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

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https://doi.org/10.5194/amt-2017-239
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
04 Aug 2017
Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Measurement Techniques (AMT) and is expected to appear here in due course.
Version 2 of the IASI NH3 neural network retrieval algorithm; near-real time and reanalysed datasets
Martin Van Damme1, Simon Whitburn1, Lieven Clarisse1, Cathy Clerbaux1,2, Daniel Hurtmans1, and Pierre-François Coheur1 1Université libre de Bruxelles (ULB), Atmospheric Spectroscopy, Service de Chimie Quantique et Photophysique, Brussels, Belgium
2LATMOS/IPSL, UPMC Univ. Paris 06 Sorbonne Universités, UVSQ, CNRS, Paris, France
Abstract. Recently, Whitburn et al. (2016) presented a neural network-based algorithm for retrieving atmospheric ammonia (NH3) columns from IASI satellite observations. In the past year, several improvements have been introduced and the resulting new baseline version, ANNI-NH3-v2, is documented here. One of the main changes to the algorithm is that separate neural networks were trained for land and sea observations, resulting in a better training performance for both groups. By reducing and transforming the input parameter space, performance is now also better for observations associated with favourable sounding conditions (i.e. enhanced thermal contrasts). Other changes relate to the introduction of a bias correction over sea and the treatment of the satellite zenith angle. In addition to these algorithmic changes, new recommendations for post-filtering the data and for averaging data in time or space are formulated. We also introduce a second dataset (ANNI-NH3-v2R-I) which relies on ERA-Interim ECMWF meteorological input data, along with built-in surface temperature, rather than the operationally provided Eumetsat IASI L2 data used for the standard near-real time version. The need for such a dataset emerged after a series of sharp discontinuities were identified in the NH3 timeseries, which could be traced back to incremental changes in the IASI L2 algorithms for temperature and clouds. The reanalysed dataset is coherent in time and can therefore be used to study trends. Furthermore, both datasets agree reasonably well in the mean on recent data, after the date when the IASI meteorological L2 version 6 became operational (30 September 2014).

Citation: Van Damme, M., Whitburn, S., Clarisse, L., Clerbaux, C., Hurtmans, D., and Coheur, P.-F.: Version 2 of the IASI NH3 neural network retrieval algorithm; near-real time and reanalysed datasets, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2017-239, in review, 2017.
Martin Van Damme et al.
Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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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 
Martin Van Damme et al.
Martin Van Damme et al.

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Short summary
This paper presents an improved version 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 Level2 (ANNI-NH3-v2) and the other uses the ECMWF ERA-Interim data (ANNI-NH3-v2R-I). An analysis illustrates well the large impact of the (meteorological) input data can have on the retrieved NH3 column.
This paper presents an improved version of the neural network-based algorithm for retrieving...
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