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Volume 10, issue 12
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.
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

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.
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Cited articles  
AERIS database, http://iasi.aeris-data.fr/NH3/, last access: 8 December 2017.
August, T., Klaes, D., Schlüssel, P., Hultberg, T., Crapeau, M., Arriaga, A., O'Carroll, A., Coppens, D., Munro, R., and Calbet, X.: IASI on Metop-A: Operational Level 2 retrievals after five years in orbit, J. Quant. Spectrosc. Ra., 113, 1340–1371, https://doi.org/10.1016/j.jqsrt.2012.02.028, 2012.
Bauduin, S., Clarisse, L., Hadji-Lazaro, J., Theys, N., Clerbaux, C., and Coheur, P.-F.: Retrieval of near-surface sulfur dioxide (SO2) concentrations at a global scale using IASI satellite observations, Atmos. Meas. Tech., 9, 721–740, https://doi.org/10.5194/amt-9-721-2016, 2016.
Bauduin, S., Clarisse, L., Theunissen, M., George, M., Hurtmans, D., Clerbaux, C., and Coheur, P.-F.: IASI's sensitivity to near-surface carbon monoxide (CO): Theoretical analyses and retrievals on test cases, J. Quant. Spectrosc. Ra., 189, 428–440, https://doi.org/10.1016/j.jqsrt.2016.12.022, 2017.
Beer, R., Shephard, M. W., Kulawik, S. S., Clough, S. A., Eldering, A., Bowman, K. W., Sander, S. P., Fisher, B. M., Payne, V. H., Luo, M., Osterman, G. B., and Worden, J. R.: First satellite observations of lower tropospheric ammonia and methanol, Geophys. Res. Lett., 35, L09801,https://doi.org/10.1029/2008GL033642, 2008.
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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...
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