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

Journal metrics

Journal metrics

  • IF value: 3.248 IF 3.248
  • IF 5-year value: 3.650 IF 5-year
    3.650
  • CiteScore value: 3.37 CiteScore
    3.37
  • SNIP value: 1.253 SNIP 1.253
  • IPP value: 3.29 IPP 3.29
  • SJR value: 1.869 SJR 1.869
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 60 Scimago H
    index 60
  • h5-index value: 47 h5-index 47
AMT | Articles | Volume 12, issue 4
Atmos. Meas. Tech., 12, 2567-2578, 2019
https://doi.org/10.5194/amt-12-2567-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 12, 2567-2578, 2019
https://doi.org/10.5194/amt-12-2567-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 May 2019

Research article | 02 May 2019

Neural network radiative transfer for imaging spectroscopy

Brian D. Bue et al.
Related authors  
Airborne DOAS retrievals of methane, carbon dioxide, and water vapor concentrations at high spatial resolution: application to AVIRIS-NG
Andrew K. Thorpe, Christian Frankenberg, David R. Thompson, Riley M. Duren, Andrew D. Aubrey, Brian D. Bue, Robert O. Green, Konstantin Gerilowski, Thomas Krings, Jakob Borchardt, Eric A. Kort, Colm Sweeney, Stephen Conley, Dar A. Roberts, and Philip E. Dennison
Atmos. Meas. Tech., 10, 3833-3850, https://doi.org/10.5194/amt-10-3833-2017,https://doi.org/10.5194/amt-10-3833-2017, 2017
Short summary
Related subject area  
Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Polarimetric radar characteristics of lightning initiation and propagating channels
Jordi Figueras i Ventura, Nicolau Pineda, Nikola Besic, Jacopo Grazioli, Alessandro Hering, Oscar A. van der Velde, David Romero, Antonio Sunjerga, Amirhossein Mostajabi, Mohammad Azadifar, Marcos Rubinstein, Joan Montanyà, Urs Germann, and Farhad Rachidi
Atmos. Meas. Tech., 12, 2881-2911, https://doi.org/10.5194/amt-12-2881-2019,https://doi.org/10.5194/amt-12-2881-2019, 2019
Short summary
Processing and quality control of FY-3C GNOS data used in numerical weather prediction applications
Mi Liao, Sean Healy, and Peng Zhang
Atmos. Meas. Tech., 12, 2679-2692, https://doi.org/10.5194/amt-12-2679-2019,https://doi.org/10.5194/amt-12-2679-2019, 2019
Short summary
Investigation of observational error sources in multi-Doppler-radar three-dimensional variational vertical air motion retrievals
Mariko Oue, Pavlos Kollias, Alan Shapiro, Aleksandra Tatarevic, and Toshihisa Matsui
Atmos. Meas. Tech., 12, 1999-2018, https://doi.org/10.5194/amt-12-1999-2019,https://doi.org/10.5194/amt-12-1999-2019, 2019
Short summary
Better turbulence spectra from velocity–azimuth display scanning wind lidar
Felix Kelberlau and Jakob Mann
Atmos. Meas. Tech., 12, 1871-1888, https://doi.org/10.5194/amt-12-1871-2019,https://doi.org/10.5194/amt-12-1871-2019, 2019
Short summary
The use of GNSS zenith total delays in operational AROME/Hungary 3D-Var over a central European domain
Máté Mile, Patrik Benáček, and Szabolcs Rózsa
Atmos. Meas. Tech., 12, 1569-1579, https://doi.org/10.5194/amt-12-1569-2019,https://doi.org/10.5194/amt-12-1569-2019, 2019
Short summary
Cited articles  
Asner, G., Martin, R., Knapp, D., Tupayachi, R., Anderson, C., Sinca, F., Vaughn, N., and Llactayo, W.: Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation, Science, 355, 385–389, 2017. a
Bodhaine, B. A., Wood, N. B., Dutton, E. G., and Slusser, J. R.: On Rayleigh optical depth calculations, J. Atmos. Ocean. Tech., 16, 1854–1861, 1999. a
Brajard, J., Jamet, C., Moulin, C., and Thiria, S.: Use of a neuro-variational inversion for retrieving oceanic and atmospheric constituents from satellite ocean colour sensor: Application to absorbing aerosols, Neural Networks, 19, 178–185, https://doi.org/10.1016/j.neunet.2006.01.015, 2006. a, b
Buehler, S. A., John, V. O., Kottayil, A., Milz, M., and Eriksson, P.: ARTICLE IN PRESS, J. Quant. Spectr. Ra., 111, 602–615, https://doi.org/10.1016/j.jqsrt.2009.10.018, 2009. a
Emde, C., Buras-Schnell, R., Kylling, A., Mayer, B., Gasteiger, J., Hamann, U., Kylling, J., Richter, B., Pause, C., Dowling, T., and Bugliaro, L.: The libRadtran software package for radiative transfer calculations (version 2.0.1), Geosci. Model Dev., 9, 1647–1672, https://doi.org/10.5194/gmd-9-1647-2016, 2016. a
Publications Copernicus
Download
Short summary
Imaging spectrometers provide valuable remote measurements of Earth's surface and atmosphere. These measurements rely on computationally expensive radiative transfer models (RTMs). Spectrometers produce too much data to process with RTMs directly, requiring approximations that trade accuracy for speed. We demonstrate that neural networks can quickly emulate RTM calculations more accurately than current approaches, enabling the application of more sophisticated RTMs than current methods permit.
Imaging spectrometers provide valuable remote measurements of Earth's surface and atmosphere....
Citation