Articles | Volume 7, issue 4
https://doi.org/10.5194/amt-7-877-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-7-877-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
04 Apr 2014
Research article |
| 04 Apr 2014
Inversion of droplet aerosol analyzer data for long-term aerosol–cloud interaction measurements
M. I. A. Berghof
Department of Physics, Lund University, Lund, Sweden
G. P. Frank
Department of Physics, Lund University, Lund, Sweden
S. Sjogren
Department of Physics, Lund University, Lund, Sweden
now at: University of Applied Sciences Northwestern Switzerland, Brugg-Windisch, Switzerland
B. G. Martinsson
Department of Physics, Lund University, Lund, Sweden
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S. Sjogren, G. P. Frank, M. I. A. Berghof, and B. G. Martinsson
Atmos. Meas. Tech., 6, 349–357, https://doi.org/10.5194/amt-6-349-2013, https://doi.org/10.5194/amt-6-349-2013, 2013
Bengt G. Martinsson, Johan Friberg, and Moa K. Sporre
EGUsphere, https://doi.org/10.5194/egusphere-2025-971, https://doi.org/10.5194/egusphere-2025-971, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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The highly variable stratospheric aerosol bears great importance for Earth's climate. The one-year average aerosol load from the 2022 volcanic eruption in Hunga Tonga is the highest since the 1991 Mt. Pinatubo eruption. The usual volcanic aerosol precursor gas (SO2) mass was not sufficient to explain the aerosol load. Volcaniclastic density current in runouts exceeding 100 km amplified the eruption and bubble-bursting in the boiling sea forms a plausible explanation for high aerosol loading.
Johan Friberg, Bengt G. Martinsson, and Moa K. Sporre
Atmos. Chem. Phys., 23, 12557–12570, https://doi.org/10.5194/acp-23-12557-2023, https://doi.org/10.5194/acp-23-12557-2023, 2023
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We study the short- and long-term stratospheric impact of smoke from the massive Australian wildfires in Dec 2019–Jan 2020 using four satellite sensors. Smoke entered the stratosphere rapidly via transport by firestorms, as well as weeks after the fires. The smoke particle properties evolved over time together with rapidly decreasing stratospheric aerosol load, suggesting photolytic loss of organics in the smoke particles. The depletion rate was estimated to a half-life (e folding) of 10 (14) d.
Bengt G. Martinsson, Johan Friberg, Oscar S. Sandvik, and Moa K. Sporre
Atmos. Chem. Phys., 22, 3967–3984, https://doi.org/10.5194/acp-22-3967-2022, https://doi.org/10.5194/acp-22-3967-2022, 2022
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Large amounts of wildfire smoke reached the stratosphere in 2017. The literature on stratospheric aerosol is mainly based on horizontally viewing sensors that saturate in dense smoke. Using also a vertically viewing sensor with orders of magnitude shorter path in the smoke, we show that the horizontally viewing sensors miss a dramatic exponential decline of the aerosol load with a half-life of 10 d, where 80 %–90 % of smoke is lost. We attribute the decline to photolytic loss of organic aerosol.
Oscar S. Sandvik, Johan Friberg, Moa K. Sporre, and Bengt G. Martinsson
Atmos. Meas. Tech., 14, 7153–7165, https://doi.org/10.5194/amt-14-7153-2021, https://doi.org/10.5194/amt-14-7153-2021, 2021
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A method to form SO2 profiles in the stratosphere with high vertical resolution following volcanic eruptions is introduced. The method combines space-based high-resolution vertical aerosol profiles and SO2 measurements the first 2 weeks after an eruption with air mass trajectory analyses. The SO2 is located at higher altitude than in most previous studies. The detailed resolution of the SO2 profile is unprecedented compared to other methods.
Franz Slemr, Andreas Weigelt, Ralf Ebinghaus, Johannes Bieser, Carl A. M. Brenninkmeijer, Armin Rauthe-Schöch, Markus Hermann, Bengt G. Martinsson, Peter van Velthoven, Harald Bönisch, Marco Neumaier, Andreas Zahn, and Helmut Ziereis
Atmos. Chem. Phys., 18, 12329–12343, https://doi.org/10.5194/acp-18-12329-2018, https://doi.org/10.5194/acp-18-12329-2018, 2018
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Total and elemental mercury were measured in the upper troposphere and lower stratosphere onboard a passenger aircraft. Their concentrations in the upper troposphere were comparable implying low concentrations of oxidized mercury in this region. Large scale seasonally dependent influence of emissions from biomass burning was also observed. Their distributions in the lower stratosphere implies a long stratospheric lifetime, which precludes significant mercury oxidation by ozone.
Johan Friberg, Bengt G. Martinsson, Sandra M. Andersson, and Oscar S. Sandvik
Atmos. Chem. Phys., 18, 11149–11169, https://doi.org/10.5194/acp-18-11149-2018, https://doi.org/10.5194/acp-18-11149-2018, 2018
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During 2006–2015 volcanism contributed 40 % of the stratospheric aerosol load. We compute the AOD (aerosol optical depth) of the stratosphere (from the tropopause to 35 km altitude) using new techniques of handling CALIOP data. Regional and global AODs are presented for the entire stratosphere in relation to transport patterns, and the AOD is presented for three stratospheric layers: the LMS, the potential temperature range of 380 to 470 K, and altitudes above the 470 K isentrope.
Julia Schmale, Silvia Henning, Stefano Decesari, Bas Henzing, Helmi Keskinen, Karine Sellegri, Jurgita Ovadnevaite, Mira L. Pöhlker, Joel Brito, Aikaterini Bougiatioti, Adam Kristensson, Nikos Kalivitis, Iasonas Stavroulas, Samara Carbone, Anne Jefferson, Minsu Park, Patrick Schlag, Yoko Iwamoto, Pasi Aalto, Mikko Äijälä, Nicolas Bukowiecki, Mikael Ehn, Göran Frank, Roman Fröhlich, Arnoud Frumau, Erik Herrmann, Hartmut Herrmann, Rupert Holzinger, Gerard Kos, Markku Kulmala, Nikolaos Mihalopoulos, Athanasios Nenes, Colin O'Dowd, Tuukka Petäjä, David Picard, Christopher Pöhlker, Ulrich Pöschl, Laurent Poulain, André Stephan Henry Prévôt, Erik Swietlicki, Meinrat O. Andreae, Paulo Artaxo, Alfred Wiedensohler, John Ogren, Atsushi Matsuki, Seong Soo Yum, Frank Stratmann, Urs Baltensperger, and Martin Gysel
Atmos. Chem. Phys., 18, 2853–2881, https://doi.org/10.5194/acp-18-2853-2018, https://doi.org/10.5194/acp-18-2853-2018, 2018
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Collocated long-term observations of cloud condensation nuclei (CCN) number concentrations, particle number size distributions and chemical composition from 12 sites are synthesized. Observations cover coastal environments, the Arctic, the Mediterranean, the boreal and rain forest, high alpine and continental background sites, and Monsoon-influenced areas. We interpret regional and seasonal variability. CCN concentrations are predicted with the κ–Köhler model and compared to the measurements.
Bengt G. Martinsson, Johan Friberg, Oscar S. Sandvik, Markus Hermann, Peter F. J. van Velthoven, and Andreas Zahn
Atmos. Chem. Phys., 17, 10937–10953, https://doi.org/10.5194/acp-17-10937-2017, https://doi.org/10.5194/acp-17-10937-2017, 2017
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We find that the aerosol of the lowermost stratosphere has a considerable climate forcing. The upper tropospheric (UT) particulate sulfur is strongly influenced by stratospheric sources the first half of the year, whereas tropospheric sources dominate in fall; 50 % of the UT particulate sulfur (S) was found to be stratospheric at background condition, and 70 % under moderate influence from volcanism. The Asian monsoon is found to be an important tropospheric source of S in the NH extratropical UT.
M. Paramonov, V.-M. Kerminen, M. Gysel, P. P. Aalto, M. O. Andreae, E. Asmi, U. Baltensperger, A. Bougiatioti, D. Brus, G. P. Frank, N. Good, S. S. Gunthe, L. Hao, M. Irwin, A. Jaatinen, Z. Jurányi, S. M. King, A. Kortelainen, A. Kristensson, H. Lihavainen, M. Kulmala, U. Lohmann, S. T. Martin, G. McFiggans, N. Mihalopoulos, A. Nenes, C. D. O'Dowd, J. Ovadnevaite, T. Petäjä, U. Pöschl, G. C. Roberts, D. Rose, B. Svenningsson, E. Swietlicki, E. Weingartner, J. Whitehead, A. Wiedensohler, C. Wittbom, and B. Sierau
Atmos. Chem. Phys., 15, 12211–12229, https://doi.org/10.5194/acp-15-12211-2015, https://doi.org/10.5194/acp-15-12211-2015, 2015
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The research paper presents the first comprehensive overview of field measurements with the CCN Counter performed at a large number of locations around the world within the EUCAARI framework. The paper sheds light on the CCN number concentrations and activated fractions around the world and their dependence on the water vapour supersaturation ratio, the dependence of aerosol hygroscopicity on particle size, and seasonal and diurnal variation of CCN activation and hygroscopic properties.
M. Tjernström, C. Leck, C. E. Birch, J. W. Bottenheim, B. J. Brooks, I. M. Brooks, L. Bäcklin, R. Y.-W. Chang, G. de Leeuw, L. Di Liberto, S. de la Rosa, E. Granath, M. Graus, A. Hansel, J. Heintzenberg, A. Held, A. Hind, P. Johnston, J. Knulst, M. Martin, P. A. Matrai, T. Mauritsen, M. Müller, S. J. Norris, M. V. Orellana, D. A. Orsini, J. Paatero, P. O. G. Persson, Q. Gao, C. Rauschenberg, Z. Ristovski, J. Sedlar, M. D. Shupe, B. Sierau, A. Sirevaag, S. Sjogren, O. Stetzer, E. Swietlicki, M. Szczodrak, P. Vaattovaara, N. Wahlberg, M. Westberg, and C. R. Wheeler
Atmos. Chem. Phys., 14, 2823–2869, https://doi.org/10.5194/acp-14-2823-2014, https://doi.org/10.5194/acp-14-2823-2014, 2014
P. Kupiszewski, C. Leck, M. Tjernström, S. Sjogren, J. Sedlar, M. Graus, M. Müller, B. Brooks, E. Swietlicki, S. Norris, and A. Hansel
Atmos. Chem. Phys., 13, 12405–12431, https://doi.org/10.5194/acp-13-12405-2013, https://doi.org/10.5194/acp-13-12405-2013, 2013
M. D. Shupe, P. O. G. Persson, I. M. Brooks, M. Tjernström, J. Sedlar, T. Mauritsen, S. Sjogren, and C. Leck
Atmos. Chem. Phys., 13, 9379–9399, https://doi.org/10.5194/acp-13-9379-2013, https://doi.org/10.5194/acp-13-9379-2013, 2013
S. Sjogren, G. P. Frank, M. I. A. Berghof, and B. G. Martinsson
Atmos. Meas. Tech., 6, 349–357, https://doi.org/10.5194/amt-6-349-2013, https://doi.org/10.5194/amt-6-349-2013, 2013
Related subject area
Subject: Clouds | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme
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Ice crystal images from optical array probes. Compatibility of morphology specific size distributions, retrieved with specific and global Convolutional Neural Networks for HVPS, PIP, CIP, and 2DS
In situ observations of supercooled liquid water clouds over Dome C, Antarctica, by balloon-borne sondes
Partition between supercooled liquid droplets and ice crystals in mixed-phase clouds based on airborne in situ observations
Innovative cloud quantification: deep learning classification and finite-sector clustering for ground-based all-sky imaging
Revealing halos concealed by cirrus clouds
An Analysis of Cloud Microphysical Features over UAE Using Multiple Data Sources
Quantifying riming from airborne data during the HALO-(AC)3 campaign
Estimation of 24 h continuous cloud cover using a ground-based imager with a convolutional neural network
Neural network processing of holographic images
Ice crystal images from optical array probes: classification with convolutional neural networks
Detection and analysis of cloud boundary in Xi'an, China, employing 35 GHz cloud radar aided by 1064 nm lidar
The University of Washington Ice–Liquid Discriminator (UWILD) improves single-particle phase classifications of hydrometeors within Southern Ocean clouds using machine learning
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Block-based cloud classification with statistical features and distribution of local texture features
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Huiying Zhang, Xia Li, Fabiola Ramelli, Robert O. David, Julie Pasquier, and Jan Henneberger
Atmos. Meas. Tech., 17, 7109–7128, https://doi.org/10.5194/amt-17-7109-2024, https://doi.org/10.5194/amt-17-7109-2024, 2024
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Our innovative IceDetectNet algorithm classifies each part of aggregated ice crystals, considering both their basic shape and physical processes. Trained on ice crystal images from the Arctic taken by a holographic camera, it correctly classifies over 92 % of the ice crystals. These more detailed insights into the components of aggregated ice crystals have the potential to improve our estimates of microphysical properties such as riming rate, aggregation rate, and ice water content.
Fuzeng Wang, Yuanyu Duan, Yao Huo, Yaxi Cao, Qiusong Wang, Tong Zhang, Junqing Liu, and Guangmin Cao
Atmos. Meas. Tech., 17, 6933–6944, https://doi.org/10.5194/amt-17-6933-2024, https://doi.org/10.5194/amt-17-6933-2024, 2024
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Louis Jaffeux, Jan Breiner, Pierre Coutris, and Alfons Schwarzenböck
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Airborne cloud observation relies on high frequency black and white image information. The study presents automatic shape recognition tools developed with machine learning techniques and adapted for this type of images. Applied on a recent field campaign, these tools are compared across four instruments that cover different size ranges. The analysis show that the tools are performing well and are consistent across the different instruments.
Philippe Ricaud, Pierre Durand, Paolo Grigioni, Massimo Del Guasta, Giuseppe Camporeale, Axel Roy, Jean-Luc Attié, and John Bognar
Atmos. Meas. Tech., 17, 5071–5089, https://doi.org/10.5194/amt-17-5071-2024, https://doi.org/10.5194/amt-17-5071-2024, 2024
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Clouds in Antarctica are key elements affecting climate evolution. Some clouds are composed of supercooled liquid water (SLW; water held in liquid form below 0 °C) and are difficult to forecast by models. We performed in situ observations of SLW clouds at Concordia Station using SLW sondes attached to meteorological balloons in summer 2021–2022. The SLW clouds were observed in a saturated layer at the top of the planetary boundary layer in agreement with ground-based lidar observations.
Flor Vanessa Maciel, Minghui Diao, and Ching An Yang
Atmos. Meas. Tech., 17, 4843–4861, https://doi.org/10.5194/amt-17-4843-2024, https://doi.org/10.5194/amt-17-4843-2024, 2024
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The partition between supercooled liquid water and ice crystals in mixed-phase clouds is investigated using aircraft-based in situ observations over the Southern Ocean. A novel method is developed to define four phases of mixed-phase clouds. Relationships between cloud macrophysical and microphysical properties are quantified. Effects of aerosols and thermodynamic and dynamical conditions on ice nucleation and phase partitioning are examined.
Jingxuan Luo, Yubing Pan, Debin Su, Jinhua Zhong, Lingxiao Wu, Wei Zhao, Xiaoru Hu, Zhengchao Qi, Daren Lu, and Yinan Wang
Atmos. Meas. Tech., 17, 3765–3781, https://doi.org/10.5194/amt-17-3765-2024, https://doi.org/10.5194/amt-17-3765-2024, 2024
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Accurate cloud quantification is critical for climate research. We developed a novel computer vision framework using deep neural networks and clustering algorithms for cloud classification and segmentation from ground-based all-sky images. After a full year of observational training, our model achieves over 95 % accuracy on four cloud types. The framework enhances quantitative analysis to support climate research by providing reliable cloud data.
Yuji Ayatsuka
Atmos. Meas. Tech., 17, 3739–3750, https://doi.org/10.5194/amt-17-3739-2024, https://doi.org/10.5194/amt-17-3739-2024, 2024
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Many types of halos appear in the sky. Each type of halo reflects the state of the atmosphere; therefore observing them from the ground greatly helps in understanding the state of the atmosphere. However, halos are easily obscured by the contrast of the cloud itself, making it difficult to observe them. This study describes the construction of a sky-color model for halos and a new effective algorithm to reveal halos in images.
Zhenhai Zhang, Vesta Afzali Gorooh, Duncan Axisa, Chandrasekar Radhakrishnan, Eun Yeol Kim, Venkatachalam Chandrasekar, and Luca Delle Monache
EGUsphere, https://doi.org/10.5194/egusphere-2024-1400, https://doi.org/10.5194/egusphere-2024-1400, 2024
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Water is a precious resource, and it is essential to monitor and predict the current and future occurrence of precipitation-producing clouds. We investigate the cloud characteristics related to precipitation with several cloud cases in the United Arab Emirates using the data from aircraft measurements, satellite observations, and weather radar observations. This study provides scientific support to the development of an applicable framework to examine cloud precipitation processes.
Nina Maherndl, Manuel Moser, Johannes Lucke, Mario Mech, Nils Risse, Imke Schirmacher, and Maximilian Maahn
Atmos. Meas. Tech., 17, 1475–1495, https://doi.org/10.5194/amt-17-1475-2024, https://doi.org/10.5194/amt-17-1475-2024, 2024
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In some clouds, liquid water droplets can freeze onto ice crystals (riming). Riming leads to the formation of snowflakes. We show two ways to quantify riming using aircraft data collected in the Arctic. One aircraft had a cloud radar, while the other aircraft was measuring directly in cloud. The first method compares radar and direct observations. The second looks at snowflake shape. Both methods agree, except when there were gaps in the cloud. This improves our ability to understand riming.
Bu-Yo Kim, Joo Wan Cha, and Yong Hee Lee
Atmos. Meas. Tech., 16, 5403–5413, https://doi.org/10.5194/amt-16-5403-2023, https://doi.org/10.5194/amt-16-5403-2023, 2023
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A camera-based imager and convolutional neural network (CNN) were used to estimate ground cloud cover. Image data from 2019 were used for training and validation, and those from 2020 were used for testing. The CNN model exhibited high performance, with an accuracy of 0.92, RMSE of 1.40 tenths, and 93% agreement with observed cloud cover within ±2 tenths' difference. It also outperformed satellites and ceilometers and proved to be the most suitable for ground-based cloud cover estimation.
John S. Schreck, Gabrielle Gantos, Matthew Hayman, Aaron Bansemer, and David John Gagne
Atmos. Meas. Tech., 15, 5793–5819, https://doi.org/10.5194/amt-15-5793-2022, https://doi.org/10.5194/amt-15-5793-2022, 2022
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We show promising results for a new machine-learning based paradigm for processing field-acquired cloud droplet holograms. The approach is fast, scalable, and leverages GPUs and other heterogeneous computing platforms. It combines applications of transfer and active learning by using synthetic data for training and a small set of hand-labeled data for refinement and validation. Artificial noise applied during synthetic training enables optimized models for real-world situations.
Louis Jaffeux, Alfons Schwarzenböck, Pierre Coutris, and Christophe Duroure
Atmos. Meas. Tech., 15, 5141–5157, https://doi.org/10.5194/amt-15-5141-2022, https://doi.org/10.5194/amt-15-5141-2022, 2022
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Optical array probes are instruments used aboard research aircraft to capture 2D images of ice or water particles in clouds. This study presents a new tool using innovative machine learning, called convolutional neural networks, designed to identify the shape of imaged ice particles for two of these imagers, namely 2DS and PIP. Such a tool will be a very strong asset for understanding cloud microphysics. Beyond traditional evaluation metrics, human inspections were performed of unknown data.
Yun Yuan, Huige Di, Yuanyuan Liu, Tao Yang, Qimeng Li, Qing Yan, Wenhui Xin, Shichun Li, and Dengxin Hua
Atmos. Meas. Tech., 15, 4989–5006, https://doi.org/10.5194/amt-15-4989-2022, https://doi.org/10.5194/amt-15-4989-2022, 2022
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We put forward a new algorithm for joint observation of the cloud boundary by lidar and Ka-band millimetre-wave cloud radar. Cloud cover and boundary distribution characteristics are analysed from December 2020 to November 2021 in Xi'an. More than 34 % of clouds appear as a single layer every month. The maximum and minimum normalized cloud cover occurs in summer and winter, respectively. The study can provide more information on aerosol–cloud interactions and forecasting numerical models.
Rachel Atlas, Johannes Mohrmann, Joseph Finlon, Jeremy Lu, Ian Hsiao, Robert Wood, and Minghui Diao
Atmos. Meas. Tech., 14, 7079–7101, https://doi.org/10.5194/amt-14-7079-2021, https://doi.org/10.5194/amt-14-7079-2021, 2021
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Many clouds with temperatures between 0 °C and −40 °C contain both liquid and ice particles, and the ratio of liquid to ice particles influences how the clouds interact with radiation and moderate Earth's climate. We use a machine learning method called random forest to classify images of individual cloud particles as either liquid or ice. We apply our algorithm to images captured by aircraft within clouds overlying the Southern Ocean, and we find that it outperforms two existing algorithms.
Bu-Yo Kim, Joo Wan Cha, and Ki-Ho Chang
Atmos. Meas. Tech., 14, 6695–6710, https://doi.org/10.5194/amt-14-6695-2021, https://doi.org/10.5194/amt-14-6695-2021, 2021
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This study investigates a method for 24 h cloud cover calculation using a camera-based imager and supervised machine learning methods. The cloud cover is calculated by learning the statistical characteristics of the ratio, difference, and luminance using RGB channels of the image with a machine learning model. The proposed approach is suitable for nowcasting because it has higher learning and prediction speed than the method in which the many pixels of a 2D image are learned.
Jun Inoue, Yutaka Tobo, Kazutoshi Sato, Fumikazu Taketani, and Marion Maturilli
Atmos. Meas. Tech., 14, 4971–4987, https://doi.org/10.5194/amt-14-4971-2021, https://doi.org/10.5194/amt-14-4971-2021, 2021
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A cloud particle sensor (CPS) sonde is an observing system to obtain the signals of the phase, size, and the number of cloud particles. Based on the field experiments in the Arctic regions and numerical experiments, we proposed a method to correct the CPS sonde data and found that the CPS sonde system can appropriately observe the liquid cloud if our correction method is applied.
Ilona Ylivinkka, Santeri Kaupinmäki, Meri Virman, Maija Peltola, Ditte Taipale, Tuukka Petäjä, Veli-Matti Kerminen, Markku Kulmala, and Ekaterina Ezhova
Atmos. Meas. Tech., 13, 5595–5619, https://doi.org/10.5194/amt-13-5595-2020, https://doi.org/10.5194/amt-13-5595-2020, 2020
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In this study, we developed a new algorithm for cloud classification using solar radiation and cloud base height measurements. Our objective was to develop a simple and inexpensive but effective algorithm for the needs of studies related to ecosystem and atmosphere interactions. In the present study, we used the algorithm for obtaining cloud statistics at a measurement station in southern Finland, and we discuss the advantages and shortcomings of the algorithm.
Georgios Touloupas, Annika Lauber, Jan Henneberger, Alexander Beck, and Aurélien Lucchi
Atmos. Meas. Tech., 13, 2219–2239, https://doi.org/10.5194/amt-13-2219-2020, https://doi.org/10.5194/amt-13-2219-2020, 2020
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Images of cloud particles give important information for improving our understanding of microphysical cloud processes. For phase-resolved measurements, a large number of water droplets and ice crystals need to be classified by an automated approach. In this study, a convolutional neural network was designed, which exceeds the classification ability of traditional methods and therefore shortens the analysis procedure of cloud particle images.
Carola Barrientos Velasco, Hartwig Deneke, Hannes Griesche, Patric Seifert, Ronny Engelmann, and Andreas Macke
Atmos. Meas. Tech., 13, 1757–1775, https://doi.org/10.5194/amt-13-1757-2020, https://doi.org/10.5194/amt-13-1757-2020, 2020
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In the changing Arctic, quantifying the resulting variability of incoming solar radiation is important to better elucidate the net radiative effect of clouds. As part of a multidisciplinary expedition in the central Arctic held in early summer 2017, a novel network of pyranometers was deployed over an ice floe to investigate the spatiotemporal variability of solar radiation under different sky conditions. This study presents the collected data and an analysis of the spatiotemporal variability.
Sylke Boyd, Stephen Sorenson, Shelby Richard, Michelle King, and Morton Greenslit
Atmos. Meas. Tech., 12, 4241–4259, https://doi.org/10.5194/amt-12-4241-2019, https://doi.org/10.5194/amt-12-4241-2019, 2019
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How cirroform clouds affect the radiation balance of the atmosphere depends on their properties, including ice particle types such as crystals, pellets, and fragments. Ice halos form if ice particles in these clouds are in a smooth hexagonal crystalline form. This paper introduces a method to search long-term records of sky images for ice halos, as gathered by total sky imagers (TSIs). Such an analysis will allow one to explore geographical and seasonal variations in cirrus cloud particle types.
Thibault Vaillant de Guélis, Alfons Schwarzenböck, Valery Shcherbakov, Christophe Gourbeyre, Bastien Laurent, Régis Dupuy, Pierre Coutris, and Christophe Duroure
Atmos. Meas. Tech., 12, 2513–2529, https://doi.org/10.5194/amt-12-2513-2019, https://doi.org/10.5194/amt-12-2513-2019, 2019
Michael L. Larsen and Raymond A. Shaw
Atmos. Meas. Tech., 11, 4261–4272, https://doi.org/10.5194/amt-11-4261-2018, https://doi.org/10.5194/amt-11-4261-2018, 2018
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A statistical tool frequently utilized to measure scale-dependent departures from perfect randomness is the radial distribution function. This tool has many strengths, but it is not easy to calculate for particle detections within a three-dimensional sample volume. In this manuscript, we introduce and test a new method to estimate the three-dimensional radial distribution function in realistic measurement volumes.
Jiangchuan Tao, Chunsheng Zhao, Ye Kuang, Gang Zhao, Chuanyang Shen, Yingli Yu, Yuxuan Bian, and Wanyun Xu
Atmos. Meas. Tech., 11, 895–906, https://doi.org/10.5194/amt-11-895-2018, https://doi.org/10.5194/amt-11-895-2018, 2018
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Existing chamber technologies for direct measurements of number concentration of cloud condensation nuclei (NCCN) are sophisticated and expensive. In this paper, a new method is proposed to calculate NCCN based only on measurements of a humidified nephelometer system which have accounted for influences of both aerosol size and aerosol hygroscopicity on NCCN calculation. This new method makes NCCN measurements more convenient and is capable of obtaining NCCN at lower supersaturations.
Christine Aebi, Julian Gröbner, Niklaus Kämpfer, and Laurent Vuilleumier
Atmos. Meas. Tech., 10, 4587–4600, https://doi.org/10.5194/amt-10-4587-2017, https://doi.org/10.5194/amt-10-4587-2017, 2017
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The current study analyses the cloud radiative effect during the daytime depending on cloud fraction and cloud type at two stations in Switzerland over a time period of 3–5 years. Information about fractional cloud coverage and cloud type is retrieved from images taken by visible all-sky cameras. Cloud cover, cloud type and other atmospheric parameters have an influence on the magnitude of the longwave cloud effect as well as on the shortwave.
Emmanuel Fontaine, Delphine Leroy, Alfons Schwarzenboeck, Julien Delanoë, Alain Protat, Fabien Dezitter, Alice Grandin, John Walter Strapp, and Lyle Edward Lilie
Atmos. Meas. Tech., 10, 2239–2252, https://doi.org/10.5194/amt-10-2239-2017, https://doi.org/10.5194/amt-10-2239-2017, 2017
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In this study we evaluate a method to estimate cloud water content (CWC) knowing cloud reflectivity. Ice hydrometeors are replace by ice oblate spheroids to simulate their reflectivity. There is no assumption on the relation between mass and their size. Then, a broad range of CWCs are compared with direct measurements of CWC. The accuracy of the method is ~ ±32 %. This study is performed in areas of convective clouds where reflectivity and CWC are especially high, what makes it unique.
Hsu-Yung Cheng and Chih-Lung Lin
Atmos. Meas. Tech., 10, 199–208, https://doi.org/10.5194/amt-10-199-2017, https://doi.org/10.5194/amt-10-199-2017, 2017
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A cloud detection method for all-sky images is proposed. Obtaining improved cloud detection results is helpful for cloud classification, tracking and solar irradiance prediction. The features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We have shown that taking advantages of multiple classifiers and various patch sizes is able to increase the detection accuracy.
Qingyong Li, Zhen Zhang, Weitao Lu, Jun Yang, Ying Ma, and Wen Yao
Atmos. Meas. Tech., 9, 753–764, https://doi.org/10.5194/amt-9-753-2016, https://doi.org/10.5194/amt-9-753-2016, 2016
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This paper proposes a new cloud classification method, named bag of micro-structures (BoMS), for whole-sky imagers. BoMS treats an all-sky image as a collection of micro-structures mapped from image patches, rather than a collection of pixels. BoMS identifies five different sky conditions: cirriform, cumuliform, stratiform, clear sky, and mixed cloudiness (often appearing in all-sky images but seldom addressed in the literature). The performance of BoMS overperforms those of traditional methods.
Som Sharma, Rajesh Vaishnav, Munn V. Shukla, Prashant Kumar, Prateek Kumar, Pradeep K. Thapliyal, Shyam Lal, and Yashwant B. Acharya
Atmos. Meas. Tech., 9, 711–719, https://doi.org/10.5194/amt-9-711-2016, https://doi.org/10.5194/amt-9-711-2016, 2016
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Cloud base height observations from Ceilometer CL31 were extensively studied during May 2013 to January 2015 over Ahmedabad (23.03°N, 72.54°E), India. Results indicate that the ceilometer is an excellent instrument to precisely detect low- and mid-level clouds, and that the MODIS satellite provides accurate retrieval of high-level clouds over this region.
S. Gagné, L. P. MacDonald, W. R. Leaitch, and J. R. Pierce
Atmos. Meas. Tech., 9, 619–630, https://doi.org/10.5194/amt-9-619-2016, https://doi.org/10.5194/amt-9-619-2016, 2016
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Measurements of clouds with an aircraft are essential to understand how clouds form and how they affect the Earth's climate. These measurements are used in climate models to help predict how our climate might develop in the next century. Aircraft measurements are, however, difficult for modellers to interpret because the way they were acquired and analyzed varies from one team of scientists to the next. We present a software platform for scientists to share and compare their analysis tools.
H.-Y. Cheng and C.-C. Yu
Atmos. Meas. Tech., 8, 1173–1182, https://doi.org/10.5194/amt-8-1173-2015, https://doi.org/10.5194/amt-8-1173-2015, 2015
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This work performs cloud classification on all-sky images. To deal with mixed cloud types, we propose performing block-based classification. The proposed method combines local texture features with classical statistical texture features. The experimental results have shown that applying the combined feature results in higher classification accuracy. It is also validated that using block-based classification outperforms classification on the entire images.
A. Korolev and P. R. Field
Atmos. Meas. Tech., 8, 761–777, https://doi.org/10.5194/amt-8-761-2015, https://doi.org/10.5194/amt-8-761-2015, 2015
M. L. Krüger, S. Mertes, T. Klimach, Y. F. Cheng, H. Su, J. Schneider, M. O. Andreae, U. Pöschl, and D. Rose
Atmos. Meas. Tech., 7, 2615–2629, https://doi.org/10.5194/amt-7-2615-2014, https://doi.org/10.5194/amt-7-2615-2014, 2014
A. Schwarzenboeck, G. Mioche, A. Armetta, A. Herber, and J.-F. Gayet
Atmos. Meas. Tech., 2, 779–788, https://doi.org/10.5194/amt-2-779-2009, https://doi.org/10.5194/amt-2-779-2009, 2009
Cited articles
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, 1989.
Anderson, T. L., Charlson, R. J., and Covert, D. S.: Calibration of a Counterflow Virtual Impactor at Aerodynamic Diameters from 1 to 15 μm, Aerosol Sci. Technol., 19, 317–329, https://doi.org/10.1080/02786829308959639, 1993.
Belyaev, S. and Levin, L.: Techniques for collection of representative aerosol samples, J. Aerosol Sci., 5, 325–338, 1974.
Cederfelt, S. I., Martinsson, B. G., Svenningsson, B., Weidensohler, A., Frank, G., Hansson, H. C., Swietlicki, E., Wendish, M., Beswick, K. M., Bower, K. N., Gallagher, M. W., Pahl, S., Maser, R., and Schell, D.: Field validation of the droplet aerosol analyser, Atmos. Environ., 31, 2657–2670, 1997.
Draxler, R. and Rolph, G.: HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) Model access via NOAA ARL READY Website http://ready.arl.noaa.gov/HYSPLIT.php, NOAA Air Resources Laboratory, Silver Spring, MD, 2013.
European Environment Agency: Air quality in Europe, 2012 report, Tech. rep., European Environment Agency, 2012.
Frank, G., Martinsson, B. G., Cederfelt, S.-I., Berg, O. H., Swietlick, E., Wendisch, M., Yuskiewicz, B., Heitzenberg, J., Wiedensohler, A., Orsini, D., Stratmann, F., Lai, P., and Ricci, L.: Droplet formation and growth in polluted fogs, Contr. Atmos. Phys., 71, 65–85, 1998.
Frank, G. P., Cederfelt, S.-I., and Martinsson, B. G.: Characterisation of a unipolar charger for droplet aerosols of 0.1–20 μm in diameter, J. Aerosol Sci., 35, 117–134, 2004.
Frank, G. P., Berghof, M., Sjogren, S., and Martinsson, B.: A Droplet Aerosol Analyser (DAA) for Longterm Aerosol-Cloud Microphysics Measurements, in preparation, 2014.
Hoppel, W. A., Frick, G. M., and Fitzgerald, J.: Deducing droplet concentration and supersaturation in marine boundary layer clouds from surface aerosol measurements, J. Geophys. Res., 101, 26553–26565, 1996.
Kandler, K. and Schütz, L.: Climatology of the average water-soluble volume fraction of atmospheric aerosol, Atmos. Res., 83, 77–92, https://doi.org/10.1016/j.atmosres.2006.03.004, 2007.
Karlsson, M. N. A. and Martinsson, B. G.: Methods to measure and predict the transfer function size dependence of individual DMAs, J. Aerosol Sci., 34, 603–625, 2003.
Lawless, P. A.: Particle charging bounds, symmetry rrelation, and an analytic charging rate model for the continuum regime, J. Aerosol Sci., 27, 191–215, 1996.
Liou, K.-N. and Ou, S.-C.: The Role of Cloud Microphysical Processes in Climate:An Assessment From a One-Dimensional Perspective, J. Geophys. Res., 94, 8599–8607, 1989.
Lohmann, U. and Feichter, J.: Global indirect aerosol effects: a review, Atmos. Chem. Phys., 5, 715–737, https://doi.org/10.5194/acp-5-715-2005, 2005.
Martinsson, B. G.: Physical basis for a droplet aerosol analysing method, J. Aerosol Sci., 27, 997–1013, 1996.
Martinsson, B. G., Cederfelt, S. I., Svenningsson, B., Frank, G., Hansson, H. C., Swietlicki, E., Wiedensohler, A., Wendisch, M., Gallagher, M. W., Colvile, R. N., Beswick, K. M., Choularton, T. W., and Bower, K. N.: Experimental determination of the connection between cloud droplet size and its dry residue size, Atmos. Environ., 31, 2477–2490, 1997.
Martinsson, B. G., Frank, G. P., Cederfelt, S.-I., Swietlicki, E., Berg, O. H., Zhou, J., Bower, K. N., Bradbury, C., Birmili, W., Stratmann, F., Wendisch, M., Wiedensohler, A., and Yuskiewicz, B. A.: Droplet nucleation and growth in orographic clouds in relation to the aerosol population, Atmos. Res., 50, 289–315, 1999.
Martinsson, B. G., Frank, G., Cederfelt, S. I., Berg, O. H., Mentes, B., Papaspiropolos, G., Swietlicki, E., Zhou, J., Flynn, M., Bower, K. N., Choularton, T. W., Mäkelä, J., Virkkula, A., and v Dingenen, R.: Validation of very high cloud droplet number concentrations in air masses transported thousands of kilometres over the ocean, Tellus, 52, 801–814, 2000.
Martinsson, B. G., Karlsson, M. N. A., and Frank, G.: Metodology to Estimate the Transfer Function of Individual Differential Mobility Analyzers, Aerosol Sci. Technol., 35, 815–823, 2001.
Mertes, S., Lehmann, K., Nowak, A., Massling, A., and Wiedensohler, A.: Link between aerosolhygroscopic growth and droplet activation observed for hill-capped clouds at connected flow conditions during FEBUKO, Atmos. Environ., 39, 4247–4256, 2005.
Noone, K. J., Ogren, J. A., Heintzenberg, J., Charlson, R. J., and Covert, D. S.: Design and Calibration of a Counterflow Virtual Impactor for Sampling of Atmospheric Fog and Cloud Droplets, Aerosol Sci. Technol., 8, 235–244, https://doi.org/10.1080/02786828808959186, 1988.
Ogren, J. A., Heintzenberg, J., and Charlson, R. J.: In-situ sampling of clouds with a droplet to aerosol converter, Geophys. Res. Lett., 12, 121–124, https://doi.org/10.1029/GL012i003p00121, 1985.
Schwarzenboeck, A. and Heintzenberg, J.: Cut size minimization and cloud element break-up in a ground-based CVI, J. Aerosol Sci., 31, 477–489, 2000.
Sjogren, S., Frank, G. P., Berghof, M. I. A., and Martinsson, B. G.: Continuous stand-alone controllable aerosol/cloud droplet dryer for atmospheric sampling, Atmos. Meas. Tech., 6, 349–357, https://doi.org/10.5194/amt-6-349-2013, 2013.
Sorensen, C. M., Gebhart, J., O'Hern, T. J., and Rader, D. J.: Optical Measurement Techniques: Fundamentals and Applications, 269–312, John Wiley & Sons, Inc., https://doi.org/10.1002/9781118001684.ch13, 2011.
Spiegel, J. K., Zieger, P., Bukowiecki, N., Hammer, E., Weingartner, E., and Eugster, W.: Evaluating the capabilities and uncertainties of droplet measurements for the fog droplet spectrometer (FM-100), Atmos. Meas. Tech., 5, 2237–2260, https://doi.org/10.5194/amt-5-2237-2012, 2012.
Stevens, B. and Feingold, G.: Untangling aerosol effects on clouds and precipitation in a buffered system, Nature, 461, 607–613, 2009.
Stolzenburg, M. R.: An ultrafine aerosol size distribution measuring system., Ph.D. thesis, University of Minnesota, 1988.
Svenningsson, B., Hansson, H.-C., Martinsson, B. G., Wiedensohler, A., Swietlicki, E., Cederfelt, S.-I., Wendisch, M., Bower, K. N., Choularton, T. W., and Colvile, R. N.: Cloud droplet nucleation scavenging in relation to the size and hygroscopic behaviour of aerosol particles, Atmos. Environ., 31, 2463–2475, 1997.
Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 8, 1251–1256, 1974.
Warner, J.: A Reduction in Rainfall Associated with Smoke from Sugar-Cane Fires – An Inadvertent Weather Modification?, J. Appl. Meteorol., 7, 247–251, 1968.
Wiedensohler, A.: An approximation of the bipolar charge distribution for particles in the submicron size range, J. Aerosol Sci., 19, 387–389, 1988.
