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.

To date, a large variety of water vapour data records from satellite and reanalysis are available. It is key to understand the quality and uncertainty of these data records in order to fully exploit these records and to avoid data being employed incorrectly or misinterpreted. Therefore, it is important to inform users on accuracy and limitations of these data records based on consistent inter-comparisons carried out in the framework of international assessments. Addressing this challenge is the major objective of the Global Water and Energy Exchanges (GEWEX) water vapor assessment (G-VAP) which was initiated by the GEWEX Data and Assessments Panel (GDAP). Here, an overview of G-VAP objectives and an introduction to the results from G-VAP’s first phase are given. After this overview, a summary of available data records on water vapour and closely related variables and a short introduction to the utilized methods are presented. The results from inter-comparisons, homogeneity testing and inter-comparison of trend estimates, achieved within G-VAP’s first phase are summarized. The conclusions on future research directions for the wider community and for G-VAP’s next phase are outlined and recommendations have been formulated. For instance, a key recommendation is the need for recalibration and improved inter-calibration of radiance data records and subsequent reprocessing in order to increase stability and to provide uncertainty estimates. This need became evident from a general disagreement in trend estimates (e.g., trends in TCWV ranging from −1.51 ± 0.17 kg/m²/decade to 1.22 ± 0.16 kg/m²/decade) and the presence of break points on global and regional scale. It will be a future activity of G-VAP to reassess the stability of updated or new data records and to assess consistency, i.e., the closeness of data records given their uncertainty estimates.

The Infrared Atmospheric Sounding Interferometers (IASIs) are three instruments flying on board the Metop satellites, launched in 2006 (IASI-A), 2012 (IASI-B), and 2018 (IASI-C). They measure infrared radiance from the Earth and atmosphere system, from which the atmospheric composition and temperature can be retrieved using dedicated algorithms, forming the Level 2 (L2) product. The operational near real-time processing of IASI data is conducted by the EUropean organisation for the exploitation of METeorological SATellites (EUMETSAT). It has improved over time, but due to IASI’s large data flow, the whole dataset has not yet been reprocessed backwards. A necessary step that must be completed before initiating this reprocessing is to uniformize the IASI radiance record (Level 1C), which has also changed with time due to various instrumental and software modifications. In 2019, EUMETSAT released a reprocessed IASI-A 2007–2017 radiance dataset that is consistent with both the L1C product generated after 2017 and with IASI-B. First, this study aimed to assess the changes in radiance associated with this update by comparing the operational and reprocessed datasets. The differences in the brightness temperature ranged from 0.02 K at 700 cm⁻¹ to 0.1 K at 2200 cm⁻¹. Additionally, two major updates in 2010 and 2013 were seen to have the largest impact. Then, we investigated the effects on the retrieved temperatures due to successive upgrades to the Level 2 processing chain. We compared IASI L2 with ERA5 reanalysis temperatures. We found differences of ~5–10 K at the surface and between 1 and 5 K in the atmosphere. These differences decreased abruptly after the release of the IASI L2 processor version 6 in 2014. These results suggest that it is not recommended to use the IASI inhomogeneous temperature products for trend analysis, both for temperature and trace gas trends.

The space and time variabilities of methane (CH4) total column and upper tropospheric mixing ratios are analysed above the Mediterranean Basin (MB) as part of the Chemical and Aerosol Mediterranean Experiment (ChArMEx) programme. Since the analysis of the mid-to-upper tropospheric CH4 distribution from spaceborne sensors and model outputs is challenging, we have adopted a climatological approach and have used a wide variety of data sets. We have combined spaceborne measurements from the Thermal And Near infrared Sensor for carbon Observations – Fourier Transform Spectrometer (TANSO-FTS) instrument on the Greenhouse gases Observing SATellite (GOSAT) satellite, the Atmospheric InfraRed Spectrometer (AIRS) on the AURA platform and the Infrared Atmospheric Sounder Interferometer (IASI) instrument aboard the MetOp-A platform with model results from the Chemical Transport Model (CTM) MOCAGE, and the Chemical Climate Models (CCMs) CNRM-AOCCM and LMDz-OR-INCA (according to different emission scenarios). In order to minimize systematic errors in the spaceborne measurements, we have only considered maritime pixels over the MB. The period of interest spans from 2008 to 2011 considering satellite and MOCAGE data and, regarding the CCMs, from 2001 to 2010. Although CH4 is a long-lived tracer with lifetime of ~12 years and is supposed to be well mixed in the troposphere, an east–west gradient in CH4 is observed and modelled in the mid-to-upper troposphere with a maximum in the Western MB in all seasons except in summer when CH4 accumulates above the Eastern MB. The peak-to-peak amplitude of the east–west seasonal variation in CH4 above the MB in the upper troposphere (300 hPa) is weak but almost twice as great in the satellite measurements (~25 ppbv) as in the model data (~15 ppbv). The maximum of CH4 in summer above the eastern MB can be explained by a series of dynamical processes only occurring in summer. The Asian monsoon traps and uplifts high amounts of CH4 to the upper troposphere where they build up. The Asian Monsoon Anticyclone redistributes these elevated CH4 amounts towards North Africa and the Middle East to finally reach and descend in the eastern MB. In the lower troposphere, the CH4 variability is mainly driven by the local sources of emission in the vicinity of the MB.

This paper presents the tools and methodology for performing a routine comprehensive monitoring of consistency and quality of IASI (Infrared Atmospheric Sounding Interferometer) trace gas Level 2 (L2) products (O₃, CO, N₂O, CH₄, and CO₂) generated at EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) using ground-based observations at the Izaña Atmospheric Observatory (IZO, Tenerife). As a demonstration the period 2010–2014 was analysed, covering the version 5 of the IASI L2 processor. Firstly, we assess the consistency between the total column (TC) observations from the IASI sensors on board the EUMETSAT Metop-A and Metop-B meteorological satellites (IASI-A and IASI-B respectively) in the subtropical North Atlantic region during the first 2 years of IASI-B operations (2012–2014). By analysing different timescales, we probe the daily and annual consistency of the variability observed by IASI-A and IASI-B and thereby assess the suitability of IASI-B for continuation of the IASI-A time series. The continuous intercomparison of both IASI sensors also offers important diagnostics for identifying inconsistencies between the data records and for documenting their temporal stability. Once the consistency of IASI sensors is documented we estimate the overall accuracy of all the IASI trace gas TC products by comparing to coincident ground-based Fourier transform infrared spectrometer (FTS) measurements performed at IZO from 2010 to 2014. The IASI L2 products reproduce the ground-based FTS observations well at the longest temporal scales, i.e. annual cycles and long-term trends for all the trace gases considered (Pearson correlation coefficient, R, larger than 0.95 and 0.75 for long-term trends and annual cycles respectively) with the exception of CO₂. For CO₂ acceptable agreement is only achieved for long-term trends (R ∼ 0.70). The differences observed between IASI and FTS observations can be in part attributed to the different vertical sensitivities of the two remote sensing instruments and also to the degree of maturity of the IASI products: O₃ and CO are pre-operational, while N₂O, CH₄, and CO₂ are, for the period covered by this study, aspirational products only and are not considered mature. Regarding shorter timescales (single or daily measurements), only the O₃ product seems to show good sensitivity to actual atmospheric variations (R ∼ 0.80), while the CO product is only moderately sensitive (R ∼ 0.50). For the remainder of the trace gases, further improvements would be required to capture the day-to-day real atmospheric variability.

With more than 15 years of continuous and consistent measurements, the Infrared Atmospheric Sounding Interferometer (IASI) radiance dataset is becoming a reference climate data record. To be exploited to its full potential, it requires a cloud filter that is accurate, unbiased over the full IASI life span and strict enough to be used in satellite data retrieval schemes. Here, we present a new cloud detection algorithm which combines (1) a high sensitivity, (2) a good consistency over the whole IASI time series and between the different copies of the instrument flying on board the suite of Metop satellites, and (3) simplicity in its parametrization. The method is based on a supervised neural network (NN) and relies, as input parameters, on the IASI radiance measurements only. The robustness of the cloud mask over time is ensured in particular by avoiding the IASI channels that are influenced by CO₂, N₂O, CH₄, CFC-11 and CFC-12 absorption lines and those corresponding to the ν₂ H₂O absorption band. As a reference dataset for the training, version 6.5 of the operational IASI Level 2 (L2) cloud product is used. We provide different illustrations of the NN cloud product, including comparisons with other existing products. We find very good agreement overall with version 6.5 of the operational IASI L2 with an identical mean annual cloud amount and a pixel-by-pixel correspondence of about 87 %. The comparison with the other cloud products shows a good correspondence in the main cloud regimes but with sometimes large differences in the mean cloud amount (up to 10 %) due to the specificities of each of the different products. We also show the good capability of the NN product to differentiate clouds from dust plumes.