. Phys., 25, 12737–12751, https://doi.org/10.5194/acp-25-12737-2025, https://doi.org/10.5194/acp-25-12737-2025, 2025
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Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, and Antti Lipponen
Atmos. Meas. Tech., 18, 4771–4789, https://doi.org/10.5194/amt-18-4771-2025, https://doi.org/10.5194/amt-18-4771-2025, 2025
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This study proposes a novel machine learning method to estimate pollution levels (PM2.5) on urban areas at fine scale. Our model generates hourly PM2.5 maps with high spatial resolution, by combining satellite data, ground measurements, geophysical model data, and different geographical indicators. The model properly accounts for spatial and temporal variability of the urban pollution levels, and can be highly beneficial for air quality monitoring and health protection.
Xin Xi, Jun Wang, Zhendong Lu, Andrew M. Sayer, Jaehwa Lee, Robert C. Levy, Yujie Wang, Alexei Lyapustin, Hongqing Liu, Istvan Laszlo, Changwoo Ahn, Omar Torres, Sabur Abdullaev, James Limbacher, and Ralph A. Kahn
Atmos. Chem. Phys., 25, 7403–7429, https://doi.org/10.5194/acp-25-7403-2025, https://doi.org/10.5194/acp-25-7403-2025, 2025
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Ulrike Stöffelmair, Thomas Popp, Marco Vountas, and Hartmut Bösch
Atmos. Meas. Tech., 18, 2005–2020, https://doi.org/10.5194/amt-18-2005-2025, https://doi.org/10.5194/amt-18-2005-2025, 2025
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Aerosol composition has a large influence on the climate system. This study uses realistic simulated scenarios to look at the information content of a combination of three satellite-based instruments (SLSTR, IASI and GOME-2). It shows that it is possible to retrieve 6 to 15 different aerosol components in addition to the aerosol optical depth (AOD) and different surface parameters. The results are used for the development of a synergistic multi-sensor retrieval algorithm.
Timo H. Virtanen, Anu-Maija Sundström, Elli Suhonen, Antti Lipponen, Antti Arola, Christopher O'Dell, Robert R. Nelson, and Hannakaisa Lindqvist
Atmos. Meas. Tech., 18, 929–952, https://doi.org/10.5194/amt-18-929-2025, https://doi.org/10.5194/amt-18-929-2025, 2025
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We find that small particles suspended in the air (aerosols) affect the satellite observations of carbon dioxide (CO2) made by the Orbiting Carbon Observatory-2 satellite instrument. Satellite estimates of CO2 appear to be too high for clean areas and too low for polluted areas. Our results show that CO2 and aerosols are often co-emitted, and this is partly masked out in the current retrievals. Correctly accounting for the aerosol effect is important for CO2 emission estimates by satellites.
Harri Kokkola, Juha Tonttila, Silvia M. Calderón, Sami Romakkaniemi, Antti Lipponen, Aapo Peräkorpi, Tero Mielonen, Edward Gryspeerdt, Timo Henrik Virtanen, Pekka Kolmonen, and Antti Arola
Atmos. Chem. Phys., 25, 1533–1543, https://doi.org/10.5194/acp-25-1533-2025, https://doi.org/10.5194/acp-25-1533-2025, 2025
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Understanding how atmospheric aerosols affect clouds is a scientific challenge. One question is how aerosols affects the amount of cloud water. We used a cloud-scale model to study these effects on marine clouds. The study showed that variations in cloud properties and instrument noise can cause bias in satellite-derived cloud water content. However, our results suggest that for similar weather conditions with well-defined aerosol concentrations, satellite data can reliably track these effects.
Mariya Petrenko, Ralph Kahn, Mian Chin, Susanne E. Bauer, Tommi Bergman, Huisheng Bian, Gabriele Curci, Ben Johnson, Johannes W. 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
Atmos. Chem. Phys., 25, 1545–1567, https://doi.org/10.5194/acp-25-1545-2025, https://doi.org/10.5194/acp-25-1545-2025, 2025
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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.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024, https://doi.org/10.5194/amt-17-7027-2024, 2024
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Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, and Antti Lipponen
Atmos. Meas. Tech., 17, 5747–5764, https://doi.org/10.5194/amt-17-5747-2024, https://doi.org/10.5194/amt-17-5747-2024, 2024
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This study focuses on improving the accuracy of satellite-based PM2.5 retrieval, crucial for monitoring air quality and its impact on health. It employs machine learning to correct the AOD-to-PM2.5 conversion ratio using various data sources. The approach produces high-resolution PM2.5 estimates with improved accuracy. The method is flexible and can incorporate additional training data from different sources, making it a valuable tool for air quality monitoring and epidemiological studies.
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.
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.
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
Atmos. Chem. Phys., 24, 1329–1344, https://doi.org/10.5194/acp-24-1329-2024, https://doi.org/10.5194/acp-24-1329-2024, 2024
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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.
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.
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.
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.
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.
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...
