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Long-term, 21st century ground-based ozone measurements are crucial to study the recovery of stratospheric ozone as well as the trends of tropospheric ozone. This study is performed in the context of the LOTUS (Long-term Ozone Trends and Uncertainties in the Stratosphere) and TOAR-II (Tropospheric Ozone Assessment Report, phase II) initiatives. Within LOTUS, we want to know why different trends have been observed by different ground-based measurements at Lauder. In TOAR-II, intercomparison studies among the different ground-based datasets are needed to evaluate their quality and relevance for trend studies. To achieve these goals, we perform an intercomparison study of total column ozone and its vertical distribution among the ground-based measurement instruments available at the Lauder station from 2000 to 2022, which are a Fourier transform infrared (FTIR) spectrometer, a Dobson spectrophotometer, a UV2 (ultraviolet double monochromator), a microwave radiometer (MWR), ozonesondes, and a stratospheric lidar. Because only the latter two provide high-vertical-resolution profiles, the vertical ozone distribution is validated using partial columns, defined to provide independent information: one tropospheric column and three stratospheric columns. Because FTIR provides total columns and vertical information covering all partial columns as well as high temporal sampling, the intercomparisons (bias, scatter, and drift) are analyzed using FTIR as the reference. Very good agreement between the FTIR and Dobson (FTIR and UV2) total column ozone records is apparent in the high Pearson correlation of 0.97 (0.93), low biases of - 3 % (- 2 %), and 2 % (3 %) dispersions, which are within the respective systematic and random uncertainties. The small observed drifts 0.4 % (0.3 %) per decade are "non-significant" (or rather a low certainty in a 95 % confidence interval) and show good stability of the three ozone total column series at Lauder. In the troposphere we find a small bias of - 1.9 % with the ozonesondes but a larger one (+ 10.7 %) with Umkehr, which can be explained by the low degrees of freedom for signal (0.5) of Umkehr in the troposphere. However, no significant drift is found among the three instruments in the troposphere, which proves their relevance for trend studies within TOAR-II. The negative bias observed in total columns is confirmed by negative biases in all stratospheric columns for all instruments with respect to FTIR (between - 1.2 % and - 6.8 %). This, confirmed by the total column biases, points to a 2 %–3 % underestimation of the infrared spectroscopic line intensities. Nevertheless, the dispersion between FTIR and all techniques is typically within 5 % for the stratospheric partial columns, in close agreement with the given random uncertainty budgets. We observe no significant drift in the stratosphere between ozonesondes and FTIR for all partial columns, with ozonesonde trends being less negative than in LOTUS further referred to as the LOTUS22. The only significant drift in the lower-stratospheric columns is obtained between FTIR and Umkehr, as was already found in LOTUS22. Two significant positive drifts are observed in the middle stratosphere (2 % and 3 % per decade) with lidar and MWR, respectively, while two significant negative drifts are observed in the upper stratosphere (- 3 % and - 4 % per decade) with Umkehr and lidar, respectively. While remaining drifts are still present, our study explains roughly half of the differences in observed trends in LOTUS22 by the different sampling, vertical sensitivity, or time periods and gaps. In addition, the FTIR data in the present work have been improved since LOTUS22, reducing the differences in the upper-stratospheric and tropospheric trends. This shows the necessity for continuous review and improvement of the measurement and retrieval processes. This study also reflects the importance of super sites such as Lauder for cross-validating the long-term ozone measurements. Our study demonstrated that well-harmonized, optimized, well-characterized instruments that show very good agreement in terms of bias, dispersion, and correlation are capable of detecting trends that agree within their respective measurement uncertainties. [ABSTRACT FROM AUTHOR]

Accurate observations of atmospheric ozone (O3) are essential to monitor in detail the key role of O3 in the atmospheric chemistry. The present paper examines the performance of different O3 retrieval strategies from FTIR (Fourier Transform InfraRed) spectrometry by using the 20-year time series of the high-resolution solar spectra acquired from 1999 to 2018 at the subtropical Izaña Observatory (IZO, Spain) within NDACC (Network for the Detection of Atmospheric Composition Change). In particular, the effect of two of the most influential factors have been investigated: the spectral region used for O3 retrievals and inclusion of an atmospheric temperature profile fit. The theoretical and experimental quality assessments of the different FTIR O3 products (total column, TC, amounts and volume mixing ratio, VMR, profiles) provide consistent results. Combining an optimal selection of spectral O3 absorption lines and a simultaneous temperature retrieval results in superior FTIR O3 products, with a precision greater than 0.6-0.7% for O3 TCs as compared to coincident NDACC Brewer observations used as reference. However, this improvement can be only achieved provided the FTIR spectrometer is properly characterised and stable over time. For unstable instruments, the temperature fit has been found to exhibit a strong negative influence on O3 retrievals by increasing the cross-interference between instrumental performance and temperature retrieval. This cross-interference becomes especially noticeable beyond the upper troposphere/lower stratosphere as documented theoretically, as well as experimentally by comparing FTIR O3 profiles to those measured using Electrochemical Concentration Cell (ECC) sondes within NDACC. Consequently, it should be taken into account for the reliable monitoring of O3 vertical distribution, especially on long-term timescales. [ABSTRACT FROM AUTHOR]

IASI (Infrared Atmospheric Sounding Interferometer) is the core instrument of the currently three Metop (Meteorological operational) satellites of EUMETSAT (European Organization for the Exploitation of Meteorological Satellites). The MUSICA IASI processing has been developed in the framework of the European Research Council project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water). The processor performs an optimal estimation of the vertical distributions of water vapour (H2O), the ratio between two water vapour isotopologues (the HDO / H2O ratio), nitrous oxide (N2O), methane (CH4), and nitric acid (HNO3), and works with IASI radiances measured under cloud-free conditions in the spectral window between 1190 and 1400 cm−1. The retrieval of the trace gas profiles is performed on a logarithmic scale, which allows the constraint and the analytic treatment of ln[HDO] - ln[H2O] as proxy for the HDO / H2O ratio. Currently, the MUSICA IASI processing has been applied to all IASI measurements available between October 2014 and April 2020, so more than 1.4 billion individual retrievals have been performed. Here we describe the MUSICA IASI full retrieval product data set. The data set is made available in form of netcdf data files that are compliant with version 1.7 of the CF (Climate and Forecast) metadata convention. For each orbit an individual standard output data file is provided. These files contain for each individual retrieval information on the a priori usage and constraint, the retrieved atmospheric trace gas and temperature profiles, profiles of the leading error components, information on vertical representativeness in form of the averaging kernels as well as averaging kernel metrics, which are more handy than the full kernels. We discuss data filtering options and give examples of the high horizontal and continuous temporal coverage of the MUSICA IASI data products. The standard output data files provide comprehensive information for each individual retrieval resulting in a rather large data volume (about 25 TB for the more than five years of data with global daily coverage). This at a first glance apparent drawback of large data files and data volume is counterbalanced by multiple possibilities of data reusability, which are briefly discussed. In an extended output data file the same variables as in the standard output data files are provided in addition to Jacobians for many different uncertainty sources and Gain matrices (due to this additional variables it is called the extended output). It is limited to 74 observations over a polar, mid-latitudinal and tropical site. We use this additional Jacobian and Gain data for assessing the typical impact of different uncertainty sources - like surface emissivity or spectroscopic parameters - and different cloud types on the retrieval results. We offer two data packages with DOI for free download via the repository RADAR4KIT. The first data package has a data volume of about 17.5 GB and is linked to https://doi.org/10.35097/408 (Schneider, et al., 2021b). It contains example standard output data files for all MUSICA IASI retrievals made for a single day (more than 0.6 million). Furthermore, it includes a ReadMe.pdf file with a description of how to access the total data set (the 25 TB) or parts of it. This data package is for users interested in the typical global daily data coverage and in information about how to download the large data volumes of global daily data for longer periods. The second data package is linked to https://doi.org/10.35097/412 (Schneider et al., 2021a) and contains the extended output data file. Because it provides data for only 74 example retrievals, its data volume is only 73 MB and it is thus recommended to users for having a quick look on the data. [ABSTRACT FROM AUTHOR]

The Sentinel-5 Precursor (S5P) mission with the TROPOspheric Monitoring Instrument (TROPOMI) onboard has been measuring solar radiation backscattered by the Earth's atmosphere and its surface since its launch on 13 October 2017. Methane (CH4) and carbon monoxide (CO) data with a spatial resolution (initially 7 x 7 km2, upgraded to 5.5 x 7 km2 on 6th of August 2019) have been retrieved from shortwave infrared (SWIR) and near-infrared (NIR) measurements since the end of November 2017 and made available to the experts for early validation and quality checks before the official product release. In this paper, we present for the first time the S5P CH4 and CO validation results (covering a period from November 2017 to September 2020) using global Total Carbon Column Observing Network (TCCON) and Infrared Working Group of the Network for the Detection of Atmospheric Composition Change (NDACC-IRWG) network data, accounting for a priori alignment and smoothing uncertainties in the validation, and testing the sensitivity of validation results towards the application of advanced co-location criteria.We found that the required bias (systematic error) of 1.5 % and random error of 1 % for the S5P standard and bias-corrected methane data are met for measurements over land surfaces with pixels having quality assurance (QA) value > 0.5. The systematic difference between the S5P standard XCH4 and TCCON data is on average -0.69 ± 0.73 %. The systematic difference changes to a value of -0.25 ± 0.57 % for the S5P bias-corrected XCH4 data. We found a correlation of above 0.6 for most stations, which is mostly dominated by the seasonal cycle. The contributions of smoothing uncertainty at the individual stations are estimated and found to be dependent on the location. The highest contribution of the smoothing uncertainty is observed for mid-latitude TCCON stations and high latitude stations for NDACC. A seasonal dependency of the relative bias is seen. We observe a high bias during the springtime measurements at high SZA and a decreasing bias with increasing SZA for the rest of the year.We found that the required bias (systematic error) of 15 % and random error of < 10 % for the S5P carbon monoxide data are met in general for measurements over all surfaces with pixels having quality assurance value of >0.5. There are a few stations where this is not the case, mostly due to co-location mismatches and the limited availability of co-located data. We compared the S5P XCO data with respect to standard TCCON XCO and unscaled TCCON XCO (without application of the empirical scaling factor) data sets. The systematic difference between the S5P XCO and the TCCON data is on average 9.14 ± 3.33 % (standard TCCON XCO data) and 2.36 ± 3.22 % (unscaled TCCON XCO data). We found that the systematic difference between the S5P CO column and NDACC CO column data (excluding two stations that were obvious outliers) is on average 6.44 ± 3.79 %. We found a correlation of above 0.9 for most TCCON and NDACC stations indicating that the temporal variations in CO column captured by the ground-based instruments are reproduced very similarly by the S5P CO column. The contribution of smoothing uncertainty at the individual stations is estimated and found to be significant. They are found to be dependent on the location with large changes seen for stations located in the Southern Hemisphere as compared to the Northern Hemisphere and at highly polluted stations. A cone co-location criterion, which gives a better match between the ground- based instrument's line-of-sight and satellite pixels, seems to give better results for high latitude stations and stations located close to emission sources. The validation results for the clear-sky and cloud cases of S5P pixels are comparable to the validation results including all pixels with quality assurance value of > 0.5. We observe that the relative bias increases with increasing SZA. We estimated this increase is about 10 % over the complete range of measurement SZAs.The study shows the high quality of S5P CH4 and CO data by validating the products against reference global TCCON and NDACC stations covering a wide range of latitudinal bands, atmospheric conditions, and surface conditions. [ABSTRACT FROM AUTHOR]

The thermal infrared nadir spectra of IASI (Infrared Atmospheric Sounding Interferometer) are successfully used for retrievals of different atmospheric trace gas profiles. However, these retrievals offer generally reduced information about the lowermost tropospheric layer due to the lack of thermal contrast close to the surface. Spectra of scattered solar radiation observed in the near and/or short wave infrared, for instance by TROPOMI (TROPOspheric Monitoring Instrument) offer higher sensitivity near ground and are used for the retrieval of total column averaged mixing ratios of a variety of atmospheric trace gases. Here we present a method for the synergetic use of IASI profile and TROPOMI total column data. Our method uses the output of the individual retrievals and consists of linear algebra a posteriori calculations (i.e. calculation after the individual retrievals). We show that this approach is largely equivalent to applying the spectra of the different sensors together in a single retrieval procedure, but with the substantial advantage of being applicable to data generated with different individual retrieval processors, of being very time efficient, and of directly benefiting from the high quality and most recent improvements of the individual retrieval processors. We demonstrate the method exemplarily for atmospheric methane (CH4). We perform a theoretical evaluation and show that the a posteriori combination method yields a total column averaged CH4 product (XCH4) that conserves the good sensitivity of the corresponding TROPOMI product while merging it with the upper tropospheric and lower stratospheric (UTLS) CH4 partial column information of the corresponding IASI product. As consequence, the combined product offers in addition sensitivity for the tropospheric CH4 partial column, which is not provided by the individual TROPOMI nor the individual IASI product. The theoretically predicted synergetic effects are verified by comparisons to CH4 reference data obtained from collocated XCH4 measurements at six globally distributed TCCON (Total Carbon Column Observing Network) stations, CH4 profile measurements made by 24 individual AirCore soundings, and lower tropospheric CH4 data derived from continuous ground-based in-situ observations made at two nearby Global Atmospheric Watch (GAW) mountain stations. The comparisons clearly demonstrate that the combined product can reliably detect XCH4 signals and allows to distinguish between tropospheric and UTLS CH4 partial column averaged mixing ratios, which is not possible by the individual TROPOMI and IASI products. We find indications of a weak positive bias of about +1% of the combined lower tropospheric data product with respect to the references. For the UTLS CH4 partial columns we find no significant bias. [ABSTRACT FROM AUTHOR]

Column-averaged dry-air mole fraction of CO (XCO) measurements are obtained from two ground-based Fourier transform infrared (FTIR) spectrometers networks: the Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC). In this study, the differences between the TCCON and NDACC XCO measurements are investigated and discussed based on six NDACC/TCCON sites using data over the period 2007-2017. The NDACC XCO measurements are about 5.5 % larger than the TCCON data at Ny-Ålesund, Bremen, and Izaña (Northern Hemisphere), and about 0.3 % larger than the TCCON data at St Denis, Wollongong and Lauder (Southern Hemisphere). The hemispheric dependence of the bias is mainly attributed to their smoothing errors. The systematic smoothing error of the TCCON XCO data varies in the range between 0.2 % (Bremen) and 7.9 % (Lauder), and the random smoothing error in the range between 2.0 % and 3.6 %. The systematic smoothing error of NDACC data is between 0.1 % and 0.8 %, and the random smoothing error of NDACC data is about 0.3 %. For TCCON data, the smoothing error can be significant in that it is much higher than the reported uncertainty for TCCON XCO. To reduce the influence from the a priori profiles and different vertical sensitivities, the scaled NDACC a priori profiles are used as the common a priori profiles for comparing TCCON and NDACC retrievals. As a result, the biases between TCCON and NDACC XCO measurements become more consistent (5.6-8.5 %) with a mean value of 6.8 % at these sites. To understand the remaining bias, regular AirCore measurements at Orleans and Sodankylä are compared to co-located TCCON measurements. It is found that TCCON XCO measurements are 6.0 ± 1.9 % and 6.9 ± 2.5 % smaller than the AirCore measurements at Orleans and Sodankylä respectively, indicating that the scaling factor of TCCON XCO data should be around 1.0000 instead of 1.0672. Further investigations should be carried out in the TCCON community to determine the correct scaling factor to be applied to the TCCON XCO data. This paper also demonstrates that the smoothing error must be taken into account when comparing FTIR XCO data, and especially TCCON XCO data, with model or satellite data. [ABSTRACT FROM AUTHOR]

Volume mixing ratio water vapour profiles have been retrieved from IASI (Infrared Atmospheric Sounding Interferometer) spectra by using the MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) processor. The retrievals are made for IASI observations that coincide with Vaisala RS92 radiosonde measurements performed in the framework of the GCOS (Global Climate Observing System) Reference Upper-Air Network (GRUAN) in three different climate zones: the tropics (Manus Island, 2° S), mid-latitudes (Lindenberg, 52° N) and polar regions (Sodankylä, 67° N). The retrievals show good sensitivity with respect to the vertical H2O distribution between 1-2 km above ground and the upper troposphere. Typical DOFS (degree of freedom for signal) values are about 5.5 for the tropics, 5.0 for summertime midlatitudes, 4.0 for wintertime mid-latitudes, and 4.5 for summertime polar regions. The errors of the IASI water vapour profiles have been theoretically estimated considering the contribution of a large number of uncertainty sources. For all three climate regions unrecognized cirrus clouds and uncertainties in atmospheric temperature have been identified as the most important error sources, whereby the total errors are estimated to be typically 25 %. The IASI water vapour profiles have been compared to 100 individual coinciding GRUAN water vapour profiles. The systematic difference between the IASI and GRUAN data is within 12 % at all altitudes. The scatter is largest close to the surface and close to the tropopause, but does never exceed 30 %. The study documents that the MUSICA MetOp/IASI retrieval processor provides H2O profiles with good accuracy and captures the variations in H2O volume mixing ratio profiles from 1-2 km above ground up to altitudes close to the tropopause with a precision that is in accordance to the theoretical error assessment. [ABSTRACT FROM AUTHOR]

The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) has shown that the sensor IASI aboard the satellite MetOp can measure the free tropospheric {H2O,δD} pair distribution twice per day on a quasi-global scale. Such data are very promising for investigating tropospheric moisture pathways, however, the complex data characteristics compromise their usage in the context of model evaluation studies. Here we present a tool that allows for simulating MUSICA MetOp/IASI {H2O,δD} pair remote sensing data for a given model atmosphere, thereby creating model data that have the remote sensing data characteristics assimilated. This model data can then be compared to the MUSICA data. The retrieval simulation method is based on the physical principles of radiative transfer and we show that the uncertainty of the simulations is within the uncertainty of the MUSICA MetOp/IASI products, i.e. the retrieval simulations are reliable enough.We demonstrate the working principle of the simulator by applying it to ECHAM5-wiso model data. The few case studies clearly reveal the large potential of the MUSICA MetOp/IASI {H2O,δD} data pairs for evaluating modelled moisture pathways. The tool is made freely available in form of MATLAB and Python routines and can be easily connected to any atmospheric water vapour isotopologue model. [ABSTRACT FROM AUTHOR]

This paper presents upper tropospheric methane (CH4) and nitrous oxide (N2O) concentrations retrieved from thermal infrared spectra as observed by the remote sensor IASI (Infrared Atmospheric Sounding Interferometer) on-board the EUMETSAT/MetOp meteorological satellites. The CH4 and N2O mixing ratios are retrieved as side products of the MetOp/IASI retrieval developed for the European Research Council project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water). The MUSICA/IASI CH4 and N2O retrieval strategy is described in detail as well as their characterisation in terms of the vertical resolution and expected errors. Theoretically, we document that MUSICA/IASI products can capture the upper tropospheric CH4 and N2O variability (at ≈300-350hPa) with a precision better than 2%. We compare the remote sensing data to coincident high precision aircraft vertical profiles taken within the HIAPER Pole-to-Pole Observations (HIPPO) project and empirically estimate a precision of 2.1% (38.2ppbv) for each individual IASI CH4 observation. The precision is improved to 1.7% (32.1ppbv) for IASI data that have been averaged within 2°×2° boxes. For N2O the empirically estimated precision is 2.7% (8.7ppbv) for each individual observation and 2.1% (6.9ppbv) for the 2°×2° averages. The empirical study works with data from the missions HIPPO1 and HIPPO5, which cover latitudes between 67°S and 80°N during typical winter and summer conditions in both hemispheres, thus being reasonably representative for global observation during different seasons. In addition, we present a product that combines the CH4 and N2O retrieval estimates. The combination is made a-posteriori and we theoretically and empirically show that the combined product has a much better precision than the individual CH4 and N2O products. For the combined product the theoretical precision is 0.8% and the comparison with HIPPO data gives an empirical precision estimate of 1.5% (26.3ppbv) when considering all individual IASI observations and of 1.2% (21.8ppbv) for the 2°×2° averages. In the case that the horizontal, vertical and temporal variation of N2O can be robustly modeled, we can easily reconstruct CH4 from the combined product and generate high quality IASI CH4 data. [ABSTRACT FROM AUTHOR]

We report on the ground-based FTIR (Fourier transform infrared) tropospheric water vapour isotopologue remote sensing data that have been recently made available via the database of NDACC (Network for the Detection of Atmospheric Composition Change; ftp://ftp.cpc.ncep.noaa.gov/ndacc/MUSICA/) and via doi:10.5281/zenodo.48902. Currently, data are available for 12 globally distributed stations. They have been centrally retrieved and quality-filtered in the framework of the MUSICA project (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water). We explain particularities of retrieving the water vapour isotopologue state (vertical distribution of H216 O, H218 O, and HD16O) and reveal the need for a new metadata template for archiving FTIR isotopologue data. We describe the format of different data components and give recommendations for correct data usage. Data are provided as two data types. The first type is best-suited for tropospheric water vapour distribution studies disregarding different isotopologues (comparison with radiosonde data, analyses of water vapour variability and trends, etc.). The second type is needed for analysing moisture pathways by means of fH2O; δDg-pair distributions. [ABSTRACT FROM AUTHOR]

In the lower/middle troposphere, {H2O,δD} pairs are good proxies for moisture pathways; however, their observation, in particular when using remote sensing techniques, is challenging. The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) addresses this challenge by integrating the remote sensing with in situ measurement techniques. The aim is to retrieve calibrated tropospheric {H2O,δD} pairs from the middle infrared spectra measured from ground by FTIR (Fourier transform infrared) spectrometers of the NDACC (Network for the Detection of Atmospheric Composition Change) and the thermal nadir spectra measured by IASI (Infrared Atmospheric Sounding Interferometer) aboard the MetOp satellites. In this paper, we present the final MUSICA products, and discuss the characteristics and potential of the NDACC/FTIR and MetOp/IASI {H2O,δD} data pairs. First, we briefly resume the particularities of an {H2O,δD} pair retrieval. Second, we show that the remote sensing data of the final product version are absolutely calibrated with respect to H2O and δD in situ profile references measured in the subtropics, between 0 and 7 km. Third, we reveal that the {H2O,δD} pair distributions obtained from the different remote sensors are consistent and allow distinct lower/middle tropospheric moisture pathways to be identified in agreement with multi-year in situ references. Fourth, we document the possibilities of the NDACC/FTIR instruments for climatological studies (due to long-term monitoring) and of the MetOp/IASI sensors for observing diurnal signals on a quasi-global scale and with high horizontal resolution. Fifth, we discuss the risk of misinterpreting {H2O,δD} pair distributions due to incomplete processing of the remote sensing products. [ABSTRACT FROM AUTHOR]