We present a retrieval algorithm for nitric oxide (

Since the nominal mode limb scans extend only to about 91 km, we use

Analysing all SCIAMACHY nominal limb scans provides almost
10 years (from August 2002 to April 2012) of daily

Solar, auroral, and radiation belt electrons as well as soft solar X-rays
produce nitric oxide (

We adapt the

Exactly as in the case of the MLT retrieval, we use SCIAMACHY's UV
(ultra-violet) channel 1 (214–334 nm) to derive the

Further

This paper is organised as follows: we present some details about SCIAMACHY and its
nominal limb mode in Sect.

SCIAMACHY is a UV–visible–near-infrared (214–2380 nm) spectrometer on the European Envisat
satellite. This satellite was in a sun-synchronous orbit at approximately
800 km altitude from 2002

From the beginning of its mission in August 2002 to the middle of October 2003,
the nominal limb scans extended up to 105 km.
On 15 October 2003,
the sampling pattern of the nominal mode was changed to 30 limb tangent points
from

As in the case of the SCIAMACHY MLT retrieval

The retrieval is adapted from the SCIAMACHY MLT

As discussed in

The SCIAMACHY spectrometer resolves the vibrational lines but not
the rotational lines.
An example of a synthetic spectrum in the SCIAMACHY UV wavelength
range can be found in

A generic objective in atmospheric remote sounding is to extract the relevant
values (almost always at places without direct measurements) from the
measured quantities. SCIAMACHY, and any limb sounder in general, measures the
electromagnetic spectra, reaching the instrument from along the line of sight
through the atmosphere. In this study, we aim to derive the

The general forward model

Results from the SCIAMACHY MLT sample orbit (no. 41 467, 3 February
2010) restricted to the 50–91 km limb tangent points.

Our forward model calculates the line of sight fraction within each grid cell
and accounts for ozone and weak oxygen absorption along the line of sight
from the grid point to the satellite and along the line from the sun to the
grid point. Knowing the length of the line of sight within the grid cell, we
can then calculate the possible emission signal from this cell. The details
of this calculation are laid out in

Fitting each vibrational

The electronic excitation and emission occur at different wavelengths in general and for the three chosen gamma bands in particular. To account for this, we calculate the ozone and oxygen absorption-corrected emission matrix for the three gamma bands separately.

We subtract the Rayleigh background before the spectral fit. This enables

Absolute

In our case,

For one orbit, the SCIAMACHY MLT limb scans involve approximately

Underdetermined systems can be solved using additional constraints, such as a
priori input and regularisations

Note that, because of improved
so-called M-factors (a measure of the degradation of the SCIAMACHY detectors)
and an adjusted fit error calculation, the values reported here differ from
the ones given in

Regularisation parameters as used in the

The retrieval is equal to minimising the regularised

In this notation
the subscript matrix acts like a metric for the norm:

The SCIAMACHY nominal limb scans extend only up to

Relative

In this work, we employ two models of the

The second model uses results from the multilinear regression analysis
from the measurement comparison study of

At 92 km and below, the prior is set to zero with a smooth
transition between 100 and 92 km. For the smooth transition we apply the
standard analysis approach for the partition of unity. First, we define the
smooth function

As described in

As in the MLT retrieval described in

Median of the

Accordingly, using the MLT retrieval algorithm without a priori input, the
retrieved

In contrast, the lower-left panel of Fig.

The retrieved

SCIAMACHY's MLT mode and nominal mode were carried out on different days. To
estimate the error introduced by only sampling up to 91 km instead of
150 km, we simulate the nominal mode on MLT days by restricting the limb
scan data from the MLT mode to 91 km before performing the retrieval. We
then compare the resulting number densities over the full altitude range.
However, when evaluating the results the main focus should be on altitudes
below 91 km. This comparison begins with the results without a priori input
and then evaluates the impact of the different prior choices below in
Sect.

The restricted slant column densities are shown in the left panel of
Fig.

Figure

Median of the

Altitude averaging kernel matrix elements (left panel) for a sample
orbit (no. 41 467, 3 February 2010) at a particular latitude grid point
(71.25

As seen in Fig.

Including these prior inputs, the retrieved

Figure

Figure

The zonal median number densities retrieved from the full MLT scans as well
as the restricted MLT scans with and without a priori information above
90 km are shown in the top panel of Fig.

Altitude averaging kernel matrix elements (first panel from the left)
and the respective full widths at half maximum (second panel) at the 71.25

Differences of the

The median of the absolute differences of the number densities of restricted
MLT scans to the results from the full MLT retrieval are shown in the middle
panel of Fig.

In the following we focus on the differences between 60 and 91 km. Without a
priori input (light blue line in Fig.

Using a priori inputs (dark orange and dark purple lines in
Fig.

Using the regression model as a priori input (dark purple line),
these differences are smaller.
At high latitudes, the largest difference is

Since using the a priori values as they are seems to over-correct the missing data
above 100 km, we extended the retrieval algorithm to include an
additional scale factor

The median of the number densities of the restricted MLT scans using the scaled
a priori values compared to the number densities from the
full MLT retrieval are shown in the left panel of Fig.

At high northern latitudes, fitting the values of either model as a priori
results in number density differences of less than 5 % below 85 km, and
less than 10 % above. Fitting the NOEM model at middle and low latitudes
results in differences of less than 3 % across the altitude range of
interest. Using the fitted regression model results in a difference of less
than 10 % up to around 80 km and up to 15 % larger number densities
above. At high southern latitudes, the differences using the fitted NOEM
model are the largest, up to

The vertical resolution using the SCIAMACHY MLT scans was extensively
discussed in

Figure

For comparison, the third and fourth panels of
Fig.

In terms of resolution, the above orbits are typical examples.
For all retrievals the averaging kernels are similar in all regions where
measurements are available, i.e. between

We present an algorithm to retrieve

The altitude range of plausible results from the SCIAMACHY nominal limb scans
extends from 60 to about 85 km. The missing measurements above about 91 km
manifest in larger

The SCIAMACHY nominal limb scans provide almost 10 years of continuous daily
spectra of the middle atmosphere (up to

Ten years of daily measurements make it possible to investigate

The SCIAMACHY/Envisat Level 1b spectra were downloaded via the
official ESA data browser:

Downloading requires applying for data access at ESA.
The calibration was carried out with the ESA tool

Stefan Bender and Miriam Sinnhuber thank the Helmholtz-society for funding this project under the grant number VH-NG-624. The SCIAMACHY project was a national contribution to the ESA Envisat, funded by German Aerospace (DLR), the Dutch Space Agency, SNO, and the Belgium ministry. The University of Bremen as Principal Investigator has led the scientific support and development of SCIAMACHY and the scientific exploitation of its data products. This study is also relevant to ESA studies such as MesosphEO. We acknowledge support by Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of Karlsruhe Institute of Technology. The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association. Edited by: M. Rapp Reviewed by: P. Espy and one anonymous referee