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
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Revised manuscript not accepted
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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
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Thomas F. Eck, Jeffrey S. Reid, Alexander Sinyuk, Pawan Gupta, Antti Arola, Ilya Slutsker, Brent N. Holben, Philippe Goloub, Oleg Dubovik, Joel S. Schafer, Mikhail G. Sorokin, Alexander Smirnov, Hongbin Yu, Dongchul Kim, Elena Lind, Jason Kraft, Elizabeth A. Reid, Aboubacry Diallo, Thierno Ndiaye, Africa Barreto, Sidi Baika, Lahouari Zeudmi-Sahraoui, Francisco Evora, Gael Dubois, Luc Blarel, Rachel T. Pinker, Isaac Abiodun Adimula, Jean Louis Rajot, Aliko Maman, Muguette Allegre, Cassandra Gaston, Peter Sealy, Edmund Blades, Rebecca Chewitt-Lucas, Ian Lau, David Barker, Adel Saad Alshihiween, Abdulrahman Alsaferan, Odbayar Mishigdorj, and Chogsom Jargalsaixan
EGUsphere, https://doi.org/10.5194/egusphere-2026-5531, https://doi.org/10.5194/egusphere-2026-5531, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Maria Filioglou, Krista Luoma, Eija Asmi, Viet Le, Klaus Haikarainen, David Brus, Tero Mielonen, Mika Komppula, Annele Virtanen, Olli Sippula, Iida Pullinen, Angela Buchholz, Pasi Miettinen, Snehitha Kommula, Saara Peltokorpi, Liqing Hao, Kajar Köster, Annika Saarto, Sanna Pätsi, Pieter G. van Zyl, Liezl Bredenkamp, and Ville Vakkari
Atmos. Chem. Phys., 26, 11525–11542, https://doi.org/10.5194/acp-26-11525-2026, https://doi.org/10.5194/acp-26-11525-2026, 2026
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William Wandji Nyamsi, Ville Leinonen, Antti Lipponen, Else van den Besselaar, Santtu Mikkonen, Arturo Sanchez-Lorenzo, Martin Wild, Doris Folini, Tero Mielonen, Harri Kokkola, Antti Kukkurainen, Neus Sabater, Rei Kudo, Ben Liley, Raghav Srinivasan, Bruce W. Forgan, Alexandru Dumitrescu, Grzegorz Urban, Michał K. Kowalewski, Márcia Akemi Yamasoe, Nilton Évora do Rosário, Dimitra Founda, Stelios Kazadzis, Veronica Manara, Derbetini A. Vondou, Christian Gueymard, Anders V. Lindfors, Atsumu Ohmura, and Antti Arola
Atmos. Chem. Phys., 26, 10767–10788, https://doi.org/10.5194/acp-26-10767-2026, https://doi.org/10.5194/acp-26-10767-2026, 2026
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EGUsphere, https://doi.org/10.5194/egusphere-2026-1806, https://doi.org/10.5194/egusphere-2026-1806, 2026
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EGUsphere, https://doi.org/10.5194/egusphere-2026-1987, https://doi.org/10.5194/egusphere-2026-1987, 2026
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EGUsphere, https://doi.org/10.5194/egusphere-2026-2743, https://doi.org/10.5194/egusphere-2026-2743, 2026
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We studied what causes errors in Copernicus Atmosphere Monitoring Service solar radiation data using explainable machine learning methods and ground-based observations. The analysis showed that cloud properties are the main source of errors, while aerosols and surface reflectance have smaller effects. The results improve understanding of the strengths and weaknesses of current models and can support future model development, forecasting and solar energy applications.
Nick Schutgens, Elisabeth J. Andrews, Antti Arola, Yusuf Bhatti, Guanliang Fu, Otto Hasekamp, Pekka Kolmonen, Antti Lipponen, Tero Mielonen, Leighton Reygare, and Andrew M. Sayer
EGUsphere, https://doi.org/10.5194/egusphere-2026-2656, https://doi.org/10.5194/egusphere-2026-2656, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Aerosol are fine particles in the air that affect public health and the climate. We develop a new method to study aerosol through patterns (called autocorrelations) in their time-series. We show that autocorrelations are linked to aerosol deposition processes and contain information on the important but uncertain aerosol lifetime. Autocorrelations can be observed from satellites and we provide the first ever global climatology of aerosol autocorrelations.
Vishnu Nair, Edward Gryspeerdt, Antti Arola, Antti Lipponen, and Timo Virtanen
Atmos. Chem. Phys., 26, 4049–4066, https://doi.org/10.5194/acp-26-4049-2026, https://doi.org/10.5194/acp-26-4049-2026, 2026
Short summary
Short summary
Surface winds affect clouds by generating sea salt aerosols and evaporating water from the ocean and transporting them to the cloud base. Previous studies analysed these processes using satellite snapshots; here we use observations of evolving clouds which capture feedbacks due to time-dependent adjustments of clouds to aerosol changes. We show that even though sea salt changes droplet sizes, evaporation from the ocean surface has a stronger impact on clouds, thus hiding the real aerosol effect.
William Wandji Nyamsi, Anders V. Lindfors, Angela Meyer, Antti Lipponen, and Antti Arola
Atmos. Meas. Tech., 19, 899–922, https://doi.org/10.5194/amt-19-899-2026, https://doi.org/10.5194/amt-19-899-2026, 2026
Short summary
Short summary
This paper proposes a new, fast and accurate method for estimating the cloud optical depth (τc) from photovoltaic (PV) power measurements under overcast sky conditions. The method performs very well with the European Centre for Medium-Range Weather Forecasts (ECMWF) products as inputs describing the state of the atmosphere. The method exhibits a better performance than published state-of-the-art methods when compared to ground and satellite-based τc retrievals serving as reference.
Antti Kukkurainen, Antti Mikkonen, Antti Arola, Antti Lipponen, Ville Kolehmainen, and Neus Sabater
Geosci. Model Dev., 18, 7529–7544, https://doi.org/10.5194/gmd-18-7529-2025, https://doi.org/10.5194/gmd-18-7529-2025, 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 (High-Resolution Transmission Molecular Absorption) database and streamlining computations. This tool helps researchers analyze how gases interact with radiation in the Earth's atmosphere and at the surface, improving atmospheric studies and satellite observations and making detailed modeling more accurate and accessible.
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.
Timo H. Virtanen, Anu-Maija Sundström, Elli Suhonen, Antti Lipponen, Antti Arola, Christopher O'Dell, Robert R. Nelson, and Hannakaisa Lindqvist
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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.
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.
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.
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
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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
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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
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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
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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.
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.
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.
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.
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
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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
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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.
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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...
