The MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instrument has been operating on-board the ENVISAT satellite since March 2002. In the first two years, it acquired in a nearly continuous manner high resolution (0.025 cm⁻¹ unapodized) emission spectra of the Earth's atmosphere at limb in the middle infrared region. This paper describes the level 2 near real-time (NRT) and off-line (OL) ESA processors that have been used to derive level 2 geophysical products from the calibrated and geolocated level 1b spectra. The design of the code and the analysis methodology have been driven by the requirements for NRT processing. This paper reviews the performance of the optimized retrieval strategy that has been implemented to achieve these requirements and provides estimated error budgets for the target products: pressure, temperature, O₃, H₂O, CH₄, HNO₃, N₂O and NO₂, in the altitude measurement range from 6 to 68 km. From application to real MIPAS data, it was found that no change was needed in the developed code although an external algorithm was introduced to identify clouds with high opacity and to exclude affected spectra from the analysis. In addition, a number of updates were made to the set-up parameters and to auxiliary data. In particular, a new version of the MIPAS dedicated spectroscopic database was used and, in the OL analysis, the retrieval range was extended to reduce errors due to uncertainties in extrapolation of the profile outside the retrieval range and more stringent convergence criteria were implemented. A statistical analysis on the χ² values obtained in one year of measurements shows good agreement with the a priori estimate of the forward model errors. On the basis of the first two years of MIPAS measurements the estimates of the forward model and instrument errors are in general found to be conservative with excellent performance demonstrated for frequency calibration. It is noted that the total retrieval error is limited by forward model errors which make effectless a further reduction of random errors. However, such a reduction is within the capabilities of MIPAS measurements, which contain many more spectral signatures of the target species than what has currently been used. Further work is needed to reduce the amplitude of the forward model errors, so that the random error and the total error budget can be reduced accordingly. The importance of the Averaging kernels for a full characterization of the target products is underlined and the equations are provided for their practical applications.

Peak stratospheric chlorofluorocarbon (CFC) and other ozone depleting substance (ODS) concentrations were reached in the mid- to late 1990s. Detection and attribution of the expected recovery of the stratospheric ozone layer in an atmosphere with reduced ODSs as well as efforts to understand the evolution of stratospheric ozone in the presence of increasing greenhouse gases are key current research topics. These require a critical examination of the ozone changes with an accurate knowledge of the spatial (geographical and vertical) and temporal ozone response. For such an examination, it is vital that the quality of the measurements used be as high as possible and measurement uncertainties well quantified. In preparation for the 2014 United Nations Environment Programme (UNEP)/World Meteorological Organization (WMO) Scientific Assessment of Ozone Depletion, the SPARC/IO₃C/IGACO-O₃/NDACC (SI²N) Initiative was designed to study and document changes in the global ozone profile distribution. This requires assessing long-term ozone profile data sets in regards to measurement stability and uncertainty characteristics. The ultimate goal is to establish suitability for estimating long-term ozone trends to contribute to ozone recovery studies. Some of the data sets have been improved as part of this initiative with updated versions now available. This summary presents an overview of stratospheric ozone profile measurement data sets (ground and satellite based) available for ozone recovery studies. Here we document measurement techniques, spatial and temporal coverage, vertical resolution, native units and measurement uncertainties. In addition, the latest data versions are briefly described (including data version updates as well as detailing multiple retrievals when available for a given satellite instrument). Archive location information for each data set is also given.

The MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instrument on the Envisat (Environmental satellite) satellite has provided vertical profiles of the atmospheric composition on a global scale for almost ten years. The MIPAS mission is divided in two phases: the full resolution phase, from 2002 to 2004, and the optimized resolution phase, from 2005 to 2012, which is characterized by a finer vertical and horizontal sampling attained through a reduction of the spectral resolution. While the description and characterization of the products of the ESA processor for the full resolution phase has been already described in previous papers, in this paper we focus on the performances of the latest version of the ESA (European Space Agency) processor, named ML2PP V6 (MIPAS Level 2 Prototype Processor), which has been used for reprocessing the entire mission. The ESA processor had to perform the operational near real time analysis of the observations and its products needed to be available for data assimilation. Therefore, it has been designed for fast, continuous and automated analysis of observations made in quite different atmospheric conditions and for a minimum use of external constraints in order to avoid biases in the products. The dense vertical sampling of the measurements adopted in the second phase of the MIPAS mission resulted in sampling intervals finer than the instantaneous field of view of the instrument. Together with the choice of a retrieval grid aligned with the vertical sampling of the measurements, this made ill-conditioned the retrieval problem of the MIPAS operational processor. This problem has been handled with minimal changes to the original retrieval approach but with significant improvements nonetheless. The Levenberg–Marquardt method, already present in the retrieval scheme for its capability to provide fast convergence for nonlinear problems, is now also exploited for the reduction of the ill-conditioning of the inversion. An expression specifically designed for the regularizing Levenberg–Marquardt method has been implemented for the computation of the covariance matrices and averaging kernels of the retrieved products. The regularization of the Levenberg–Marquardt method is controlled by the convergence criteria and is deliberately kept weak. The resulting oscillations of the retrieved profile are a posteriori damped by an innovative self-adapting Tikhonov regularization. The convergence criteria and the weakness of the self-adapting regularization ensure that minimum constraints are used and the best vertical resolution obtainable from the measurements is achieved in all atmospheric conditions. Random and systematic errors, as well as vertical and horizontal resolution are compared in the two phases of the mission for all products, namely: temperature, H_2O, O₃, HNO₃, CH₄, N_2O, NO₂, CFC-11, CFC-12, N₂O₅ and ClONO₂. The use in the two phases of the mission of different optimized sets of spectral intervals ensures that, despite the different spectral resolutions, comparable performances are obtained in the whole MIPAS mission in terms of random and systematic errors, while the vertical resolution and the horizontal resolution are significantly better in the case of the optimized resolution measurements.

The ACE-FTS (Atmospheric Chemistry Experiment – Fourier Transform Spectrometer) instrument on the Canadian SCISAT satellite, which has been in operation for over 12 years, has the capability of deriving stratospheric profiles of many of the NO_y (N + NO + NO₂+ NO₃+ 2 × N₂O₅+ HNO₃+ HNO₄+ ClONO₂+ BrONO₂) species. Version 2.2 of ACE-FTS NO, NO₂, HNO₃, N₂O₅, and ClONO₂ has previously been validated, and this study compares the most recent version (v3.5) of these five ACE-FTS products to spatially and temporally coincident measurements from other satellite instruments – GOMOS, HALOE, MAESTRO, MIPAS, MLS, OSIRIS, POAM III, SAGE III, SCIAMACHY, SMILES, and SMR. For each ACE-FTS measurement, a photochemical box model was used to simulate the diurnal variations of the NO_y species and the ACE-FTS measurements were scaled to the local times of the coincident measurements. The comparisons for all five species show good agreement with correlative satellite measurements. For NO in the altitude range of 25–50 km, ACE-FTS typically agrees with correlative data to within −10 %. Instrument-averaged mean relative differences are approximately −10 % at 30–40 km for NO₂, within ±7 % at 8–30 km for HNO₃, better than −7 % at 21–34 km for local morning N₂O₅, and better than −8 % at 21–34 km for ClONO₂. Where possible, the variations in the mean differences due to changes in the comparison local time and latitude are also discussed.

Nineteen limb-viewing data sets (occultation, passive thermal, and UV scattering) and two nadir upper tropospheric humidity (UTH) data sets are intercompared and also compared to frost-point hygrometer balloon sondes. The upper troposphere considered here covers the pressure range from 300–100 hPa. UTH is a challenging measurement, because concentrations vary between 2–1000 ppmv (parts per million by volume), with sharp changes in vertical gradients near the tropopause. Cloudiness in this region also makes the measurement challenging. The atmospheric temperature is also highly variable ranging from 180–250 K. The assessment of satellite-measured UTH is based on coincident comparisons with balloon frost-point hygrometer sondes, multi-month mapped comparisons, zonal mean time series comparisons, and coincident satellite-to-satellite comparisons. While the satellite fields show similar features in maps and time series, quantitatively they can differ by a factor of 2 in concentration, with strong dependencies on the amount of UTH. Additionally, time-lag response-corrected Vaisala RS92 radiosondes are compared to satellites and the frost-point hygrometer measurements. In summary, most satellite data sets reviewed here show on average ∼30 % agreement amongst themselves and frost-point data but with an additional ∼30 % variability about the mean bias. The Vaisala RS92 sonde, even with a time-lag correction, shows poor behavior for pressures less than 200 hPa.

High quality long-term data sets of altitude-resolved measurements of the atmospheric composition are important because they can be used both to study the evolution of the atmosphere and as a benchmark for future missions. For the final ESA reprocessing of MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) on ENVISAT (ENViromental SATellite) data, numerous improvements were implemented in the Level 2 (L2) processor Optimised Retrieval Model (ORM) version 8.22 (V8) and its auxiliary data. The implemented changes involve all aspects of the processing chain, from the modelling of the measurements with the handling of the horizontal inhomogeneities along the line of sight to the use of the optimal estimation technique to retrieve the minor species, from a more sensitive approach to detecting the spectra affected by clouds to a refined method for identifying low quality products. Improvements in the modelling of the measurements were also obtained with an update of the used spectroscopic data and of the databases providing the a priori knowledge of the atmosphere. The HITRAN_mipas_pf4.45 spectroscopic database was finalised with new spectroscopic data verified with MIPAS measurements themselves, while recently measured cross-sections were used for the heavy molecules. The Level 2 Initial Guess (IG2) data set, containing the climatology used by the MIPAS L2 processor to generate the initial guess and interfering species profiles when the retrieved profiles from previous scans are not available, was improved taking into account the diurnal variation of the profiles defined using climatologies from both measurements and models. Horizontal gradients were generated using the ECMWF ERA-Interim data closest in time and space to the MIPAS data. Further improvements in the L2 V8 products derived from the use of the L1b V8 products, which were upgraded to reduce the instrumental temporal drift and to handle the abrupt changes in the calibration gain. The improvements introduced into the ORM V8 L2 processor and its upgraded auxiliary data, together with the use of the L1b V8 products, lead to the generation of the MIPAS L2 V8 products, which are characterised by an increased accuracy, better temporal stability and a greater number of retrieved species.