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
    3.700
  • CiteScore<br/> value: 3.59 CiteScore
    3.59
  • 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
https://doi.org/10.5194/amt-10-4905-2017
© 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.
Download
Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
 
RC1: 'Review of Van Damme et al', Anonymous Referee #2, 21 Aug 2017 Printer-friendly Version 
AC2: 'Reply to anonymous referee #2', Martin Van Damme, 20 Oct 2017 Printer-friendly Version Supplement 
 
RC2: 'Referee Comment', Anonymous Referee #1, 31 Aug 2017 Printer-friendly Version 
AC1: 'Reply to anonymous referee #1', Martin Van Damme, 20 Oct 2017 Printer-friendly Version Supplement 
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
AR by Martin Van Damme on behalf of the Authors (20 Oct 2017)  Author's response  Manuscript
ED: Publish as is (07 Nov 2017) by Mark Weber
CC BY 4.0
Publications Copernicus
Download
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...
Share