The vertical distribution of ozone plays an important role in atmospheric chemistry, climate change, air pollution, and human health. Over the 21st century, spaceborne remote-sensing methods and instrumentation have evolved to better determine this distribution. We quantify the ability of ozone retrievals to characterize this distribution through a sequential combination of thermal infrared (TIR) and ultraviolet (UV) spectral radiances, harnessing co-located TIR measurements from the Cross-track Infrared Sounder (CrIS) on board the Suomi National Polar-orbiting Partnership (NPP) and UV measurements from the TROPOspheric Monitoring Instrument (TROPOMI), which is on the Sentinel 5-Precursor (S5P) satellite. Using the MUlti-SpEctra, MUlti-SpEcies, MUlti-SEnsors (MUSES) algorithm, the sequential combination of TIR and UV measurements, which follows retrievals from each instrument separately, moderately improves the ability of satellites to characterize global ozone profiles over the use of each instrument/band individually. The CrIS retrievals enhanced by TROPOMI radiances in the Huggins band (325–335 nm) show good agreement with independent datasets both in the troposphere and in the stratosphere in spite of calibration issues in the TROPOMI UV. Improved performance is characterized in the stratosphere from CrIS-TROPOMI, firstly through a modest increase in the degrees of freedom for signal (DFS; often between 0.1–0.2) and secondly through comparisons with the Microwave Limb Sounder (MLS), where a global multi-month-long comparison shows a mean difference $\sim \times \mathrm{10}$ lower than either CrIS or TROPOMI individually and R² values 3 % higher. In the troposphere, CrIS-TROPOMI and CrIS show similar degrees of freedom for signal, with about 2 globally, but these are higher in the tropics partitioned equally between the lower and upper troposphere. CrIS-TROPOMI validation with ozonesondes shows improved performance over CrIS-only validation, with a difference in the tropospheric-column bias of between 30 % and 200 % depending on the season. Cross-comparisons with satellite instruments and reanalysis datasets show similar performances in terms of correlations and biases. These results demonstrate that CrIS and CrIS-TROPOMI retrievals have the potential to improve global satellite ozone retrievals, especially with future developments. If spectral accuracy is improved in future TROPOMI calibration, the degrees of freedom for signal in the stratosphere could double when using bands 1 and 2 of TROPOMI (270–330 nm), while tropospheric degrees of freedom for signal could increase by 25 %.

The next and current generations of methane-retrieving satellite instruments are reliant on the Total Carbon Column Observing Network (TCCON) for validation. Understanding the biases inherent in TCCON and satellite methane retrievals is as important now as when TCCON started in 2004. In this study we highlight possible biases between different methane products by assessing the retrievals of the main methane isotopologue ¹²CH₄. Using the TCCON GGG2014 retrieval environment, retrievals are performed using five separate spectroscopic databases from four separate TCCON sites (namely, Ascension Island, Ny-Ålesund, Darwin and Tsukuba) over the course of a year. The spectroscopic databases include those native to TCCON, GGG2014 and GGG2020; the high-resolution transmission molecular absorption database 2016 (HITRAN2016); the Gestion et Etude des Informations Spectroscopiques Atmosphériques 2020 (GEISA2020) database; and the ESA Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases (SEOM-IAS). We assess the biases in retrieving methane using the standard TCCON windows and the methane window used by the Sentinel-5 Precursor (S5P) TROPOspheric Ozone Monitoring Instrument (TROPOMI) for each of the different spectroscopic databases. By assessing the retrieved ¹²CH₄ values from individual windows against the standard TCCON retrievals, we find bias values of between 0.05 and 2.5 times the retrieval noise limit. These values vary depending on the window and TCCON site, with Ascension Island showing the lowest biases (typically <0.5) and Ny-Ålesund or Tsukuba showing the largest. For the spectroscopic databases, GEISA2020 shows the largest biases, often greater than 1.5 across the TCCON sites and considered windows. The TROPOMI spectral window (4190–4340 cm⁻¹) shows the largest biases of all the spectral windows, typically >1, for all spectroscopic databases, suggesting that further improvements in spectroscopic parameters are necessary. We further assess the sensitivity of these biases to locally changing atmospheric conditions such as the solar zenith angle (SZA), water vapour and temperature. We find evidence of significant non-linear relationships between the variation in local conditions and the retrieval biases based on regression analysis. In general, each site, database and window combination indicates different degrees of sensitivity, with GEISA2020 often showing the most sensitivity for all TCCON sites. Ny-Ålesund and Tsukuba show the most sensitivity to variations in local condition, while Ascension Island indicates limited sensitivity. Finally, we investigate the biases associated with retrieving ¹³CH₄ from each TCCON site and spectroscopic database, through the calculation of δ¹³C values. We find high levels of inconsistency, in some cases >100 ‰ between databases, suggesting more work is required to refine the spectroscopic parameters of ¹³CH₄.

Inversion of radiative transfer models (RTMs) is key to interpreting satellite observations of air quality and greenhouse gases, but is computationally expensive. Surrogate models that emulate the full forward physical RTM can speed up the simulation, reducing computational and timing costs and allowing the use of more advanced physics for trace gas retrievals. In this study, we present the development of surrogate models for two RTMs: the RemoTeC algorithm using the LINTRAN RTM and the SCIATRAN RTM. We estimate the intrinsic dimensionality of the input and output spaces and embed them in lower dimensional subspaces to facilitate the learning task. Two methods are tested for dimensionality reduction, autoencoders and principle component analysis (PCA), with PCA consistently outperforming autoencoders. Different sampling methods are employed for generating the training datasets: sampling focused on expected atmospheric parameters and latin hypercube sampling. The results show that models trained on the smaller (n = 1000) uniformly sampled dataset can perform as well as those trained on the larger (n = 50000), more focused dataset. Surrogate models for both datasets are able to accurately emulate Sentinel 5P spectra within a millisecond or less, as compared to the minutes or hours needed to simulate the full physical model. The SCIATRAN-trained forward surrogate models are able to generalize the emulation to a broader set of parameters and can be used for less constrained applications, while achieving a normalized RMSE of 7.3%. On the other hand, models trained on the LINTRAN dataset can completely replace the RTM simulation in more focused expected ranges of atmospheric parameters, as they achieve a normalized RMSE of 0.3%.

This paper presents an intercomparison between existing tropospheric ozone column (TrOC) datasets obtained using combined limb and nadir observations, i.e., exploiting collocated stratospheric profile and total column information retrieved from limb and nadir satellite observations, respectively. In particular, seven datasets have been considered, covering the past 2 decades and consisting of monthly-averaged time series with nearly global coverage. We perform a comparison in terms of climatology and seasonality, investigate the tropopause height used for the construction of each dataset and the related biases, and finally discuss long-term TrOC drifts and trends. The overall goal of the study is to assess the consistency between the datasets and explore possible strategies to reconcile the differences between them. Despite uncertainties associated with the limb–nadir residual methodology and large biases between the mean values of the considered datasets, we identify an overall agreement of TrOC distribution patterns. The different tropopause height definitions used to construct the datasets did not show a relevant role in explaining the biases between them. We demonstrate that a thorough investigation of the drifts with respect to ground-based observations is needed to evaluate TrOC trends from satellite data and that long-term trends in specific regions can be consistently detected, e.g., a positive trend of up to 1.5 DU per decade over China for the 2005–2021 period.

Retrievals of methane isotopologues have the potential to differentiate between natural and anthropogenic methane sources types, which can provide much needed information about the current global methane budget. We investigate the feasibility of retrieving the second most abundant isotopologue of atmospheric methane (¹³ CH₄, roughly 1.1 % of total atmospheric methane) from the shortwave infrared (SWIR) channels of the future Sentinel-5/ultra-violet, visible, near-infrared, shortwave infrared (UVNS) and current Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) instruments. With the intended goal of calculating the δ¹³C value, we assume that a δ¹³C uncertainty of better than 1 ‰ is sufficient to differentiate between source types, which corresponds to a ¹³ CH₄ uncertainty of <0.02 ppb. Using the well-established information content analysis techniques and assuming clear-sky, non-scattering conditions, we find that the SWIR3 (2305–2385 nm) channel on the TROPOMI instrument can achieve a mean uncertainty of <1 ppb, while the SWIR1 channel (1590–1675 nm) on the Sentinel-5 UVNS instrument can achieve <0.68 ppb or <0.2 ppb in high signal-to-noise ratio (SNR) cases. These uncertainties combined with significant spatial and/or temporal averaging techniques can reduce δ¹³C uncertainty to the target magnitude or better. However, we find that ¹³ CH₄ retrievals are highly sensitive to errors in a priori knowledge of temperature and pressure, and accurate knowledge of these profiles is required before ¹³ CH₄ retrievals can be performed on TROPOMI and future Sentinel-5/UVNS data. In addition, we assess the assumption that scattering-induced light path errors are cancelled out by comparing the δ¹³C values calculated for non-scattering and scattering scenarios. We find that there is a minor bias in δ¹³C values from scattering and non-scattering retrievals, but this is unrelated to scattering-induced errors.

Atmospheric methane is comprised of multiple isotopic molecules, with the most abundant being ¹² CH ₄ and ¹³ CH ₄ , making up 98 and 1.1 % of atmospheric methane respectively. It has been shown that is it possible to distinguish between sources of methane (biogenic methane, e.g. marshland, or abiogenic methane, e.g. fracking) via a ratio of these main methane isotopologues, otherwise known as the δ ¹³ C value. δ ¹³ C values typically range between −10 and −80 ‰, with abiogenic sources closer to zero and biogenic sources showing more negative values. Initially, we suggest that a δ ¹³ C difference of 10 ‰ is sufficient, in order to differentiate between methane source types, based on this we derive that a precision of 0.2 ppbv on ¹³ CH ₄ retrievals may achieve the target δ ¹³ C variance. Using an application of the well-established information content analysis (ICA) technique for assumed clear-sky conditions, this paper shows that using a combination of the shortwave infrared (SWIR) bands on the planned Greenhouse gases Observing SATellite (GOSAT-2) mission, ¹³ CH ₄ can be measured with sufficient information content to a precision of between 0.7 and 1.2 ppbv from a single sounding (assuming a total column average value of 19.14 ppbv), which can then be reduced to the target precision through spatial and temporal averaging techniques. We therefore suggest that GOSAT-2 can be used to differentiate between methane source types. We find that large unconstrained covariance matrices are required in order to achieve sufficient information content, while the solar zenith angle has limited impact on the information content.