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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 11 | Copyright
Atmos. Meas. Tech., 9, 5499-5508, 2016
https://doi.org/10.5194/amt-9-5499-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 18 Nov 2016

Research article | 18 Nov 2016

Errors induced by different approximations in handling horizontal atmospheric inhomogeneities in MIPAS/ENVISAT retrievals

Elisa Castelli1, Marco Ridolfi2,6, Massimo Carlotti3, Björn-Martin Sinnhuber4, Oliver Kirner5, Michael Kiefer4, and Bianca Maria Dinelli1 Elisa Castelli et al.
  • 1Istituto di Scienze dell'Atmosfera e del Clima, ISAC-CNR, Bologna, Italy
  • 2Dipartimento di Fisica e Astronomia, Universita' di Bologna, Bologna, Italy
  • 3Dipartimento di Chimica Industriale “Toso Montanari”, Universita' di Bologna, Bologna, Italy
  • 4Karlsruhe Institute of Technology, IMK-ASF, Karlsruhe, Germany
  • 5Karlsruhe Institute of Technology, Steinbuch Center for Computing, Karlsruhe, Germany
  • 6Istituto di Fisica Applicata “Nello Carrara”, IFAC-CNR, Firenze, Italy

Abstract. MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) is a mid-infrared limb emission sounder that operated on board the polar satellite ENVISAT from 2002 to 2012. The retrieval algorithm used by the European Space Agency to process MIPAS measurements exploits the assumption that the atmosphere is horizontally homogeneous. However, previous studies highlighted how this assumption causes significant errors on the retrieved profiles of some MIPAS target species.

In this paper we quantify the errors induced by this assumption and evaluate the performances of three different algorithms that can be used to mitigate the problem. We generate synthetic observations with a high spatial resolution atmospheric model and carry out the retrievals with four alternative methods. The first assumes horizontal homogeneity (1-D retrieval), the second includes a model of the horizontal gradient of atmospheric temperature (1-D plus temperature gradient retrieval), the third accounts for an horizontal gradient of temperature and composition (1-D plus temperature and composition gradient retrieval), while the fourth is the full two-dimensional (2-D) inversion approach.

Our results highlight that the 1-D retrieval implies errors that are significant for averages of profiles. Furthermore, for some targets (e.g. T, CH4 and N2O below 10hPa) the error induced by the 1-D approximation also becomes visible in the individual retrieved profiles. The inclusion of any kind of horizontal variability model improves all the targets with respect to the horizontal homogeneity assumption. For temperature, HNO3 and CFC-11, the inclusion of an horizontal temperature gradient leads to a significant reduction of the error. For other targets, such as H2O, O3, N2O, CH4, the improvements due to the inclusion of an horizontal temperature gradient are minor. In these cases, the inclusion of a gradient in the target volume mixing ratio leads to significant improvements. Among all the methods tested in this work, the 2-D approach, as expected, implies the smallest errors for almost all the target parameters. This residual error of the 2-D approach is the smoothing caused by the retrieval grid, which is coarser than that of the atmospheric model.

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MIPAS is a satellite-borne limb emission sounder. The algorithm used to infer atmospheric composition from its measurements exploits the assumption that the atmosphere is horizontally homogeneous. This assumption can cause significant errors. We use synthetic observations to quantify these errors. Furthermore we show that the inclusion of any kind of horizontal variability model improves all the retrieval targets and that the two-dimensional approach implies the smallest errors.
MIPAS is a satellite-borne limb emission sounder. The algorithm used to infer atmospheric...
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