Articles | Volume 4, issue 12
https://doi.org/10.5194/amt-4-2619-2011
https://doi.org/10.5194/amt-4-2619-2011
Research article
 | 
07 Dec 2011
Research article |  | 07 Dec 2011

Volcanic ash detection and retrievals using MODIS data by means of neural networks

M. Picchiani, M. Chini, S. Corradini, L. Merucci, P. Sellitto, F. Del Frate, and S. Stramondo

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
The impact and estimation of uncertainty correlation for multi-angle polarimetric remote sensing of aerosols and ocean color
Meng Gao, Kirk Knobelspiesse, Bryan A. Franz, Peng-Wang Zhai, Brian Cairns, Xiaoguang Xu, and J. Vanderlei Martins
Atmos. Meas. Tech., 16, 2067–2087, https://doi.org/10.5194/amt-16-2067-2023,https://doi.org/10.5194/amt-16-2067-2023, 2023
Short summary
POLIPHON conversion factors for retrieving dust-related cloud condensation nuclei and ice-nucleating particle concentration profiles at oceanic sites
Yun He, Zhenping Yin, Albert Ansmann, Fuchao Liu, Longlong Wang, Dongzhe Jing, and Huijia Shen
Atmos. Meas. Tech., 16, 1951–1970, https://doi.org/10.5194/amt-16-1951-2023,https://doi.org/10.5194/amt-16-1951-2023, 2023
Short summary
Ground-based remote sensing of aerosol properties using high-resolution infrared emission and lidar observations in the High Arctic
Denghui Ji, Mathias Palm, Christoph Ritter, Philipp Richter, Xiaoyu Sun, Matthias Buschmann, and Justus Notholt
Atmos. Meas. Tech., 16, 1865–1879, https://doi.org/10.5194/amt-16-1865-2023,https://doi.org/10.5194/amt-16-1865-2023, 2023
Short summary
The CALIPSO version 4.5 stratospheric aerosol subtyping algorithm
Jason L. Tackett, Jayanta Kar, Mark A. Vaughan, Brian J. Getzewich, Man-Hae Kim, Jean-Paul Vernier, Ali H. Omar, Brian E. Magill, Michael C. Pitts, and David M. Winker
Atmos. Meas. Tech., 16, 745–768, https://doi.org/10.5194/amt-16-745-2023,https://doi.org/10.5194/amt-16-745-2023, 2023
Short summary
Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case
Ilaria Petracca, Davide De Santis, Matteo Picchiani, Stefano Corradini, Lorenzo Guerrieri, Fred Prata, Luca Merucci, Dario Stelitano, Fabio Del Frate, Giorgia Salvucci, and Giovanni Schiavon
Atmos. Meas. Tech., 15, 7195–7210, https://doi.org/10.5194/amt-15-7195-2022,https://doi.org/10.5194/amt-15-7195-2022, 2022
Short summary

Cited articles

Aiuppa, A., Giudice, G., Gurrieri, S., Liuzzo, M., Burton, M., Caltabiano, T., McGonigle, A. J. S., Salerno, G., Shinohara, H., and Valenza M.: Total volatile flux from Mount Etna, Geophys. Res. Lett., 35, L24302, https://doi.org/10.1029/2008GL035871, 2008.
Anderson, G. P., Wang, J., and Chrtwynd, J. H.: MODTRAN3: An update and recent validation against airborne high resolution inferometer measurements, In Summaries of the Fifth Annual Jet Propulsion Laboratory Airborne Earth Science Workshop, 95-1 (1), 5–8, 1995.
Atkinson, P. M. and Tatnall, A. R. L.: Neural networks in remote sensing, Int. J. Remote Sens., 18, 699–709, 1997.
Bankert, R. L.: Cloud classification of AVHRR imagery in maritime regions using a probabilistic neural network, J. Appl. Meteorol., 33, 909–918, 1994.
Download