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

Journal metrics

  • IF value: 3.089 IF 3.089
  • IF 5-year<br/> value: 3.700 IF 5-year
  • CiteScore<br/> value: 3.59 CiteScore
  • SNIP value: 1.273 SNIP 1.273
  • SJR value: 2.026 SJR 2.026
  • IPP value: 3.082 IPP 3.082
  • h5-index value: 45 h5-index 45
Atmos. Meas. Tech., 10, 4905-4914, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
15 Dec 2017
Version 2 of the IASI NH3 neural network retrieval algorithm: near-real-time and reanalysed datasets
Martin Van Damme et al.


Total article views: 711 (including HTML, PDF, and XML)

HTML PDF XML Total BibTeX EndNote
405 297 9 711 13 20

Views and downloads (calculated since 04 Aug 2017)

Cumulative views and downloads (calculated since 04 Aug 2017)

Viewed (geographical distribution)

Total article views: 708 (including HTML, PDF, and XML)

Thereof 701 with geography defined and 7 with unknown origin.

Country # Views %
  • 1


Saved (final revised paper)

Saved (discussion paper)

Discussed (final revised paper)

Discussed (discussion paper)

Latest update: 19 Mar 2018
Publications Copernicus
Short summary
This paper presents an improved version (v2.1) of the neural-network-based algorithm for retrieving atmospheric ammonia (NH3) columns from IASI satellite observations. Two datasets using different input data for the retrieval are described: one is based on the operationally provided EUMETSAT Level 2 (ANNI-NH3-v2.1), and the other uses the ECMWF ERA-Interim data (ANNI-NH3-v2.1R-I). Analyses illustrate well that the (meteorological) input data can have a large impact on the retrieved NH3 columns.
This paper presents an improved version (v2.1) of the neural-network-based algorithm for...