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Satellite-based observations of free tropospheric water vapour isotopologue ratios (δD) with good global and temporal coverage have become recently available. We investigate the potential of these observations for constraining the uncertainties of the atmospheric analyses fields of specific humidity (q), temperature (T), and δD and of variables that capture important properties of the atmospheric water cycle, namely the vertical velocity (!), the latent heating rate (Q2), and the precipitation rate (Prcp). Our focus is on the impact of 5 the δD observations if used in addition to the observation of q and T, which are much easier to be observed by satellites and routinely in use for atmospheric analyses. For our investigations we use an Observing System Simulation Experiment, i.e. simulate the satellite observations of q, T, and δD with known uncertainties, then use them within a Kalman filter based assimilation framework in order to evaluate their potential for improving the quality of atmospheric analyses. The study is made for low latitudes (30°S to 30°N) and for 40 days between mid-July and end of August 10 2016. We find that the assimilation of q and T observations alone well constrains the atmospheric q and T fields (analyses skills in the free troposphere of up to 60%), and moderately constrains the fields of δD, !, Q2, and Prcp (analyses skills of 20%-40%). The additional assimilation of δD observations further improves the quality of the analyses of all variables. We use Q2 as proxy for the presence of condensation and evaporation processes, and we show that the additional improvement is rather weak when evaporation or condensation are negligible (additional analyses skills of generally below 5%), and strongest for high condensation rates (additional skills of about 15% and above). The very high condensation rates (identified by large positive Q2 values) are rare, but related to extreme events (very high ! and Prcp) that are not well captured in the analyses (for these extreme events also the analyses uncertainties of !, Q2, and Prcp are very large), i.e. the additional assimilation of δD observations significantly improves the analyses of the water cycle related variables for the events when an improvement is most important. In real world satellite datasets δD observations affected by such strong latent heating events are frequently available, suggesting that the here demonstrated additional δD impact for the simulated world is also a realistic scenario for a real world data assimilation. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from three-year (April 2018 - March 2021) TROPOMI observations by combining a wind-assigned anomaly approach and a Machine Learning (ML) method, the so-called Gradient Descent. This combined approach is firstly applied to the Saudi Arabian capital city Riyadh, as a test site, and yields a total emission rate of 1.04×1026 molec./s. The ML-trained anomalies fit very well with the wind-assigned anomalies with an R2 value of 1.0 and a slope of 0.99. Hotspots of NO2 emissions are apparent at several sites where the cement plant and power plants are located and over areas along the highways. Using the same approach, an emission rate of 1.80×1025 molec./s is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable to the CAMS inventory. Weekly variations of NO2 emission are highly related to anthropogenic activities, such as the transport sector. The NO2 emissions were reduced by 24% at weekends in Riyadh, and high reductions are found near the city center and the areas along the highway. An average weekend reduction estimate of 30% in Madrid is found. The regions with dominant sources are located in the east of Madrid, where the residential areas and the Madrid-Barajas airport are located. Additionally, the NO2 emissions decreased by 21% in March-June 2020 compared to the same period in 2019 induced by the COVID-19 lockdowns in Riyadh. A much higher reduction (60%) is estimated for Madrid where a very strict lockdown policy was implemented. The high emission strengths during lockdown only persist in the residential areas and cover smaller areas during weekdays than at weekends. The spatial patterns of NO2 emission strengths during lockdown are similar to those observed at weekends in both cities. Though our analysis is limited to two cities as testing examples, the method has proved to provide reliable and consistent results. Therefore, it is expected to be suitable for other trace gases and other target regions. [ABSTRACT FROM AUTHOR]

Retrieving high-precision concentrations of atmospheric trace gases from FTIR (Fourier Transform Infrared) spectrometry requires a precise knowledge of the instrumental performance. In this context, this paper examines the impact on the ozone (O3) retrievals of several approaches used to characterise the Instrumental Line Shape (ILS) function of ground-based FTIR spectrometers within NDACC (Network for the Detection of Atmospheric Composition Change). The analysis has been carried out at the subtropical Izaña Observatory (IZO, Spain) by using the 20-year time series of the high-resolution FTIR solar absorption spectra acquired between 1999 and 2018. The theoretical quality assessment and the comparison to independent O3 observations available at IZO (Brewer O3 total columns and Electrochemical Concentration Cell, ECC, sondes) reveal consistent findings. The inclusion of a simultaneous retrieval of the ILS parameters in the O3 retrieval strategy allows, on the one hand, a rough instrumental characterisation to be obtained and, on the other hand, the precision of the FTIR O3 products to be slightly improved. The improvement is of special relevance above the lower stratosphere, where the cross-interference between the O3 vertical distribution and the instrumental performance is more significant. However, it has been found that the simultaneous ILS retrieval leads to a misinterpretation of the O3 variations on daily and seasonal scales. Therefore, in order to ensure the independence of the O3 retrievals and the instrumental response, the optimal approach to deal with the FTIR instrumental characterisation is found to be the continuous monitoring of the ILS function by means of independent observations, such as gas-cell measurements. [ABSTRACT FROM AUTHOR]

This work employs ground- and space-based observations, together with model data to study columnar abundances of atmospheric trace gases (XH2O, XCO2, XCH4, and XCO) in two high-latitude Russian cities, St. Petersburg and Yekaterinburg. Two portable COllaborative Column Carbon Observing Network (COCCON) spectrometers were used for continuous measurements at these locations during 2019 and 2020. Additionally, a subset of data of special interest (a strong gradient in XCH4 and XCO was detected) collected in the framework of a mobile city campaign performed in 2019 using both instruments is investigated. All studied satellite products (TROPOMI, OCO-2, GOSAT, MUSICA IASI) show generally good agreement with COCCON observations. Satellite and ground-based observations at high latitude are much sparser than at low or mid latitude, which makes direct coincident comparisons between remote-sensing observations more difficult. Therefore, a method of scaling continuous CAMS model data to the ground-based observations is developed and used for creating virtual COCCON observations. These adjusted CAMS data are then used for satellite validation, showing good agreement in both Peterhof and Yekaterinburg cities. The gradients between the two study sites (ΔXgas) are similar between CAMS and CAMS-COCCON data sets, indicating that the model gradients are in agreement with the gradients observed by COCCON. This is further supported by a few simultaneous COCCON and satellite ΔXgas measurements, which also agree with the model gradient. With respect to the city campaign observations recorded in St. Petersburg, the downwind COCCON station measured obvious enhancements for both XCH4 (10.6 ppb) and XCO (9.5 ppb), which is nicely reflected by TROPOMI observations, which detect city-scale gradients of the order 9.4 ppb for XCH4 and 12.5 ppb XCO, respectively. [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]

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]

In this paper, we compare column-averaged dry-air mole fractions of water vapor (XH2O) retrievals from COCCON (COllaborative Carbon Column Observing Network) with retrievals from two co-located high-resolution FTIR (Fourier transform infrared) spectrometers as references at two boreal sites, Kiruna, Swedenand Sodankylä, Finland. In the framework of the NDACC (Network for the Detection of Atmospheric Composition Change) an FTIR spectrometer is operated in Kiruna. The H2O product derived from these observations has been generated with the MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) processor. In Sodankylä, a TCCON (Total Carbon Column Observing Network) spectrometer is operated, and the official XH2O data as provided by TCCON are used for this study. The datasets are in good overall agreement, with COCCON data showing a wet bias of (49.20 ± 58.61) ppm ((3.33 ± 3.37) %, R2 = 0.9992) compared to MUSICA NDACC and (56.32 ± 45.63) ppm ((3.44 ± 1.77) %, R2 = 0.9997) compared to TCCON. Furthermore, the a priori H2O VMR (volume mixing ratio) profiles (MAP) used as a priori in the TCCON retrievals (also adopted for COCCON retrievals) are evaluated with respect to radiosonde (Vaisala RS41) profiles at Sodankylä. The MAP and radiosonde profiles show similar shapes and good correlation of integrated XH2O, indicating that MAP is a reasonable approximation for the true atmospheric state and an appropriate choice for the scaling retrieval methods as applied by COCCON and TCCON. COCCON shows a reduced dry bias (-1.66 %) in comparison to TCCON (-5.63 %) with respect to radiosonde XH2O and this small bias indicates that besides XCO2 and XCH4 COCCON is also able to serve as validation tool for space-borne XH2O measurements. Finally, we investigate the quality of satellite data at high latitudes. For this purpose, the COCCON XH2O is compared with retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) generated with the MUSICA processor (MUSICA IASI) and with retrievals from the TROPOspheric Monitoring Instrument (TROPOMI). Both paired datasets show generally good agreement and similar correlations at the two sites. COCCON measures 4.64 % less XH2O at Kiruna and 3.36 % at Sodankylä with respect to MUSICA IASI, while COCCON measures 9.71 % more XH2O at Kiruna and 7.75 % at Sodankylä compared with TROPOMI. Our study supports the assumption that COCCON also delivers a well-characterized XH2O data product. This emphasizes the approach of supplementing the TCCON network for satellite validation efforts. This is the first published study where COCCON XH2O is compared with MUSICA NDACC and TCCON retrievals, and for MUSICA IASI and TROPOMI validation. [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]

This paper presents a new data set of vertical column densities of the water vapour isotopologues H2O and HDO retrieved from short-wave infrared (2.3 μm) reflectance measurements by the Tropospheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite. TROPOMI features daily global coverage with a spatial resolution of up to 7 km × 7 km. The retrieval utilises a profile-scaling approach. The forward model neglects scattering, thus strict cloud filtering is necessary. For validation, recent ground-based water vapour isotopologue measurements by the Total Carbon Column Observing Network (TCCON) are employed. A comparison of TCCON δD with measurements by the project Multi-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) for data prior to 2014 (where MUSICA data is available) shows a bias in TCCON δD estimates. As TCCON HDO is currently not validated, an overall correction of recent TCCON HDO data is derived based on this finding. The agreement between the corrected TCCON measurements and collocated TROPOMI observations is good with an average bias of (0.02 ± 2) · 1021 molec cm−2 in H2O and (−0.3 ± 7) · 1017 molec cm−2 in HDO, which corresponds to a bias of (−12 ± 17) ‰ in a posteriori δD. The use of the data set is demonstrated with a case study of a blocking anticyclone in northwestern Europe in July 2018 using single overpass data. [ABSTRACT FROM AUTHOR]

Nitrous oxide (N2O) is an important greenhouse gas and it can also generate nitric oxide, which depletes ozone in the stratosphere. It is a common target species of ground-based FTIR near-infrared (TCCON) and mid-infrared (NDACC) measurements. Both TCCON and NDACC networks provide a long-term global distribution of atmospheric N2O mole fraction. In this study, the dry-air column averaged mole fraction of N2O (XN2O) from the TCCON and NDACC measurements are compared against each other at seven sites around the world (Ny-Ålesund, Sodankylä, Bremen, Izaña, Reunion Island, Wollongong, Lauder) in the time period of 2007-2017. The mean differences in XN2O between the TCCON and NDACC (NDACC-TCCON) at these sites are between -3.32 and 1.37ppb (-1.1-0.5%) with the standard deviations between 1.69 and 5.01ppb (0.5-1.6%), which are within the uncertainties of the two datasets. The NDACC N2O retrieval has good sensitivity throughout the troposphere and stratosphere, while the TCCON retrieval underestimates a deviation from the a priori in the troposphere and overestimates it in the stratosphere. As a result, the TCCON XN2O measurement is strongly affected by its a priori profile. Trends and seasonal cycles of XN2O are derived from the TCCON and NDACC measurements and the nearby surface flask sample measurements, and compared with the results from GEOS-Chem model a priori and a posteriori simulations. The a posteriori N2O fluxes in the model are optimized based on surface N2O measurements with a 4D-Var inversion method. The XN2O trends from the GEOS-Chem a posteriori simulation are very close to those from the NDACC and the surface flask sample measurements (0.9-1.0ppb/year). The XN2O trends from the TCCON measurements are slightly lower (0.8-0.9ppb/year) due to the underestimation of the trend in TCCON a priori. The XN2O trends from the GEOS-Chem a priori simulation are about 1.25ppb/year, and our study confirms that the N2O fluxes from the a priori inventories are overestimated. The seasonal cycles of XN2O from the FTIR measurements and the model simulations are close to each other in the Northern Hemisphere with a maximum in August-October and a minimum in February-April. However, in the Southern Hemisphere, the modeled XN2O shows a minimum in February-April while the FTIR XN2O retrievals shows a minimum in August-October. By comparing the partial column averaged N2O from the model and NDACC for three vertical ranges (surface-8, 8-17, 17-50km), we find that the discrepancy in the XN2O seasonal cycle between the model simulations and the FTIR measurements in the Southern Hemisphere is mainly due to their stratospheric differences. [ABSTRACT FROM AUTHOR]

Improving measurements of water vapour in the lower stratosphere and upper troposphere (UTLS) is a priority for the atmospheric science community. In this work, UTLS water vapour profiles derived from Atmospheric Chemistry Experiment (ACE) satellite measurements are assessed with coincident ground-based measurements taken at a high Arctic observatory at Eureka, Nunavut, Canada. Additional comparisons to satellite measurements taken by AIRS, MIPAS, MLS, SCIAMACHY, and TES are included to put the ACE-FTS and ACE-MAESTRO results in context. Measurements of water vapour profiles at Eureka are made using a Bruker 125HR solar absorption Fourier transform infrared spectrometer at the Polar Environment Atmospheric Research Laboratory (PEARL) and radiosondes launched from the Eureka Weather Station. Radiosonde measurements used in this study have been processed with software developed by the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) to account for known biases and calculate uncertainties in a well-documented and consistent manner. ACE-FTS measurements were within 11 ppmv (13 %) of 125HR measurements between 6 and 14 km. Between 8 and 14 km ACE-FTS profiles showed a small wet bias of approximately 8 % relative to the 125HR. ACE-FTS water vapour profiles had mean differences of 13 ppmv (32 %) or better when compared to coincident radiosonde profiles at altitudes between 6 and 14 km; mean differences were within 6 ppmv (12 %) between 7 and 11 km. ACE-MAESTRO profiles showed a small dry bias relative to the 125HR of approximately 7 % between 6 and 9 km and 10 % between 10 and 14 km. ACE-MAESTRO profiles agreed within 30 ppmv (36 %) of the radiosondes between 7 and 14 km. ACE-FTS and ACE-MAESTRO comparison results show closer agreement with the radiosondes and PEARL 125HR overall than other satellite datasets - except AIRS. Close agreement was observed between AIRS and the 125HR and radiosonde measurements, with mean differences within 5 % and correlation coefficients above 0.83 in the troposphere between 1 and 7 km. Comparisons to MLS at altitudes around 10 km showed a dry bias, e.g., mean differences between MLS and radiosondes were -25.6 %. SCIAMACHY comparisons were very limited due to minimal overlap between the vertical extent of the measurements. TES had no temporal overlap with the radiosonde dataset used in this study. Comparisons between TES and the 125HR showed a wet bias of approximately 25 % in the UTLS and mean differences within 14 % below 5 km. [ABSTRACT FROM AUTHOR]

On 13th October, 2017, the European Space Agency (ESA) successfully launched the Sentinel-5 Precursor satellite with the Tropospheric Monitoring Instrument (TROPOMI) as its single payload. TROPOMI is the first of ESA's atmospheric composition Sentinel missions, which will provide complete long-term records of atmospheric trace gases for the coming 30 years as a contribution to the European Union's Earth Observing programme Copernicus. One of TROPOMI's primary products is atmospheric carbon monoxide (CO). It is observed with daily global coverage and a high spatial resolution of 7 × 7 km2. Due to its moderate atmospheric residence time, its atmospheric abundance provides information on both localized pollution hot spots and the pollutant transport on regional to global scales. In this contribution, we demonstrate the game-changing performance of the TROPOMI CO product, sensing CO enhancements above cities and industrial areas and tracking, with daily coverage, the atmospheric transport of pollution from biomass burning regions. The CO data product is validated with two months of Fourier-transform spectroscopy (FTS) measurements at nine ground-based stations operated by the Total Carbon Column Observing Network (TCCON). We found a good agreement between both data sets with a mean bias of 6 ppb for both clear-sky and cloudy TROPOMI CO retrievals. Together with the corresponding standard deviation of the station-to-station bias of 3.9 ppb for clear-sky and 2.4 ppb for cloudy-sky, it indicates that the CO data product is already well within the mission requirement. [ABSTRACT FROM AUTHOR]

In this study we present the retrieval of the column averaged dry air mole fraction of carbon dioxide (XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) satellite observations using the RemoTeC algorithm, previously successfully applied to retrieval of greenhouse gas concentration from the Greenhouse Gases Observing Satellite (GOSAT). The XCO2 product has been validated with collocated ground based measurements from the Total Carbon Column Observing Network (TCCON) for almost 2 years of OCO-2 data from September 2014 to July 2016. We found that fitting an additive radiometric offset in all three spectral bands of OCO-2 significantly improved the retrieval. Based on a small correlation of the XCO2 error over land with fit residuals, we applied an a posteriori bias correction to our OCO-2 retrievals. In daily averaged results, XCO2 retrievals have a standard deviation ~ 1.30 ppm and a station-to-station variability of ~ 0.40 ppm among collocated TCCON sites. The seasonal relative accuracy (SRA) has a value of 0.52 ppm. The validation shows relatively larger difference with TCCON over high latitude areas and some specific regions like Japan. [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 cross-comparison of different techniques for atmospheric integrated water vapour (IWV) measurements is the essential part of their quality assessment protocol. We inter-compare the synchronised data sets of IWV values measured by Fourier-transform infrared spectrometer Bruker 125 HR (FTIR), microwave radiometer RPG-HATPRO (MW) and global navigation satellite system receiver Novatel ProPak-V3 (GPS) at St. Petersburg site between August 2014 and October 2016. Generally, all three techniques agree well with each other and therefore are suitable for monitoring IWV values at St. Petersburg site. We show that GPS and MW data quality depends on the atmospheric conditions; in dry atmosphere (IWV smaller than 6mm), these techniques are less reliable at St. Petersburg site than the FTIR method. We evaluate the upper bound of statistical measurement errors for clear-sky conditions as 0.33±0.03mm (2.0±0.3%), 0.54±0.03mm (4.5±0.3%), and 0.76±0.04mm (6.3±0.7%) for FTIR, GPS and MW methods, respectively. We conclude that accurate spatial and temporal matching of different IWV measurements is necessary for achieving the better agreement between various methods for IWV monitoring. [ABSTRACT FROM AUTHOR]

The primary instrument on the Greenhouse gases Observing SATellite (GOSAT) is the Thermal And Near infrared Sensor for carbon Observations (TANSO) Fourier Transform Spectrometer (FTS). TANSO-FTS uses three short-wave infrared (SWIR) bands to retrieve total columns of CO2 and CH4 along its optical line-of-sight, and one thermal infrared (TIR) channel to retrieve vertical profiles of CO2 and CH4 volume mixing ratios (VMRs) in the troposphere. We examine version 1 of the TANSO-FTS TIR CH4 product by comparing co-located CH4 VMR vertical profiles from two other remote sensing FTS systems: the Canadian Space Agency's Atmospheric Chemistry Experiment-FTS (ACE-FTS) on SCISAT (version 3.5), and the European Space Agency's Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat (ESA ML2PP version 6 and IMK-IAA reduced-resolution version V5R_CH4_224/225), as well as 16 ground stations with the Network for the Detection of Atmospheric Composition Change (NDACC). This work follows an initial inter-comparison study over the Arctic, which incorporated a ground-based FTS at the Polar Environment Atmospheric Research Laboratory (PEARL) at Eureka, Canada, and focuses on tropospheric and lower-stratospheric measurements made at middle and tropical latitudes between 2009 to 2013 (mid 2012 for MIPAS). For comparison, vertical profiles from all instruments are interpolated onto a common pressure grid, and the ACE-FTS, MIPAS, and NDACC vertical profiles are smoothed using the TANSO-FTS averaging kernels. We present zonally-averaged mean CH4 differences between each instrument and TANSO-FTS with and without smoothing, examine their information content, sensitive altitude range, correlation, a priori dependence, and the variability within each data set. Partial columns are calculated from the VMR vertical profiles, and their correlations are examined. We find that the TANSO-FTS vertical profiles agree with the ACE-FTS and both MIPAS retrievals' vertical profiles within 4% below 15km when smoothing is applied to the profiles from instruments with finer vertical resolution, but that the relative differences can increase to on the order of 25% when no smoothing is applied. Computed partial columns are tightly correlated for each pair of data sets. We investigated whether the difference between TANSO-FTS and other CH4 VMR data products varies with latitude. Our study reveals a small dependence of around 0.1% per ten degrees latitude, with smaller differences over the equator, and greater differences towards the poles. [ABSTRACT FROM AUTHOR]

We present atmospheric ozone (O3) profiles measured over central Mexico between November 2012 and February 2014 by two different ground-based FTIR (Fourier transform infrared) solar absorption experiments. The first instrument offers very high resolution spectra and contributes to NDACC (Network for the Detection of Atmospheric Composition Change). It is located at a mountain observatory about 1700 m above the Mexico City basin. The second instrument has a medium spectral resolution and is located inside of Mexico City at a horizontal distance of about 60 km to the mountain observatory. It is documented that the retrieval with the high and medium resolution experiments give O3 variations for three and four independent atmospheric altitude ranges, respectively, whereby the theoretically estimated errors of these profile data are mostly within 10 %. The good quality of the data is empirically demonstrated: above the tropopause by intercomparing the two FTIR O3 data and for the boundary layer by comparing the Mexico City FTIR O3 data with in-situ O3 surface data. Furthermore, we develop a combined boundary layer O3 remote sensing product that uses the retrieval results of both FTIR experiments and demonstrate theoretically and empirically the improvements achieved by such combination. [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]

Water vapour is a critical component of the Earth system. Techniques to acquire and improve measurements of atmospheric water vapour and its isotopes are under active development. This work presents a detailed intercomparison of water vapour total column measurements taken between 2006 and 2014 at a Canadian high Arctic research site. Instruments include radiosondes, sun photometers, a microwave radiometer, and emission and solar absorption Fourier transform spectrometers (FTSs). Good agreement is observed between all combination of datasets, with correlation coefficients ≥ 0.90 showing high correlations. A variety of biases and calibration issues are revealed and discussed for all instruments. A new FTS dataset, resulting from the MUSICA (Multi-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) retrieval technique, is shown to offer accurate measurements of water vapour total columns; however, measurements show a small wet bias of approximately 6%. A new dataset derived from Atmospheric Emitted Radiance Interferometer (AERI) measurements is also shown to provide accurate water vapour measurements, which usefully enables measurements to be taken during day and night (especially valuable during Polar Night). In addition, limited profile comparisons are conducted using radiosonde and ground-based FTS measurements. Results show MUSICA FTS profiles were within 15% of radiosonde measurements throughout the troposphere. [ABSTRACT FROM AUTHOR]

Knowing the spatial and seasonal distributions of nitric acid (HNO3) around the globe is of great interest to apprehend the processes regulating stratospheric ozone, especially in the polar regions. Thanks to its unprecedented spatial and temporal sampling, the nadir-viewing Infrared Atmospheric Sounding Interferometer (IASI) allows sounding the atmosphere twice a day globally, with good spectral resolution and low noise. With the Fast Optimal Retrievals on Layers for IASI (FORLI) algorithm, we are retrieving, in near-real time, columns as well as vertical profiles of several atmospheric species, amongst which is HNO3. We present in this paper the first characterization of the FORLI-HNO3 profile products, in terms of vertical sensitivity and error budgets. We show that the sensitivity of IASI to HNO3 is highest in the lower stratosphere (10-20 & km), where the largest amounts of HNO3 are found, but that the vertical sensitivity of IASI only allows one level of information on the profile (DOFS 1). The sensitivity near the surface is negligible in most cases, and for this reason, a partial column (5-35 & km) is used for the analyses. Both vertical profiles and partial columns are compared to FTIR ground-based measurements from the Network for the Detection of Atmospheric Composition Change (NDACC) to characterize the accuracy and precision of the FORLI-HNO3 product. The profile validation is conducted through the smoothing of the raw FTIR profiles by the IASI averaging kernels and gives good results, with a slight overestimation of IASI measurements in the Upper Troposphere-Lower Stratosphere (UTLS) at the 6 chosen stations (Thule, Kiruna, Jungfraujoch, Izaña, Lauder and Arrival Heights). The validation of the partial columns (5-35 & km) is also conclusive with a mean correlation of 0.93 between IASI and the FTIR measurements. An initial survey of the HNO3 spatial and seasonal variabilities obtained from IASI measurements for a one year (2011) data set shows that the expected latitudinal gradient of concentrations from low to high latitudes and the large seasonal variability in polar regions (cycle amplitude around 30 & % of the seasonal signal, peak-to-peak) are well represented with IASI data. [ABSTRACT FROM AUTHOR]

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 IASIA 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. [ABSTRACT FROM AUTHOR]

The thermal And near infrared sensor for carbon observations Fourier transform spectrometer (TANSOFTS) on board the Greenhouse Gases Observing Satellite (GOSAT) applies the normal nadir mode above the land ("land data") and sun glint mode over the ocean ("ocean data") to provide global distributions of columnaveraged dry-air mole fractions of CO2 and CH4 , or XCO2 and XCH4. Several algorithms have been developed to obtain highly accurate greenhouse gas concentrations from TANSO-FTS/GOSAT spectra. So far, all the retrieval algorithms have been validated with the measurements from ground-based Fourier transform spectrometers from the Total Carbon Column Observing Network (TCCON), but limited to the land data. In this paper, the ocean data of the SRPR, SRFP (the proxy and full-physics versions 2.3.5 of SRON/KIT's RemoTeC algorithm), NIES (National Institute for Environmental Studies operational algorithm version 02.21) and ACOS (NASA's Atmospheric CO2 Observations from Space version 3.5) are compared with FTIR measurements from five TCCON sites and nearby GOSAT land data. For XCO2, both land and ocean data of NIES, SRFP and ACOS show good agreement with TCCON measurements. Averaged over all TCCON sites, the relative biases of ocean data and land data are -0.33 and -0.13% for NIES, 0.03 and 0.04% for SRFP, 0.06 and -0.03% for ACOS, respectively. The relative scatter ranges between 0.31 and 0.49 %. For XCH4, the relative bias of ocean data is even less than that of the land data for the NIES (0.02 vs. -0.35 %), SRFP (0.04 vs. 0.20 %) and SRPR (-0.02 vs. 0.06 %) algorithms. Compared to the results for XCO2, the XCH4 retrievals show larger relative scatter (0.65-0.81 %). [ABSTRACT FROM AUTHOR]