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.

Satellite limb scanning spectrometers have been widely used to measure atmospheric composition and its evolution with time. In many cases the atmospheric composition is obtained from these measurements with retrieval schemes that assume a horizontally homogeneous atmosphere (the so-called one-dimensional, or 1-D retrieval approach). In most of the stratosphere this assumption does not produce huge systematic errors. However, in the case of strong horizontal variability, retrieved profiles may be affected by significant error.

In order to quantify this error, we exploit synthetic observations simulated
for the MIPAS (Michelson Interferometer for Passive Atmospheric Sounding)
instrument, which operated on board the ENVISAT satellite from March 2002 to
April 2012. The instrument observed the atmospheric mid-infrared emission
spectrum using the limb-scanning observation technique

In order to account for the horizontal inhomogeneities of the atmosphere,
tomographic inversion codes with the capability of a full two-dimensional
(2-D) model were developed for MIPAS, e.g. geofit multi-target
retrieval (GMTR,

The paper is structured as follows: in Sect.

When analysing real measurements, the true atmospheric state is not known. For an accurate assessment of the systematic retrieval errors due to different approaches used to account for the horizontal variability, we therefore use synthetic observations based on data from a high-resolution simulation of EMAC and on a 2-D FM which is as accurate as possible. We then apply different retrieval algorithms to these observations and evaluate the errors on the basis of the differences between retrieved and reference target parameters.

Temperature, O

The reference atmosphere used to produce the synthetic spectra was extracted
from a high-resolution (T85 truncation, corresponding to a horizontal
resolution of

The results of

The synthetic observations used in our analysis are produced with the 2-D FM
internal to the GMTR code. In

In order to focus our analysis on the error caused solely by the
approximations in modelling the horizontal variability of temperature and the
target gas, we modified the
reference atmosphere for the generation of synthetic observations as follows: the observations used for the retrieval of
the VMR of a given gas were generated assuming the 2-D distributions of the reference atmospheric model of
Sect.

From previous studies (mentioned in Sect.

We carried out the retrievals on these synthetic observations using four
algorithms. All of them were based on the GMTR algorithm of

The four algorithms, however, differ in modelling the horizontal variability
of the atmosphere. In the 1-D algorithm the atmosphere is assumed to be
horizontally homogeneous, in the 1-D

While in the first three retrieval methods each limb scan is processed
individually with a global fit

The horizontal gradients used in the 1-D

Note that, in the 2-D approach, a vertical profile was retrieved at the average position of each measured limb scan. Since the spacing between adjacent limb scans is of the order of 400 km, it follows that the 2-D retrievals are affected by the so-called “smoothing error” due to a retrieval grid step which is coarser than the step adopted in the reference atmosphere.

The target profiles we retrieve from the synthetic observations are pressure,
temperature (pT joint retrieval), the VMR of H

AX–DX differences for 21 December 2011 in the 45–60

AX–DX differences for 21 December 2011 in the 45–60

AX–DX differences on 21 December 2011 in the 45–60

In order to characterise the performance of the different approaches employed
to model horizontal variability, we first group the profiles resulting from
synthetic retrievals (

In order to evaluate the performance of the horizontal variability models in
different measurement conditions, we also group the results of synthetic
retrievals according to the following classes: (a) from 60 to 90

RMSE (root mean square error) calculations for temperature (upper
panels) and H

RMSE calculations for O

Errors due to different treatments of horizontal inhomogeneities
(1-D, 1-D

In Figs.

From top to bottom: RMSE calculations for N

Figures

Figures

In the case of temperature, the 1-D method provides the largest RMSE in polar winter and midlatitude conditions and the smallest RMSE in the equatorial
scenario (Fig.

These results show that the introduction of all of the models for horizontal
variability produce improvements for all the targets
(Table

RMSE improvements due to different treatments of horizontal
inhomogeneities (1-D

The modelling of a temperature horizontal gradient produces a significant
reduction of the error in temperature, HNO

The RMSEs obtained with the 2-D approach, reported in the last column of
Table

In this work we quantify the error induced by neglecting the horizontal variability of the atmosphere in MIPAS retrievals and characterise possible alternative retrieval approaches that could help to reduce this error. Our study is based on synthetic limb observations generated with an accurate 2-D forward model that assumes a known atmospheric state taken from a high-resolution model (EMAC). We evaluate the relative performance of some different retrieval approaches: the simple 1-D model, the model of horizontal temperature gradients, the model of both temperature and a VMR horizontal gradients, the full 2-D model.

The results show that neglecting the horizontal atmospheric variability can
produce average errors of

Modelling a temperature horizontal gradient improves temperature (error
reduced to 0.6 K), HNO

Modelling both temperature and VMR gradients reduces the error to 3–10 %
for H

The horizontal temperature and VMR gradient values used in our tests are derived from the corresponding atmospheric fields retrieved with the 1-D assumption. We consider these gradients to be relatively accurate. We verified, however, that using less accurate gradients, such as those that can be inferred, e.g. from ECMWF analyses, usually produces a less significant reduction of the retrieval error.

The 2-D retrieval approach produces the smallest error in modelling the
horizontal variability of the atmosphere. We note that the real benefits of
the 2-D approach are even more evident when looking at the vertical
distribution of the errors: the 2-D results always perform better than
the other approaches, especially in the altitude regions and latitudinal
bands where the horizontal variability is largest. The remaining error is due
to the horizontal smoothing intrinsic to the measuring system. With the
adopted atmospheric model, this smoothing error is of the order of 0.5 K
for temperature, 2–8 % for H

Data retrieved from MIPAS synthetic observations used in this study are available from the authors upon request.

This work was performed under ESA-ESRIN Contract no. 21719/08/I-OL. The authors gratefully acknowledge Richard Siddans (RAL) for proofreading the manuscript and ECMWF for access to data. Edited by: P. K. Bhartia Reviewed by: three anonymous referees