Articles | Volume 15, issue 4
https://doi.org/10.5194/amt-15-895-2022
© Author(s) 2022. 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-15-895-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Feb 2022
Research article |
| 21 Feb 2022
| 21 Feb 2022
Deep-learning-based post-process correction of the aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Jaakko Reinvall
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Arttu Väisänen
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Henri Taskinen
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Timo Lähivaara
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Larisa Sogacheva
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Pekka Kolmonen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Kari Lehtinen
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Antti Arola
Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
Ville Kolehmainen
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
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We investigate the pollen distribution at different heights using measurements from ground level up to 272 meters in eastern Finland. By combining optical pollen sensors with traditional monitoring methods and atmospheric model predictions, we examined how pollen concentrations vary with height. The results show that pollen concentrations decrease with altitude and that optical sensors combined with machine learning can identify dominant pollen types for continuous pollen monitoring.
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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.
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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.
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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.
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Worldwide reconstructed historical aerosol load reveals that during the period of: 1900–1959: the atmosphere became cleaner in most European cities; 1960–1985: anthropogenic aerosols were responsible for the dimming phenomenon in Europe. AOD increased over Southeast Brazil and decreased noticeably over Japan while a small negative trend was found over Oceania; 1986–2015: generally, the atmosphere has become much cleaner everywhere after the reversal trend around 1980s mainly observed in Europe.
Antti Kukkurainen, Antti Mikkonen, Antti Arola, Antti Lipponen, Ville Kolehmainen, and Neus Sabater
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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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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.
Maria Christina Gatou, Antti Lipponen, Ari Leskinen, Petri Tiitta, Maria Filioglou, Xiaoxia Shang, Sanna Pätsi, Annika Saarto, Marjut Roponen, Elina Giannakaki, and Mika Komppula
EGUsphere, https://doi.org/10.5194/egusphere-2026-1806, https://doi.org/10.5194/egusphere-2026-1806, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
We investigate the pollen distribution at different heights using measurements from ground level up to 272 meters in eastern Finland. By combining optical pollen sensors with traditional monitoring methods and atmospheric model predictions, we examined how pollen concentrations vary with height. The results show that pollen concentrations decrease with altitude and that optical sensors combined with machine learning can identify dominant pollen types for continuous pollen monitoring.
Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, Robert Levy, Yingxi Rona Shi, Hai Zhang, and Antti Lipponen
EGUsphere, https://doi.org/10.5194/egusphere-2026-1987, https://doi.org/10.5194/egusphere-2026-1987, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
We developed a new way to improve satellite measurements of atmospheric aerosols, which affect air quality and climate. By combining data from two geostationary satellites and using advanced machine learning methods that capture changes over time, we achieved more accurate and detailed results. This approach reduces errors in current satellite-based aerosol data and can better support monitoring and decision making related to air pollution and climate impacts.
Antti Lipponen, Jorge Lezaca, Yves-Marie Saint-Drenan, Marion Schroedter-Homscheidt, and Antti Arola
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
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Short summary
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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.
Matti Niskanen, Aku Ursin, Arttu Ylisirniö, and Kari E. J. Lehtinen
EGUsphere, https://doi.org/10.5194/egusphere-2026-2013, https://doi.org/10.5194/egusphere-2026-2013, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
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Measuring tiny airborne particles by their movement in an electric field is common in atmospheric science, but the results almost never include uncertainty estimates. The largest source of uncertainty in these measurements relates to particle charging. We developed a Bayesian method to systematically quantify this uncertainty. Applied to a month of atmospheric data, accounting for charging uncertainty widened the plausible range of particle concentrations by a factor of two to ten.
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
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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
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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.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-5950, https://doi.org/10.5194/egusphere-2025-5950, 2026
Short summary
Short summary
Worldwide reconstructed historical aerosol load reveals that during the period of: 1900–1959: the atmosphere became cleaner in most European cities; 1960–1985: anthropogenic aerosols were responsible for the dimming phenomenon in Europe. AOD increased over Southeast Brazil and decreased noticeably over Japan while a small negative trend was found over Oceania; 1986–2015: generally, the atmosphere has become much cleaner everywhere after the reversal trend around 1980s mainly observed in Europe.
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
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, 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.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
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Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
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.
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
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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.
Arto Heitto, Cheng Wu, Diego Aliaga, Luis Blacutt, Xuemeng Chen, Yvette Gramlich, Liine Heikkinen, Wei Huang, Radovan Krejci, Paolo Laj, Isabel Moreno, Karine Sellegri, Fernando Velarde, Kay Weinhold, Alfred Wiedensohler, Qiaozhi Zha, Federico Bianchi, Marcos Andrade, Kari E. J. Lehtinen, Claudia Mohr, and Taina Yli-Juuti
Atmos. Chem. Phys., 24, 1315–1328, https://doi.org/10.5194/acp-24-1315-2024, https://doi.org/10.5194/acp-24-1315-2024, 2024
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Particle growth at the Chacaltaya station in Bolivia was simulated based on measured vapor concentrations and ambient conditions. Major contributors to the simulated growth were low-volatility organic compounds (LVOCs). Also, sulfuric acid had major role when volcanic activity was occurring in the area. This study provides insight on nanoparticle growth at this high-altitude Southern Hemispheric site and hence contributes to building knowledge of early growth of atmospheric particles.
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.
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.
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.
Arto Heitto, Kari Lehtinen, Tuukka Petäjä, Felipe Lopez-Hilfiker, Joel A. Thornton, Markku Kulmala, and Taina Yli-Juuti
Atmos. Chem. Phys., 22, 155–171, https://doi.org/10.5194/acp-22-155-2022, https://doi.org/10.5194/acp-22-155-2022, 2022
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For atmospheric aerosol particles to take part in cloud formation, they need to be at least a few tens of nanometers in diameter. By using a particle condensation model, we investigated how two types of chemical reactions, oligomerization and decomposition, of organic molecules inside the particle may affect the growth of secondary aerosol particles to these sizes. We show that the effect is potentially significant, which highlights the importance of increasing understanding of these processes.
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.
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
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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.
Matthew Ozon, Dominik Stolzenburg, Lubna Dada, Aku Seppänen, and Kari E. J. Lehtinen
Atmos. Chem. Phys., 21, 12595–12611, https://doi.org/10.5194/acp-21-12595-2021, https://doi.org/10.5194/acp-21-12595-2021, 2021
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Measuring the rate at which aerosol particles are formed is of importance for understanding climate change. We present an analysis method based on Kalman smoothing, which retrieves new particle formation and growth rates from size-distribution measurements. We apply it to atmospheric simulation chamber experiments and show that it agrees well with traditional methods. In addition, it provides reliable uncertainty estimates, and we suggest instrument design optimisation for signal processing.
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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.
We have developed a machine-learning-based model that can be used to correct the Sentinel-3...
