Articles | Volume 14, issue 4
https://doi.org/10.5194/amt-14-2981-2021
© Author(s) 2021. 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-14-2981-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Apr 2021
Research article |
| 22 Apr 2021
| 22 Apr 2021
Model-enforced post-process correction of satellite aerosol retrievals
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Ville Kolehmainen
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Pekka Kolmonen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Antti Kukkurainen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Tero Mielonen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Neus Sabater
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Larisa Sogacheva
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Timo H. Virtanen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Antti Arola
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Related authors
Antti Kukkurainen, Antti Mikkonen, Antti Arola, Antti Lipponen, Ville Kolehmainen, and Neus Sabater
EGUsphere, https://doi.org/10.5194/egusphere-2025-220, https://doi.org/10.5194/egusphere-2025-220, 2025
Short summary
Short summary
HAPI2LIBIS is a new software tool that enhances the capabilities of the radiative transfer model libRadtran. It simplifies high wavelength resolution simulations by using up-to-date molecular data from the HITRAN database and streamlining computations. This tool helps researchers analyze how gases interact with radiation in the Earth's atmosphere and surface, improving atmospheric studies and satellite observations, and making detailed modeling more accurate and accessible.
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
Short summary
Short summary
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.
Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, and Antti Lipponen
EGUsphere, https://doi.org/10.5194/egusphere-2024-4056, https://doi.org/10.5194/egusphere-2024-4056, 2025
Short summary
Short summary
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 (100 meters), 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, offering relevant insights for air quality monitoring and health protection.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Antti Kukkurainen, Antti Mikkonen, Antti Arola, Antti Lipponen, Ville Kolehmainen, and Neus Sabater
EGUsphere, https://doi.org/10.5194/egusphere-2025-220, https://doi.org/10.5194/egusphere-2025-220, 2025
Short summary
Short summary
HAPI2LIBIS is a new software tool that enhances the capabilities of the radiative transfer model libRadtran. It simplifies high wavelength resolution simulations by using up-to-date molecular data from the HITRAN database and streamlining computations. This tool helps researchers analyze how gases interact with radiation in the Earth's atmosphere and surface, improving atmospheric studies and satellite observations, and making detailed modeling more accurate and accessible.
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
Short summary
Short summary
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.
Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, and Antti Lipponen
EGUsphere, https://doi.org/10.5194/egusphere-2024-4056, https://doi.org/10.5194/egusphere-2024-4056, 2025
Short summary
Short summary
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 (100 meters), 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, offering relevant insights for air quality monitoring and health protection.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Markku Kulmala, Anna Lintunen, Hanna Lappalainen, Annele Virtanen, Chao Yan, Ekaterina Ezhova, Tuomo Nieminen, Ilona Riipinen, Risto Makkonen, Johanna Tamminen, Anu-Maija Sundström, Antti Arola, Armin Hansel, Kari Lehtinen, Timo Vesala, Tuukka Petäjä, Jaana Bäck, Tom Kokkonen, and Veli-Matti Kerminen
Atmos. Chem. Phys., 23, 14949–14971, https://doi.org/10.5194/acp-23-14949-2023, https://doi.org/10.5194/acp-23-14949-2023, 2023
Short summary
Short summary
To be able to meet global grand challenges, we need comprehensive open data with proper metadata. In this opinion paper, we describe the SMEAR (Station for Measuring Earth surface – Atmosphere Relations) concept and include several examples (cases), such as new particle formation and growth, feedback loops and the effect of COVID-19, and what has been learned from these investigations. The future needs and the potential of comprehensive observations of the environment are summarized.
William Wandji Nyamsi, Yves-Marie Saint-Drenan, Antti Arola, and Lucien Wald
Atmos. Meas. Tech., 16, 2001–2036, https://doi.org/10.5194/amt-16-2001-2023, https://doi.org/10.5194/amt-16-2001-2023, 2023
Short summary
Short summary
The McClear service provides estimates of surface solar irradiances in cloud-free conditions. By comparing McClear estimates to 1 min measurements performed in Sub-Saharan Africa and the Maldives Archipelago in the Indian Ocean, McClear accurately estimates global irradiance and tends to overestimate direct irrradiance. This work establishes a general overview of the performance of the McClear service.
Meri Räty, Larisa Sogacheva, Helmi-Marja Keskinen, Veli-Matti Kerminen, Tuomo Nieminen, Tuukka Petäjä, Ekaterina Ezhova, and Markku Kulmala
Atmos. Chem. Phys., 23, 3779–3798, https://doi.org/10.5194/acp-23-3779-2023, https://doi.org/10.5194/acp-23-3779-2023, 2023
Short summary
Short summary
We utilised back trajectories to identify the source region of air masses arriving in Hyytiälä, Finland, and their travel time over forests. Combined with atmospheric observations, they revealed how air mass transport over the Fennoscandian boreal forest during the growing season produced an accumulation of cloud condensation nuclei and humidity, promoting cloudiness and precipitation. By 55 h of transport, air masses appeared to reach a balanced state with the forest environment.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Hugues Brenot, Nicolas Theys, Lieven Clarisse, Jeroen van Gent, Daniel R. Hurtmans, Sophie Vandenbussche, Nikolaos Papagiannopoulos, Lucia Mona, Timo Virtanen, Andreas Uppstu, Mikhail Sofiev, Luca Bugliaro, Margarita Vázquez-Navarro, Pascal Hedelt, Michelle Maree Parks, Sara Barsotti, Mauro Coltelli, William Moreland, Simona Scollo, Giuseppe Salerno, Delia Arnold-Arias, Marcus Hirtl, Tuomas Peltonen, Juhani Lahtinen, Klaus Sievers, Florian Lipok, Rolf Rüfenacht, Alexander Haefele, Maxime Hervo, Saskia Wagenaar, Wim Som de Cerff, Jos de Laat, Arnoud Apituley, Piet Stammes, Quentin Laffineur, Andy Delcloo, Robertson Lennart, Carl-Herbert Rokitansky, Arturo Vargas, Markus Kerschbaum, Christian Resch, Raimund Zopp, Matthieu Plu, Vincent-Henri Peuch, Michel Van Roozendael, and Gerhard Wotawa
Nat. Hazards Earth Syst. Sci., 21, 3367–3405, https://doi.org/10.5194/nhess-21-3367-2021, https://doi.org/10.5194/nhess-21-3367-2021, 2021
Short summary
Short summary
The purpose of the EUNADICS-AV (European Natural Airborne Disaster Information and Coordination System for Aviation) prototype early warning system (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986).
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
Short summary
Short summary
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
Short summary
Short summary
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.
Kevin Lamy, Thierry Portafaix, Colette Brogniez, Kaisa Lakkala, Mikko R. A. Pitkänen, Antti Arola, Jean-Baptiste Forestier, Vincent Amelie, Mohamed Abdoulwahab Toihir, and Solofoarisoa Rakotoniaina
Earth Syst. Sci. Data, 13, 4275–4301, https://doi.org/10.5194/essd-13-4275-2021, https://doi.org/10.5194/essd-13-4275-2021, 2021
Short summary
Short summary
This paper is about the presentation of the UV-Indien measurement network. This network measures the ultraviolet radiation emitted by the Sun received at the Earth's surface and the clouding above each station. It has been deployed at several sites in the Indian Ocean region representing different environmental conditions. A description of the instruments and their calibration, maintenance, and data processing is presented in this paper along with a valuation of the data quality.
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
Short summary
Short summary
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.
Nicola Zoppetti, Simone Ceccherini, Bruno Carli, Samuele Del Bianco, Marco Gai, Cecilia Tirelli, Flavio Barbara, Rossana Dragani, Antti Arola, Jukka Kujanpää, Jacob C. A. van Peet, Ronald van der A, and Ugo Cortesi
Atmos. Meas. Tech., 14, 2041–2053, https://doi.org/10.5194/amt-14-2041-2021, https://doi.org/10.5194/amt-14-2041-2021, 2021
Short summary
Short summary
The new platforms for Earth observation from space will provide an enormous amount of data that can be hard to exploit as a whole. The Complete Data Fusion algorithm can reduce the data volume while retaining the information of the full dataset. In this work, we applied the Complete Data Fusion algorithm to simulated ozone profiles, and the results show that the fused products are characterized by higher information content compared to individual L2 products.
Antti Ruuskanen, Sami Romakkaniemi, Harri Kokkola, Antti Arola, Santtu Mikkonen, Harri Portin, Annele Virtanen, Kari E. J. Lehtinen, Mika Komppula, and Ari Leskinen
Atmos. Chem. Phys., 21, 1683–1695, https://doi.org/10.5194/acp-21-1683-2021, https://doi.org/10.5194/acp-21-1683-2021, 2021
Short summary
Short summary
The study focuses mainly on cloud-scavenging efficiency of absorbing particulate matter (mainly black carbon) but additionally covers cloud-scavenging efficiency of scattering particles and statistics of cloud condensation nuclei. The main findings give insight into how black carbon is distributed in different particle sizes and the sensitivity to cloud scavenged. The main findings are useful for large-scale modelling for evaluating cloud scavenging.
Jonas Gliß, Augustin Mortier, Michael Schulz, Elisabeth Andrews, Yves Balkanski, Susanne E. Bauer, Anna M. K. Benedictow, Huisheng Bian, Ramiro Checa-Garcia, Mian Chin, Paul Ginoux, Jan J. Griesfeller, Andreas Heckel, Zak Kipling, Alf Kirkevåg, Harri Kokkola, Paolo Laj, Philippe Le Sager, Marianne Tronstad Lund, Cathrine Lund Myhre, Hitoshi Matsui, Gunnar Myhre, David Neubauer, Twan van Noije, Peter North, Dirk J. L. Olivié, Samuel Rémy, Larisa Sogacheva, Toshihiko Takemura, Kostas Tsigaridis, and Svetlana G. Tsyro
Atmos. Chem. Phys., 21, 87–128, https://doi.org/10.5194/acp-21-87-2021, https://doi.org/10.5194/acp-21-87-2021, 2021
Short summary
Short summary
Simulated aerosol optical properties as well as the aerosol life cycle are investigated for 14 global models participating in the AeroCom initiative. Considerable diversity is found in the simulated aerosol species emissions and lifetimes, also resulting in a large diversity in the simulated aerosol mass, composition, and optical properties. A comparison with observations suggests that, on average, current models underestimate the direct effect of aerosol on the atmosphere radiation budget.
Kaisa Lakkala, Jukka Kujanpää, Colette Brogniez, Nicolas Henriot, Antti Arola, Margit Aun, Frédérique Auriol, Alkiviadis F. Bais, Germar Bernhard, Veerle De Bock, Maxime Catalfamo, Christine Deroo, Henri Diémoz, Luca Egli, Jean-Baptiste Forestier, Ilias Fountoulakis, Katerina Garane, Rosa Delia Garcia, Julian Gröbner, Seppo Hassinen, Anu Heikkilä, Stuart Henderson, Gregor Hülsen, Bjørn Johnsen, Niilo Kalakoski, Angelos Karanikolas, Tomi Karppinen, Kevin Lamy, Sergio F. León-Luis, Anders V. Lindfors, Jean-Marc Metzger, Fanny Minvielle, Harel B. Muskatel, Thierry Portafaix, Alberto Redondas, Ricardo Sanchez, Anna Maria Siani, Tove Svendby, and Johanna Tamminen
Atmos. Meas. Tech., 13, 6999–7024, https://doi.org/10.5194/amt-13-6999-2020, https://doi.org/10.5194/amt-13-6999-2020, 2020
Short summary
Short summary
The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S5P) satellite was launched on 13 October 2017 to provide the atmospheric composition for atmosphere and climate research. Ground-based data from 25 sites located in Arctic, subarctic, temperate, equatorial and Antarctic
areas were used for the validation of the TROPOMI surface ultraviolet (UV) radiation product. For most sites 60 %–80 % of TROPOMI data was within ± 20 % of ground-based data.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Cited articles
Albayrak, A., Wei, J., Petrenko, M., Lynnes, C. S., and Levy, R. C.: Global bias adjustment for MODIS aerosol optical thickness using neural network, J. Appl. Remote Sens., 7, 073514, https://doi.org/10.1117/1.JRS.7.073514, 2013. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
Di Noia, A., Hasekamp, O. P., Wu, L., van Diedenhoven, B., Cairns, B., and Yorks, J. E.: Combined neural network/Phillips–Tikhonov approach to aerosol retrievals over land from the NASA Research Scanning Polarimeter, Atmos. Meas. Tech., 10, 4235–4252, https://doi.org/10.5194/amt-10-4235-2017, 2017. a
Eck, T. F., Holben, B., Reid, J., Dubovik, O., Smirnov, A., O'neill, N., Slutsker, I., and Kinne, S.: Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols, J. Geophys. Res.-Atmos., 104, 31333–31349, 1999. a
GBD 2017 Risk Factor Collaborators: Global, regional, and national comparative
risk assessment of 84 behavioural, environmental and occupational, and
metabolic risks or clusters of risks for 195 countries and territories,
1990–2017: a systematic analysis for the Global Burden of Disease Study 2017, Lancet, 392, 1923–1994, https://doi.org/10.1016/S0140-6736(18)32225-6, 2018. a
Gryspeerdt, E., Quaas, J., Ferrachat, S., Gettelman, A., Ghan, S.,
Lohmann, U., Morrison, H., Neubauer, D., Partridge, D. G., Stier, P.,
Takemura, T., Wang, H., Wang, M., and Zhang, K.: Constraining the
instantaneous aerosol influence on cloud albedo, P. Natl. Acad. Sci. USA, 114, 4899–4904, https://doi.org/10.1073/pnas.1617765114, 2017. a
Hamilton, S. J., Hänninen, A., Hauptmann, A., and Kolehmainen, V.:
Beltrami-net: domain-independent deep D-bar learning for absolute imaging with electrical impedance tomography (a-EIT), Physiol. Meas., 40, 074002, https://doi.org/10.1088/1361-6579/ab21b2, 2019. a
Holben, B. N., Eck, T., Slutsker, I., Tanre, 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, b
IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Core Writing Team, Pachauri, R. K. and Meyer, L. A.,IPCC, Geneva, Switzerland, 151 pp., 2014. a
Kaufman, Y. J., Boucher, O., Tanré, D., Chin, M., Remer, L., and Takemura, T.: Aerosol anthropogenic component estimated from satellite data, Geophys. Res. Lett., 32, L17804, https://doi.org/10.1029/2005GL023125, 2005. a
Lanzaco, B. L., Olcese, L. E., Palancar, G. G., and Toselli, B. M.: An Improved Aerosol Optical Depth Map Based on Machine-Learning and MODIS Data: Development and Application in South America, Aerosol Air Qual. Res., 17, 1523–1536, 2017. a
Levy, R., Remer, L., Tanre, D., Mattoo, S., and Kaufman, Y.: Algorithm for remote sensing of tropospheric aerosol over dark targets from MODIS: Collections 005 and 051: Revision 2; Feb 2009, MODIS Algorithm Theoretical Basis Document, 2009. a
Levy, R. C., Remer, L. A., Kleidman, R. G., Mattoo, S., Ichoku, C., Kahn, R.,
and Eck, T. F.: Global evaluation of the Collection 5 MODIS dark-target
aerosol products over land, Atmos. Chem. Phys., 10, 10399–10420,
https://doi.org/10.5194/acp-10-10399-2010, 2010. a
Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia, F., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013, 2013. a, b
Lipponen, A., Kolehmainen, V., Romakkaniemi, S., and Kokkola, H.: Correction of approximation errors with Random Forests applied to modelling of cloud droplet formation, Geosci. Model Dev., 6, 2087–2098, https://doi.org/10.5194/gmd-6-2087-2013, 2013. a
Lipponen, A., Huttunen, J. M. J., Romakkaniemi, S., Kokkola, H., and Kolehmainen, V.: Correction of model reduction errors in simulations, SIAM J. Sci. Comput., 40, B305–B327, 2018. a
Mielonen, T., Levy, R. C., Aaltonen, V., Komppula, M., de Leeuw, G., Huttunen, J., Lihavainen, H., Kolmonen, P., Lehtinen, K. E. J., and Arola, A.: Evaluating the assumptions of surface reflectance and aerosol type selection within the MODIS aerosol retrieval over land: the problem of dust type selection, Atmos. Meas. Tech., 4, 201–214, https://doi.org/10.5194/amt-4-201-2011, 2011. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a
Petrenko, M., Ichoku, C., and Leptoukh, G.: Multi-sensor Aerosol Products Sampling System (MAPSS), Atmos. Meas. Tech., 5, 913–926, https://doi.org/10.5194/amt-5-913-2012, 2012. a
Randles, C. A., da Silva, A. M., Buchard, V., Colarco, P. R., Darmenov, A.,
Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair, J., Shinozuka, Y., and Flynn, C. J.: The MERRA-2 aerosol reanalysis, 1980 onward. Part I: System description and data assimilation evaluation, J. Climate, 30, 6823–6850, https://doi.org/10.1175/JCLI-D-16-0609.1, 2017. a
Salomonson, V. V., Barnes, W., Maymon, P. W., Montgomery, H. E., and Ostrow, H.: MODIS: Advanced facility instrument for studies of the Earth as a system, IEEE T. Geosci. Remote, 27, 145–153, 1989. a
Sayer, A., Hsu, N., Bettenhausen, C., Jeong, M.-J., and Meister, G.: Effect of MODIS Terra radiometric calibration improvements on Collection 6 Deep Blue aerosol products: Validation and Terra/Aqua consistency, J. Geophys. Res.-Atmos., 120, 12–157, 2015. 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
Sogacheva, L., Popp, T., Sayer, A. M., Dubovik, O., Garay, M. J., Heckel, A., Hsu, N. C., Jethva, H., Kahn, R. A., Kolmonen, P., Kosmale, M., de Leeuw, G., Levy, R. C., Litvinov, P., Lyapustin, A., North, P., Torres, O., and Arola, A.: Merging regional and global aerosol optical depth records from major available satellite products, Atmos. Chem. Phys., 20, 2031–2056, https://doi.org/10.5194/acp-20-2031-2020, 2020. a, b, c, d
Yu, H., Chin, M., Remer, L. A., Kleidman, R. G., Bellouin, N., Bian, H., and
Diehl, T.: Variability of marine aerosol fine-mode fraction and estimates of
anthropogenic aerosol component over cloud-free oceans from the Moderate
Resolution Imaging Spectroradiometer (MODIS), J. Geophys. Res.-Atmos., 114, D10206, https://doi.org/10.1029/2008JD010648, 2009. a
Short summary
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.
We have developed a new computational method to post-process-correct the satellite aerosol...
