Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 3.668
IF3.668
IF 5-year value: 3.707
IF 5-year
3.707
CiteScore value: 6.3
CiteScore
6.3
SNIP value: 1.383
SNIP1.383
IPP value: 3.75
IPP3.75
SJR value: 1.525
SJR1.525
Scimago H <br class='widget-line-break'>index value: 77
Scimago H
index
77
h5-index value: 49
h5-index49
AMT | Articles | Volume 13, issue 10
Atmos. Meas. Tech., 13, 5459–5480, 2020
https://doi.org/10.5194/amt-13-5459-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 13, 5459–5480, 2020
https://doi.org/10.5194/amt-13-5459-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 14 Oct 2020

Research article | 14 Oct 2020

Leveraging spatial textures, through machine learning, to identify aerosols and distinct cloud types from multispectral observations

Willem J. Marais et al.

Related authors

Measurement report: Fireworks impacts on air quality in Metro Manila, Philippines during the 2019 New Year revelry
Genevieve Rose Lorenzo, Paola Angela Bañaga, Maria Obiminda Cambaliza, Melliza Templonuevo Cruz, Mojtaba Azadi Agdham, Avelino Arellano, Grace Betito, Rachel Braun, Andrea F. Corral, Hossein Dadashazar, Eva-Lou Edwards, Edwin Eloranta, Robert Holz, Gabrielle Leung, Lin Ma, Alexander B. MacDonald, James Bernard Simpas, Connor Stahl, Shane Marie Visaga, and Armin Sorooshian
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-1028,https://doi.org/10.5194/acp-2020-1028, 2020
Preprint under review for ACP
Short summary
Long-range transport patterns into the tropical northwest Pacific during the CAMP2Ex aircraft campaign: chemical composition, size distributions, and the impact of convection
Miguel Ricardo A. Hilario, Ewan Crosbie, Michael Shook, Jeffrey S. Reid, Maria Obiminda L. Cambaliza, James Bernard B. Simpas, Luke Ziemba, Joshua P. DiGangi, Glenn S. Diskin, Phu Nguyen, Joseph Turk, Edward Winstead, Claire E. Robinson, Jian Wang, Jiaoshi Zhang, Yang Wang, Subin Yoon, James Flynn, Sergio L. Alvarez, Ali Behrangi, and Armin Sorooshian
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-961,https://doi.org/10.5194/acp-2020-961, 2020
Preprint under review for ACP
Short summary
Development of an OMI AI data assimilation scheme for aerosol modeling over bright surfaces – a step toward direct radiance assimilation in the UV spectrum
Jianglong Zhang, Robert J. D. Spurr, Jeffrey S. Reid, Peng Xian, Peter R. Colarco, James R. Campbell, Edward J. Hyer, and Nancy Baker
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-216,https://doi.org/10.5194/gmd-2020-216, 2020
Revised manuscript accepted for GMD
Short summary
Revisiting the Relationship between Atlantic Dust and Tropical Cyclone Activity using Aerosol Optical Depth Reanalyses: 2003–2018
Peng Xian, Philip J. Klotzbach, Jason P. Dunion, Matthew A. Janiga, Jeffrey S. Reid, Peter R. Colarco, and Zak Kipling
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-287,https://doi.org/10.5194/acp-2020-287, 2020
Revised manuscript accepted for ACP
Short summary
Investigating size-segregated sources of elemental composition of particulate matter in the South China Sea during the 2011 Vasco cruise
Miguel Ricardo A. Hilario, Melliza T. Cruz, Maria Obiminda L. Cambaliza, Jeffrey S. Reid, Peng Xian, James B. Simpas, Nofel D. Lagrosas, Sherdon Niño Y. Uy, Steve Cliff, and Yongjing Zhao
Atmos. Chem. Phys., 20, 1255–1276, https://doi.org/10.5194/acp-20-1255-2020,https://doi.org/10.5194/acp-20-1255-2020, 2020
Short summary

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Correction of a lunar-irradiance model for aerosol optical depth retrieval and comparison with a star photometer
Roberto Román, Ramiro González, Carlos Toledano, África Barreto, Daniel Pérez-Ramírez, Jose A. Benavent-Oltra, Francisco J. Olmo, Victoria E. Cachorro, Lucas Alados-Arboledas, and Ángel M. de Frutos
Atmos. Meas. Tech., 13, 6293–6310, https://doi.org/10.5194/amt-13-6293-2020,https://doi.org/10.5194/amt-13-6293-2020, 2020
Short summary
Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm
Hai Zhang, Shobha Kondragunta, Istvan Laszlo, and Mi Zhou
Atmos. Meas. Tech., 13, 5955–5975, https://doi.org/10.5194/amt-13-5955-2020,https://doi.org/10.5194/amt-13-5955-2020, 2020
Short summary
Stratospheric aerosol extinction profiles from SCIAMACHY solar occultation
Stefan Noël, Klaus Bramstedt, Alexei Rozanov, Elizaveta Malinina, Heinrich Bovensmann, and John P. Burrows
Atmos. Meas. Tech., 13, 5643–5666, https://doi.org/10.5194/amt-13-5643-2020,https://doi.org/10.5194/amt-13-5643-2020, 2020
Short summary
A feasibility study to use machine learning as an inversion algorithm for aerosol profile and property retrieval from multi-axis differential absorption spectroscopy measurements
Yun Dong, Elena Spinei, and Anuj Karpatne
Atmos. Meas. Tech., 13, 5537–5550, https://doi.org/10.5194/amt-13-5537-2020,https://doi.org/10.5194/amt-13-5537-2020, 2020
Short summary
Retrieval of aerosol properties from Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) observations during ACEPOL 2017
Anin Puthukkudy, J. Vanderlei Martins, Lorraine A. Remer, Xiaoguang Xu, Oleg Dubovik, Pavel Litvinov, Brent McBride, Sharon Burton, and Henrique M. J. Barbosa
Atmos. Meas. Tech., 13, 5207–5236, https://doi.org/10.5194/amt-13-5207-2020,https://doi.org/10.5194/amt-13-5207-2020, 2020
Short summary

Cited articles

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I. J., Harp, A., Irving, G., Isard, M., Jia, Y., Józefow-icz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D. G., Olah, C., Schuster, M., Shlens, J.,Steiner, B., Sutskever, I., Talwar, K., Tucker, P. A., Vanhoucke,V., Vasudevan, V., Viégas, F. B., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: : Tensorflow: Large-scale machine learning on heterogeneous distributed systems, arXiv [preprint], arXiv:1603.04467, 14 March 2016. a, b
Al-Saadi, J., Szykman, J., Pierce, R. B., Kittaka, C., Neil, D., Chu, D. A., Remer, L., Gumley, L., Prins, E., Weinstock, L., Wayland, R., Dimmick, F., and Fishman, J.: Improving national air quality forecasts with satellite aerosol observations, B. Am. Meteorol. Soc., 86, 1249–1262, 2005. a
Blackwell, W. J.: A neural-network technique for the retrieval of atmospheric temperature and moisture profiles from high spectral resolution sounding data, IEEE T. Geosci. Remote, 43, 2535–2546, 2005. a
Boukabara, S.-A., Krasnopolsky, V., Stewart, J. Q., Maddy, E. S., Shahroudi, N., and Hoffman, R. N.: Leveraging Modern Artificial Intelligence for Remote Sensing and NWP: Benefits and Challenges, B. Am. Meteorol. Soc., 100, ES473–ES491, 2019. a
Chilson, C., Avery, K., McGovern, A., Bridge, E., Sheldon, D., and Kelly, J.: Automated detection of bird roosts using NEXRAD radar data and Convolutional neural networks, Remote Sensing in Ecology and Conservation, 5, 20–32, 2019. a, b
Publications Copernicus
Download
Short summary
Space agencies use moderate-resolution satellite imagery to study how smoke, dust, pollution (aerosols) and cloud types impact the Earth's climate; these space agencies include NASA, ESA and the China Meteorological Administration. We demonstrate in this paper that an algorithm with convolutional neural networks can greatly enhance the automated detection of aerosols and cloud types from satellite imagery. Our algorithm is an improvement on current aerosol and cloud detection algorithms.
Space agencies use moderate-resolution satellite imagery to study how smoke, dust, pollution...
Citation