Articles | Volume 9, issue 2
https://doi.org/10.5194/amt-9-619-2016
© Author(s) 2016. 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-9-619-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
24 Feb 2016
Research article |
| 24 Feb 2016
Software to analyze the relationship between aerosol, clouds, and precipitation: SAMAC
Department of Physics and Atmospheric Science, Dalhousie
University, Halifax, B3H 3J5, Canada
Environment Canada, Downsview, Toronto, M3H 5T4, Canada
now at: National Research Council Canada, Ottawa, Ontario, K1A 0R6,
Canada
L. P. MacDonald
Department of Physics and Atmospheric Science, Dalhousie
University, Halifax, B3H 3J5, Canada
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO 80523, USA
W. R. Leaitch
Environment Canada, Downsview, Toronto, M3H 5T4, Canada
J. R. Pierce
Department of Physics and Atmospheric Science, Dalhousie
University, Halifax, B3H 3J5, Canada
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO 80523, USA
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Victoria E. Irish, Sarah J. Hanna, Megan D. Willis, Swarup China, Jennie L. Thomas, Jeremy J. B. Wentzell, Ana Cirisan, Meng Si, W. Richard Leaitch, Jennifer G. Murphy, Jonathan P. D. Abbatt, Alexander Laskin, Eric Girard, and Allan K. Bertram
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Megan D. Willis, Heiko Bozem, Daniel Kunkel, Alex K. Y. Lee, Hannes Schulz, Julia Burkart, Amir A. Aliabadi, Andreas B. Herber, W. Richard Leaitch, and Jonathan P. D. Abbatt
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John K. Kodros, Sarah J. Hanna, Allan K. Bertram, W. Richard Leaitch, Hannes Schulz, Andreas B. Herber, Marco Zanatta, Julia Burkart, Megan D. Willis, Jonathan P. D. Abbatt, and Jeffrey R. Pierce
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Jun Liu, Jeramy Dedrick, Lynn M. Russell, Gunnar I. Senum, Janek Uin, Chongai Kuang, Stephen R. Springston, W. Richard Leaitch, Allison C. Aiken, and Dan Lubin
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Sangeeta Sharma, W. Richard Leaitch, Lin Huang, Daniel Veber, Felicia Kolonjari, Wendy Zhang, Sarah J. Hanna, Allan K. Bertram, and John A. Ogren
Atmos. Chem. Phys., 17, 15225–15243, https://doi.org/10.5194/acp-17-15225-2017, https://doi.org/10.5194/acp-17-15225-2017, 2017
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Franziska Köllner, Johannes Schneider, Megan D. Willis, Thomas Klimach, Frank Helleis, Heiko Bozem, Daniel Kunkel, Peter Hoor, Julia Burkart, W. Richard Leaitch, Amir A. Aliabadi, Jonathan P. D. Abbatt, Andreas B. Herber, and Stephan Borrmann
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Roya Ghahreman, Ann-Lise Norman, Betty Croft, Randall V. Martin, Jeffrey R. Pierce, Julia Burkart, Ofelia Rempillo, Heiko Bozem, Daniel Kunkel, Jennie L. Thomas, Amir A. Aliabadi, Gregory R. Wentworth, Maurice Levasseur, Ralf M. Staebler, Sangeeta Sharma, and W. Richard Leaitch
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We present spring and summertime vertical profile measurements of Arctic dimethyl sulfide (DMS), together with model simulations to consider what these profiles indicate about DMS sources and lifetimes in the Arctic. Our results highlight the role of local open water as the source of DMS(g) during July 2014 and the influence of long-range transport of DMS(g) from further afield in the Arctic during April 2015.
Julia Burkart, Megan D. Willis, Heiko Bozem, Jennie L. Thomas, Kathy Law, Peter Hoor, Amir A. Aliabadi, Franziska Köllner, Johannes Schneider, Andreas Herber, Jonathan P. D. Abbatt, and W. Richard Leaitch
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Andrew D. Teakles, Rita So, Bruce Ainslie, Robert Nissen, Corinne Schiller, Roxanne Vingarzan, Ian McKendry, Anne Marie Macdonald, Daniel A. Jaffe, Allan K. Bertram, Kevin B. Strawbridge, W. Richard Leaitch, Sarah Hanna, Desiree Toom, Jonathan Baik, and Lin Huang
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The first airborne measurements performed with the FIRR are presented. Vertical profiles of upwelling spectral radiance in the far-infrared are measured in the Arctic atmosphere for the first time. They show the impact of the temperature inversion on the radiative budget of the atmosphere, especially in the far-infrared. The presence of ice clouds also significantly alters the far-infrared budget, highlighting the critical interplay between water vapour and clouds in this very dry region.
W. Richard Leaitch, Alexei Korolev, Amir A. Aliabadi, Julia Burkart, Megan D. Willis, Jonathan P. D. Abbatt, Heiko Bozem, Peter Hoor, Franziska Köllner, Johannes Schneider, Andreas Herber, Christian Konrad, and Ralf Brauner
Atmos. Chem. Phys., 16, 11107–11124, https://doi.org/10.5194/acp-16-11107-2016, https://doi.org/10.5194/acp-16-11107-2016, 2016
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Thought to be mostly unimportant for summertime Arctic liquid-water clouds, airborne observations show that atmospheric aerosol particles 50 nm in diameter or smaller and most likely from natural sources are often involved in cloud formation in the pristine Arctic summer. The result expands the reference for aerosol forcing of climate. Further, for extremely low droplet concentrations, no evidence is found for a connection between cloud liquid water and aerosol particle concentrations.
Amir A. Aliabadi, Jennie L. Thomas, Andreas B. Herber, Ralf M. Staebler, W. Richard Leaitch, Hannes Schulz, Kathy S. Law, Louis Marelle, Julia Burkart, Megan D. Willis, Heiko Bozem, Peter M. Hoor, Franziska Köllner, Johannes Schneider, Maurice Levasseur, and Jonathan P. D. Abbatt
Atmos. Chem. Phys., 16, 7899–7916, https://doi.org/10.5194/acp-16-7899-2016, https://doi.org/10.5194/acp-16-7899-2016, 2016
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For the first time, ship emissions of an ice-breaker, the Amundsen, is characterized while breaking ice in the Canadian Arctic using the plume intercepts by the Polar 6 aircraft. The study is novel, estimating lower plume expansion rates over the stable Arctic marine boundary layer and different emissions factors for oxides of nitrogen, black carbon, and carbon monoxide, compared to plume intercept studies in mid latitudes. These results can inform policy making and emission inventory datasets.
Alex K. Y. Lee, Jonathan P. D. Abbatt, W. Richard Leaitch, Shao-Meng Li, Steve J. Sjostedt, Jeremy J. B. Wentzell, John Liggio, and Anne Marie Macdonald
Atmos. Chem. Phys., 16, 6721–6733, https://doi.org/10.5194/acp-16-6721-2016, https://doi.org/10.5194/acp-16-6721-2016, 2016
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Substantial biogenic secondary organic aerosol (BSOA) formation was investigated in a coniferous forest mountain region in Whistler, British Columbia. A largely biogenic aerosol growth episode was observed, providing a unique opportunity to investigate BSOA formation chemistry in a forested environment. In particular, our observations provide insights into the relative importance of different oxidation mechanisms between day and night.
Betty Croft, Randall V. Martin, W. Richard Leaitch, Peter Tunved, Thomas J. Breider, Stephen D. D'Andrea, and Jeffrey R. Pierce
Atmos. Chem. Phys., 16, 3665–3682, https://doi.org/10.5194/acp-16-3665-2016, https://doi.org/10.5194/acp-16-3665-2016, 2016
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Measurements at high-Arctic sites show a strong annual cycle in atmospheric particle number and size. Previous studies identified poor scientific understanding related to global model representation of Arctic particle number and size, limiting ability to simulate this environment. Here we evaluate state-of-science ability to simulate Arctic particles using GEOS-Chem-TOMAS model, documenting key roles and interconnections of particle formation, cloud-related processes and remaining uncertainties.
R. H. Mason, M. Si, C. Chou, V. E. Irish, R. Dickie, P. Elizondo, R. Wong, M. Brintnell, M. Elsasser, W. M. Lassar, K. M. Pierce, W. R. Leaitch, A. M. MacDonald, A. Platt, D. Toom-Sauntry, R. Sarda-Estève, C. L. Schiller, K. J. Suski, T. C. J. Hill, J. P. D. Abbatt, J. A. Huffman, P. J. DeMott, and A. K. Bertram
Atmos. Chem. Phys., 16, 1637–1651, https://doi.org/10.5194/acp-16-1637-2016, https://doi.org/10.5194/acp-16-1637-2016, 2016
S. D. D'Andrea, J. Y. Ng, J. K. Kodros, S. A. Atwood, M. J. Wheeler, A. M. Macdonald, W. R. Leaitch, and J. R. Pierce
Atmos. Chem. Phys., 16, 383–396, https://doi.org/10.5194/acp-16-383-2016, https://doi.org/10.5194/acp-16-383-2016, 2016
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We evaluate aerosol size distributions predicted by GEOS-Chem-TOMAS using measurements from the peak of Whistler Mountain. We improve model-measurement comparisons of aerosol number, size, and composition during periods of free-tropospheric and boundary-layer influence by developing simple filtering techniques, and determine the influence of Asian anthropogenic and biomass burning emissions. The low-cost filtering techniques and source apportionment methods can be used for other mountain sites.
F. Yu, G. Luo, S. C. Pryor, P. R. Pillai, S. H. Lee, J. Ortega, J. J. Schwab, A. G. Hallar, W. R. Leaitch, V. P. Aneja, J. N. Smith, J. T. Walker, O. Hogrefe, and K. L. Demerjian
Atmos. Chem. Phys., 15, 13993–14003, https://doi.org/10.5194/acp-15-13993-2015, https://doi.org/10.5194/acp-15-13993-2015, 2015
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The role of low-volatility organics in new particle formation (NPF) in the atmosphere is assessed. An empirical formulation in which formation rate is a function of the concentrations of sulfuric acid and low-volatility organics significantly overpredicts NPF in the summer.
Two different schemes predict quite different nucleation rates (including their spatial patterns), concentrations of cloud condensation nuclei, and aerosol first indirect radiative forcing in North America.
R. H. Mason, M. Si, J. Li, C. Chou, R. Dickie, D. Toom-Sauntry, C. Pöhlker, J. D. Yakobi-Hancock, L. A. Ladino, K. Jones, W. R. Leaitch, C. L. Schiller, J. P. D. Abbatt, J. A. Huffman, and A. K. Bertram
Atmos. Chem. Phys., 15, 12547–12566, https://doi.org/10.5194/acp-15-12547-2015, https://doi.org/10.5194/acp-15-12547-2015, 2015
A. Franchin, S. Ehrhart, J. Leppä, T. Nieminen, S. Gagné, S. Schobesberger, D. Wimmer, J. Duplissy, F. Riccobono, E. M. Dunne, L. Rondo, A. Downard, F. Bianchi, A. Kupc, G. Tsagkogeorgas, K. Lehtipalo, H. E. Manninen, J. Almeida, A. Amorim, P. E. Wagner, A. Hansel, J. Kirkby, A. Kürten, N. M. Donahue, V. Makhmutov, S. Mathot, A. Metzger, T. Petäjä, R. Schnitzhofer, M. Sipilä, Y. Stozhkov, A. Tomé, V.-M. Kerminen, K. Carslaw, J. Curtius, U. Baltensperger, and M. Kulmala
Atmos. Chem. Phys., 15, 7203–7216, https://doi.org/10.5194/acp-15-7203-2015, https://doi.org/10.5194/acp-15-7203-2015, 2015
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The ion-ion recombination coefficient was measured at different temperatures, relative humidities and concentrations of ozone and sulfur dioxide. The experiments were carried out using the CLOUD chamber at CERN.
We observed a strong dependency on temperature and on relative humidity, which has not been reported previously. No dependency of the ion-ion recombination coefficient on ozone concentration was observed and a weak variation with sulfur dioxide concentration was also observed.
J. C. Schroder, S. J. Hanna, R. L. Modini, A. L. Corrigan, S. M. Kreidenwies, A. M. Macdonald, K. J. Noone, L. M. Russell, W. R. Leaitch, and A. K. Bertram
Atmos. Chem. Phys., 15, 1367–1383, https://doi.org/10.5194/acp-15-1367-2015, https://doi.org/10.5194/acp-15-1367-2015, 2015
S. Schobesberger, A. Franchin, F. Bianchi, L. Rondo, J. Duplissy, A. Kürten, I. K. Ortega, A. Metzger, R. Schnitzhofer, J. Almeida, A. Amorim, J. Dommen, E. M. Dunne, M. Ehn, S. Gagné, L. Ickes, H. Junninen, A. Hansel, V.-M. Kerminen, J. Kirkby, A. Kupc, A. Laaksonen, K. Lehtipalo, S. Mathot, A. Onnela, T. Petäjä, F. Riccobono, F. D. Santos, M. Sipilä, A. Tomé, G. Tsagkogeorgas, Y. Viisanen, P. E. Wagner, D. Wimmer, J. Curtius, N. M. Donahue, U. Baltensperger, M. Kulmala, and D. R. Worsnop
Atmos. Chem. Phys., 15, 55–78, https://doi.org/10.5194/acp-15-55-2015, https://doi.org/10.5194/acp-15-55-2015, 2015
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We used an ion mass spectrometer at CERN's CLOUD chamber to investigate the detailed composition of ammonia--sulfuric acid ion clusters (of both polarities) as they initially form and then grow into aerosol particles, at atmospherically relevant conditions. We found that these clusters’ composition is mainly determined by the ratio of the precursor vapors and ranges from ammonia-free clusters to clusters containing > 1 ammonia per sulfuric acid. Acid--base bindings are a key formation mechanism.
J. D. Yakobi-Hancock, L. A. Ladino, A. K. Bertram, J. A. Huffman, K. Jones, W. R. Leaitch, R. H. Mason, C. L. Schiller, D. Toom-Sauntry, J. P. S. Wong, and J. P. D. Abbatt
Atmos. Chem. Phys., 14, 12307–12317, https://doi.org/10.5194/acp-14-12307-2014, https://doi.org/10.5194/acp-14-12307-2014, 2014
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As one aspect of the NETwork on Climate and Aerosols: addressing key uncertainties in Remote Canadian Environments, measurements of the cloud condensation nucleation properties of 50 nm and 100 nm aerosol particles were conducted at Ucluelet on the west coast of Vancouver Island in August 2013. The most efficient cloud condensation nuclei arose when the organic to sulfate ratio of the aerosol was lowest and when winds arrived from the west after transport through the marine boundary layer.
J. R. Pierce, D. M. Westervelt, S. A. Atwood, E. A. Barnes, and W. R. Leaitch
Atmos. Chem. Phys., 14, 8647–8663, https://doi.org/10.5194/acp-14-8647-2014, https://doi.org/10.5194/acp-14-8647-2014, 2014
J. E. Franklin, J. R. Drummond, D. Griffin, J. R. Pierce, D. L. Waugh, P. I. Palmer, M. Parrington, J. D. Lee, A. C. Lewis, A. R. Rickard, J. W. Taylor, J. D. Allan, H. Coe, K. A. Walker, L. Chisholm, T. J. Duck, J. T. Hopper, Y. Blanchard, M. D. Gibson, K. R. Curry, K. M. Sakamoto, G. Lesins, L. Dan, J. Kliever, and A. Saha
Atmos. Chem. Phys., 14, 8449–8460, https://doi.org/10.5194/acp-14-8449-2014, https://doi.org/10.5194/acp-14-8449-2014, 2014
A. Steffen, J. Bottenheim, A. Cole, R. Ebinghaus, G. Lawson, and W. R. Leaitch
Atmos. Chem. Phys., 14, 2219–2231, https://doi.org/10.5194/acp-14-2219-2014, https://doi.org/10.5194/acp-14-2219-2014, 2014
S. D. D'Andrea, S. A. K. Häkkinen, D. M. Westervelt, C. Kuang, E. J. T. Levin, V. P. Kanawade, W. R. Leaitch, D. V. Spracklen, I. Riipinen, and J. R. Pierce
Atmos. Chem. Phys., 13, 11519–11534, https://doi.org/10.5194/acp-13-11519-2013, https://doi.org/10.5194/acp-13-11519-2013, 2013
M. D. Gibson, J. R. Pierce, D. Waugh, J. S. Kuchta, L. Chisholm, T. J. Duck, J. T. Hopper, S. Beauchamp, G. H. King, J. E. Franklin, W. R. Leaitch, A. J. Wheeler, Z. Li, G. A. Gagnon, and P. I. Palmer
Atmos. Chem. Phys., 13, 7199–7213, https://doi.org/10.5194/acp-13-7199-2013, https://doi.org/10.5194/acp-13-7199-2013, 2013
B. Ervens, Y. Wang, J. Eagar, W. R. Leaitch, A. M. Macdonald, K. T. Valsaraj, and P. Herckes
Atmos. Chem. Phys., 13, 5117–5135, https://doi.org/10.5194/acp-13-5117-2013, https://doi.org/10.5194/acp-13-5117-2013, 2013
L. Ahlm, K. M. Shakya, L. M. Russell, J. C. Schroder, J. P. S. Wong, S. J. Sjostedt, K. L. Hayden, J. Liggio, J. J. B. Wentzell, H. A. Wiebe, C. Mihele, W. R. Leaitch, and A. M. Macdonald
Atmos. Chem. Phys., 13, 3393–3407, https://doi.org/10.5194/acp-13-3393-2013, https://doi.org/10.5194/acp-13-3393-2013, 2013
J. Leppä, S. Gagné, L. Laakso, H. E. Manninen, K. E. J. Lehtinen, M. Kulmala, and V.-M. Kerminen
Atmos. Chem. Phys., 13, 463–486, https://doi.org/10.5194/acp-13-463-2013, https://doi.org/10.5194/acp-13-463-2013, 2013
Related subject area
Subject: Clouds | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
An analysis of cloud microphysical features over United Arab Emirates using multiple data sources
IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme
Distribution characteristics of the summer precipitation raindrop spectrum on the Qinghai–Tibet Plateau
Exploring the effect of training set size and number of categories on ice crystal classification through a contrastive semi-supervised learning algorithm
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
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
Twenty-four-hour cloud cover calculation using a ground-based imager with machine learning
Application of cloud particle sensor sondes for estimating the number concentration of cloud water droplets and liquid water content: case studies in the Arctic region
Clouds over Hyytiälä, Finland: an algorithm to classify clouds based on solar radiation and cloud base height measurements
A convolutional neural network for classifying cloud particles recorded by imaging probes
Spatiotemporal variability of solar radiation introduced by clouds over Arctic sea ice
Analysis algorithm for sky type and ice halo recognition in all-sky images
Study of the diffraction pattern of cloud particles and the respective responses of optical array probes
A method for computing the three-dimensional radial distribution function of cloud particles from holographic images
A new method for calculating number concentrations of cloud condensation nuclei based on measurements of a three-wavelength humidified nephelometer system
Cloud radiative effect, cloud fraction and cloud type at two stations in Switzerland using hemispherical sky cameras
Evaluation of radar reflectivity factor simulations of ice crystal populations from in situ observations for the retrieval of condensed water content in tropical mesoscale convective systems
Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques
From pixels to patches: a cloud classification method based on a bag of micro-structures
Evaluation of cloud base height measurements from Ceilometer CL31 and MODIS satellite over Ahmedabad, India
Block-based cloud classification with statistical features and distribution of local texture features
Assessment of the performance of the inter-arrival time algorithm to identify ice shattering artifacts in cloud particle probe measurements
Assessment of cloud supersaturation by size-resolved aerosol particle and cloud condensation nuclei (CCN) measurements
Inversion of droplet aerosol analyzer data for long-term aerosol–cloud interaction measurements
Response of the Nevzorov hot wire probe in clouds dominated by droplet conditions in the drizzle size range
Zhenhai Zhang, Vesta Afzali Gorooh, Duncan Axisa, Chandrasekar Radhakrishnan, Eun Yeol Kim, Venkatachalam Chandrasekar, and Luca Delle Monache
Atmos. Meas. Tech., 18, 1981–2003, https://doi.org/10.5194/amt-18-1981-2025, https://doi.org/10.5194/amt-18-1981-2025, 2025
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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 using several cloud cases in the United Arab Emirates with data from aircraft measurements, satellite observations, and weather radar observations. This study provides scientific support for the development of an applicable framework to examine cloud precipitation processes.
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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This study analyzes precipitation features over the Qinghai–Tibet Plateau, focusing on drop size distribution (DSD), terminal velocity, number density, radar reflectivity (Z), and rainfall intensity (I) at four sites. Terminal velocity is fastest at Nyalam and slowest at Lhasa. Five microphysical characteristics exhibit different correlations with altitude. Z–I relationships differ by site, and both exponential and gamma distributions fit DSD, with gamma parameters varying with altitude.
Yunpei Chu, Huiying Zhang, Xia Li, and Jan Henneberger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3160, https://doi.org/10.5194/egusphere-2024-3160, 2024
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Our study improves ice crystal shape classification, key for understanding weather and climate. By adding unsupervised pre-training before supervised classification, our algorithm reduces manual labeling effort while maintaining high accuracy. It outperforms fully supervised models across datasets of varying sizes and categories, showing strong generalization ability. This method improves ice crystal classification techniques, making it adaptable to different environmental datasets.
Louis Jaffeux, Jan Breiner, Pierre Coutris, and Alfons Schwarzenböck
EGUsphere, https://doi.org/10.5194/egusphere-2024-1910, https://doi.org/10.5194/egusphere-2024-1910, 2024
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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.
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.
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
M. I. A. Berghof, G. P. Frank, S. Sjogren, and B. G. Martinsson
Atmos. Meas. Tech., 7, 877–886, https://doi.org/10.5194/amt-7-877-2014, https://doi.org/10.5194/amt-7-877-2014, 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
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Short summary
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.
Measurements of clouds with an aircraft are essential to understand how clouds form and how they...
