Errors induced by different approximations in handling horizontal atmospheric inhomogeneities in MIPAS/ENVISAT retrievals
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, CH 4 and N 2O below 10 hPa) 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, HNO 3 and CFC-11, the inclusion of an horizontal temperature gradient leads to a significant reduction of the error. For other targets, such as H 2O, O 3, N 2O, CH 4, 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.