Abstract
Abstract. Recently, Whitburn et al.(2016) presented a neural-network-based algorithm for retrieving atmospheric ammonia (NH3) columns from Infrared Atmospheric Sounding Interferometer (IASI) satellite observations. In the past year, several improvements have been introduced, and the resulting new baseline version, Artificial Neural Network for IASI (ANNI)-NH3-v2.1, 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 land and 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-v2.1R-I) which relies on ERA-Interim ECMWF meteorological input data, along with surface temperature retrieved from a dedicated network, rather than the operationally provided Eumetsat IASI Level 2 (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 time series, 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).
Highlights
Ammonia measurements from space have come a long way since the first observations were reported (Beer et al, 2008; Coheur et al, 2009)
In the first part of the present paper we report and detail several improvements that have been introduced to the original neural-network-based retrieval, here referred to as “Artificial Neural Network for Infrared Atmospheric Sounding Interferometer (IASI)”-NH3-v1 (ANNI-NH3-v1)
In the final part we introduce a new dataset, ANNI-NH3-v2.1R-I, which differs from the baseline dataset, ANNI-NH3-v2.1, in that it uses different input data
Summary
Ammonia measurements from space have come a long way since the first observations were reported (Beer et al, 2008; Coheur et al, 2009). Van Damme et al (2014a) used 2-D look-up tables to convert HRIs to columns, while Whitburn et al (2016) used a neural network (NN) to perform the conversion The advantage of the latter is that it allows a much larger number of input parameters to be taken into account. While our baseline version uses operationally provided meteorological Level 2 (L2) data, this reanalysed dataset relies on input data from the ERA-Interim ECMWF reanalysis (Dee et al, 2011) and a secondary neural network for surface temperature retrieval The need for such a dataset arose after discontinuities were found in the analysis of time series which could be traced back to version changes in the IASI L2 processing chain for temperature and clouds.
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