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

Abstract

Abstract. MIPAS (Michelson Interferometer for PassiveAtmospheric 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 2 O 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 2 O, O 3 , N 2 O, 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.

CCl<sub>4</sub> distribution derived from MIPAS ESA V7 data: validation, trend and lifetime estimation

“…Phys. in Castelli et al (2016), cause systematic (and opposite in sign) differences between profiles retrieved from measurements acquired in the ascending and the descending parts of the satellite orbits. These errors cancel-out when calculating averages that evenly include profiles retrieved from measurements belonging to the ascending and the descending parts of the orbits.…”

CCl<sub>4</sub> distribution derived from MIPAS ESA v7 data: intercomparisons, trend, and lifetime estimation

“…Thus, in the calculated (monthly) averages they scale down with the inverse square root of the number of averaged profiles. The errors of type (b), as shown in Castelli et al (2016), cause systematic (and opposite in sign) differences between profiles retrieved from measurements acquired in the ascending and the descending parts of the satellite orbits. These errors largely cancel out when calculating averages that evenly include profiles retrieved from measurements belonging to the ascending and the descending parts of the orbits.…”

Estimating and Reporting Uncertainties in Remotely Sensed Atmospheric Composition and Temperature

“…The latter scheme lends itself particularly to reprocessing of data when the initial processing information on the horizontal variability is already available. This approach has been critically analyzed by Castelli et al (2016). Tomographic approaches and the effect of horizontal gradients were investigated for SCIAMACHY limb measurements by Pukite et al (2008) and Pukite et al (2010).…”