Articles | Volume 9, issue 2
https://doi.org/10.5194/amt-9-619-2016
https://doi.org/10.5194/amt-9-619-2016
Research article
 | 
24 Feb 2016
Research article |  | 24 Feb 2016

Software to analyze the relationship between aerosol, clouds, and precipitation: SAMAC

S. Gagné, L. P. MacDonald, W. R. Leaitch, and J. R. Pierce

Related authors

Quantifying the uncertainties in thermal–optical analysis of carbonaceous aircraft engine emissions: an interlaboratory study
Timothy A. Sipkens, Joel C. Corbin, Brett Smith, Stéphanie Gagné, Prem Lobo, Benjamin T. Brem, Mark P. Johnson, and Gregory J. Smallwood
Atmos. Meas. Tech., 17, 4291–4302, https://doi.org/10.5194/amt-17-4291-2024,https://doi.org/10.5194/amt-17-4291-2024, 2024
Short summary
Technical note: Common glitch affecting the EC/OC split point determination in the Sunset Thermal-Optical Analyzerand recommendations to reduce its occurrence
Stéphanie Gagné, Brett Smith, Gregory J. Smallwood, and Joel C. Corbin
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-298,https://doi.org/10.5194/amt-2019-298, 2019
Preprint withdrawn
Short summary
Experimental investigation of ion–ion recombination under atmospheric conditions
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
Short summary
On the composition of ammonia–sulfuric-acid ion clusters during aerosol particle formation
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
Short summary
Using measurements of the aerosol charging state in determination of the particle growth rate and the proportion of ion-induced nucleation
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
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
Short summary
IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme
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
Short summary
Distribution characteristics of the summer precipitation raindrop spectrum on the Qinghai–Tibet Plateau
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
Short summary
Exploring the effect of training set size and number of categories on ice crystal classification through a contrastive semi-supervised learning algorithm
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
Short summary
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
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
Short summary

Cited articles

Anderson, T. L. and Ogren, J. A.: Determining Aerosol Radiative Properties Using the TSI 3563 Integrating Nephelometer, Aerosol Sci. Tech. 29, 57–69, https://doi.org/10.1080/02786829808965551, 1998.
Atmospheric Science Software Applications – UCAR Community Tools (2009): available at: https://www.ucar.edu/tools/applications_desc.jsp (last access: 1 January 2016), 2009.
Barnes, N.: Publish your computer code: it is good enough, Nature, 467, p. 753, 2010.
Beazley, D. M.: Python Essential Reference, 4th Ed., Developper's Library, Addison-Wesley, Boston, USA, 2009.
Bond, T. C., Charlson, R. J., and Heintzenberg, J.: Qunatifying the emission of light-absorbing particles: Measurements tailored to climate studies, Geophys. Res. Lett., 25, 337–340, 1998.
Download
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.
Share