Characterization and Optimization of the new Imaging Fourier Transform Spectrometer GLORIA

This work focuses on the radiometric and spectrometric characterization and optimization of a new imaging Fourier transform spectrometer (IFTS) called Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA). This characterization work helps to better understand the important features of this IFTS instrument - so that scientific data recorded in campaigns can be understood better and help in understanding the current climate change

Retrievals of XCO2_{CO2}, XCH4_{CH4} and XCO_{CO} from portable, near-infrared Fourier transform spectrometer solar observations in Antarctica

The COllaborative Carbon Column Observing Network (COCCON) uses low-resolution, portable EM27/SUN Fourier transform spectrometers (FTSs) to make retrievals of column-averaged dry-air mole fractions (DMFs, represented as X$_{gas}$) of CO$_{2}$, CH$_{4}$, CO and H$_{2}$O from near-infrared solar absorption spectra. The COCCON has developed rapidly over recent years and complements the Total Carbon Column Observing Network (TCCON). In this work, we provide details of the first seasonal time series of near-infrared X$_{CO2}$, X$_{CH4}$ and X$_{CO}$ retrievals from measurements made in Antarctica during the deployment of an EM27/SUN to the Arrival Heights laboratory on Ross Island over the austral summer of 2019–2020 under the auspices of the COCCON. The DMFs of all three species were lower in Antarctica than at mid-latitude, and for X$_{CO2}$ and X$_{CO}$, the retrieved values were less variable. For X$_{CH4}$ however, the variability was significantly greater and it was found that this was strongly correlated to the proximity of the polar vortex. In order to ensure the stability of the instrument and the traceability of the retrievals, side-by-side comparisons to the TCCON station at Lauder, New Zealand and retrievals of the instrument line shape (ILS) were made before and after the measurements in Antarctica. These indicate that, over the course of the deployment, the instrument stability was such that the change in retrieved X$_{CO2}$ was well below 0.1%. The value of these data for satellite validation is demonstrated by making comparisons with the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 precursor (S5P) satellite. The dataset is available from the COCCON central facility hosted by the ESA Atmospheric Validation Data Centre (EVDC): https://doi.org/10.48477/coccon.pf10.arrivalheights.R02 (Pollard, 2021)

Copernicus Cal/Val Solution - D2.4 - Systematic Ground-Based Measurements

This document aims to map different existing ground-based and air-borne instrumented Cal/Val sites and networks acquiring measurements in a systematic manner, in Europe and worldwide. It does not include all available Cal/Val networks but only those that we interviewed or had enough information available online to include in this report. To meet the needs of satellite Cal/Val, measurements one must adhere to the definition for a Fiducial Reference Measurement (FRM)(Giuseppe Zibordi et al. 2014) and to the principles of the Quality Assurance framework for Earth Observation (QA4EO 2010). The scope of this document is not to evaluate the quality or maturity of the networks/sites that were being interviewed. It only maps the current situation and serves as an input for a later stage of the project. The completed questionnaires that we used to collect the data assembled in this report are not added directly to the document but will be available for project partners for next stage analyses

Atmospheric N2O and CH4 total columns retrieved from low-resolution Fourier transform infrared (FTIR) spectra (Bruker VERTEX 70) in the mid-infrared region

Nitrous oxide (N2O) and methane (CH4) are two important greenhouse gases in the atmosphere. In 2019, mid-infrared (MIR) solar absorption spectra were recorded by a Bruker VERTEX 70 spectrometer and a Bruker IFS 125HR spectrometer at Sodankylä, Finland, at spectral resolutions of 0.2 and 0.005 cm−1, respectively. The N2O and the CH4 retrievals from high-resolution MIR spectra have been well investigated within the Network for the Detection of Atmospheric Composition Change (NDACC) but not for MIR spectra gathered with instruments operating at low spectral resolution. In this study, N2O and CH4 retrieval strategies and retrieval uncertainties from the VERTEX 70 MIR low-resolution spectra are discussed and presented. The accuracy and precision of the VERTEX 70 N2O and CH4 retrievals are assessed by comparing them with the coincident 125HR retrievals and AirCore measurements. The relative differences between the N2O total columns retrieved from 125HR and VERTEX 70 spectra are −0.3 ± 0.7 (1σ) % with a correlation coefficient (R) of 0.93. Regarding the CH4 total column, we first used the same retrieval microwindows for 125HR and VERTEX 70 spectra, but there is an underestimation in the VERTEX 70 retrievals, especially in summer. The relative differences between the CH4 total columns retrieved from the 125HR and VERTEX 70 spectra are -1.3±1.1 (1σ) % with a R value of 0.77. To improve the VERTEX 70 CH4 retrievals, we propose alternative retrieval microwindows. The relative differences between the CH4 total columns retrieved from the 125HR and VERTEX 70 spectra in these new windows become 0.0±0.8 (1σ) %, along with an increase in the R value to 0.87. The coincident AirCore measurements confirm that the VERTEX 70 CH4 retrievals using the latter window choice are better, with relative mean differences between the VERTEX 70 CH4 retrievals and AirCore measurements of −1.9 % for the standard NDACC microwindows and of 0.13 % for the alternative microwindows. This study provides insight into the N2O and CH4 retrievals from the low-resolution (0.2 cm−1) MIR spectra observed with a VERTEX 70 spectrometer, and it demonstrates the suitability of this kind of instrument for contributing to satellite validation, model verification, and other scientific campaigns with the advantage of its transportability and lower cost compared to standard NDACC-type Fourier-transform infrared (FTIR) instruments.</p

Intercomparison of atmospheric CO2 and CH4 abundances on regional scales in boreal areas using Copernicus Atmosphere Monitoring Service (CAMS) analysis, COllaborative Carbon Column Observing Network (COCCON) spectrometers, and Sentinel-5 Precursor satellite observations

We compare the atmospheric column-averaged dry-air mole fractions of carbon dioxide (XCO$_{2}$) and methane (XCH$_{4}$) measured with a pair of COllaborative Carbon Column Observing Network (COCCON) spectrometers at Kiruna and Sodankylä (boreal areas). We compare model data provided by the Copernicus Atmosphere Monitoring Service (CAMS) between 2017 and 2019 with XCH$_{4}$ data from the recently launched Sentinel-5 Precursor (S5P) satellite between 2018 and 2019. In addition, measured and modeled gradients of XCO$_{2}$ and XCH$_{4}$ (ΔXCO$_{2}$ and ΔXCH$_{4}$) on regional scales are investigated. Both sites show a similar and very good correlation between COCCON retrievals and the modeled CAMS XCO$_{2}$ data, while CAMS data are biased high with respect to COCCON by 3.72 ppm (±1.80 ppm) in Kiruna and 3.46 ppm (±1.73 ppm) in Sodankylä on average. For XCH$_{4}$, CAMS values are higher than the COCCON observations by 0.33 ppb (±11.93 ppb) in Kiruna and 7.39 ppb (±10.92 ppb) in Sodankylä. In contrast, the S5P satellite generally measures lower atmospheric XCH$_{4}$ than the COCCON spectrometers, with a mean difference of 9.69 ppb (±20.51 ppb) in Kiruna and 3.36 ppb (±17.05 ppb) in Sodankylä. We compare the gradients of XCO$_{2}$ and XCH$_{4}$ (ΔXCO$_{2}$ and ΔXCH$_{4}$) between Kiruna and Sodankylä derived from CAMS analysis and COCCON and S5P measurements to study the capability of detecting sources and sinks on regional scales. The correlations in ΔXCO$_{2}$ and ΔXCH$_{4}$ between the different datasets are generally smaller than the correlations in XCO$_{2}$ and XCH$_{4}$ between the datasets at either site. The ΔXCO$_{2}$ values predicted by CAMS are generally higher than those observed with COCCON with a slope of 0.51. The ΔXCH$_{4}$ values predicted by CAMS are mostly higher than those observed with COCCON with a slope of 0.65, covering a larger dataset than the comparison between S5P and COCCON. When comparing CAMS ΔXCH$_{4}$ with COCCON ΔXCH$_{4}$ only in S5P overpass days (slope = 0.53), the correlation is close to that between S5P and COCCON (slope = 0.51). CAMS, COCCON, and S5P predict gradients in reasonable agreement. However, the small number of observations coinciding with S5P limits our ability to verify the performance of this spaceborne sensor. We detect no significant impact of ground albedo and viewing zenith angle on the S5P results. Both sites show similar situations with the average ratios of XCH$_{4}$ (S5P/COCCON) of 0.9949±0.0118 in Kiruna and 0.9953±0.0089 in Sodankylä. Overall, the results indicate that the COCCON instruments have the capability of measuring greenhouse gas (GHG) gradients on regional scales, and observations performed with the portable spectrometers can contribute to inferring sources and sinks and to validating spaceborne greenhouse gas sensors. To our knowledge, this is the first published study using COCCON spectrometers for the validation of XCH$_{4}$ measurements collected by S5P

Intercomparison of atmospheric CO2 and CH4 abundances on regional scales in boreal areas using Copernicus Atmosphere Monitoring Service (CAMS) analysis, COllaborative Carbon Column Observing Network (COCCON) spectrometers, and Sentinel-5 Precursor satellite observations

We compare the atmospheric column-averaged dry-air mole fractions of carbon dioxide (XCO2) and methane (XCH4) measured with a pair of COllaborative Carbon Column Observing Network (COCCON) spectrometers at Kiruna and Sodankyla (boreal areas). We compare model data provided by the Copernicus Atmosphere Monitoring Service (CAMS) between 2017 and 2019 with XCH4 data from the recently launched Sentinel-5 Precursor (S5P) satellite between 2018 and 2019. In addition, measured and modeled gradients of XCO2 and XCH4 (Delta XCO2 and Delta XCH4) on regional scales are investigated. Both sites show a similar and very good correlation between COCCON retrievals and the modeled CAMS XCO2 data, while CAMS data are biased high with respect to COCCON by 3.72 ppm (+/- 1.80 ppm) in Kiruna and 3.46 ppm (+/- 1.73 ppm) in Sodankyla on average. For XCH4, CAMS values are higher than the COCCON observations by 0.33 ppb (+/- 11.93 ppb) in Kiruna and 7.39 ppb (+/- 10.92 ppb) in Sodankyla. In contrast, the S5P satellite generally measures lower atmospheric XCH4 than the COCCON spectrometers, with a mean difference of 9.69 ppb (+/- 20.51 ppb) in Kiruna and 3.36 ppb (+/- 17.05 ppb) in So-dankyla. We compare the gradients of XCO2 and XCH4 (Delta XCO2 and Delta XCH4) between Kiruna and Sodankyla derived from CAMS analysis and COCCON and S5P measurements to study the capability of detecting sources and sinks on regional scales. The correlations in Delta XCO2 and Delta XCH4 between the different datasets are generally smaller than the correlations in XCO2 and XCH4 between the datasets at either site. The Delta XCO2 values predicted by CAMS are generally higher than those observed with COCCON with a slope of 0.51. The Delta XCH4 values predicted by CAMS are mostly higher than those observed with COCCON with a slope of 0.65, covering a larger dataset than the comparison between S5P and COCCON. When comparing CAMS Delta XCH4 with COCCON Delta XCH4 only in S5P overpass days (slope = 0.53), the correlation is close to that between S5P and COCCON (slope = 0.51). CAMS, COCCON, and S5P predict gradients in reasonable agreement. However, the small number of observations coinciding with S5P limits our ability to verify the performance of this spaceborne sensor. We detect no significant impact of ground albedo and viewing zenith angle on the S5P results. Both sites show similar situations with the average ratios of XCH4 (S5P/COCCON) of 0.9949 +/- 0.0118 in Kiruna and 0.9953 +/- 0.0089 in Sodankyla. Overall, the results indicate that the COCCON instruments have the capability of measuring greenhouse gas (GHG) gradients on regional scales, and observations performed with the portable spectrometers can contribute to inferring sources and sinks and to validating spaceborne greenhouse gas sensors. To our knowledge, this is the first published study using COCCON spectrometers for the validation of XCH4 measurements collected by S5P

Retrieval of atmospheric CH_4 vertical information from ground-based FTS near-infrared spectra

International audienceThe Total Carbon Column Observing Network (TCCON) column-averaged dry air mole fraction of CH 4 (X CH 4) measurements have been widely used to validate satellite observations and to estimate model simulations. The GGG2014 code is the standard TCCON retrieval software used in performing a profile scaling retrieval. In order to obtain several vertical pieces of information in addition to the total column, in this study, the SFIT4 retrieval code is applied to retrieve the CH 4 mole fraction vertical profile from the Fourier transform spectrometer (FTS) spectrum at six sites (Ny-Ålesund, Sodankylä, Bialystok, Bremen, Orléans and St Denis) during the time period of 2016-2017. The retrieval strategy of the CH 4 profile retrieval from ground-based FTS near-infrared (NIR) spectra using the SFIT4 code (SFIT4NIR) is investigated. The degree of freedom for signal (DOFS) of the SFIT4NIR retrieval is about 2.4, with two distinct pieces of information in the troposphere and in the stratosphere. The averaging kernel and error budget of the SFIT4NIR retrieval are presented. The data accuracy and precision of the SFIT4NIR retrievals, including the total column and two partial columns (in the troposphere and stratosphere), are estimated by TCCON standard retrievals, ground-based in situ measurements, Atmospheric Chemistry Experiment-Fourier Transform Spectrometer (ACE-FTS) satellite observations, TCCON proxy data and AirCore and aircraft measurements. By comparison against TCCON standard retrievals, it is found that the retrieval uncertainty of SFIT4NIR X CH 4 is similar to that of TCCON standard retrievals with systematic uncertainty within 0.35 % and random uncertainty of about 0.5 %. The tropospheric and strato-spheric X CH 4 from SFIT4NIR retrievals are assessed by comparison with AirCore and aircraft measurements, and there is a 1.0 ± 0.3 % overestimation in the SFIT4NIR tropospheric X CH 4 and a 4.0 ± 2.0 % underestimation in the SFIT4NIR stratospheric X CH 4 , which are within the systematic uncertainties of SFIT4NIR-retrieved partial columns in the tropo-sphere and stratosphere respectively

Update on the GOSAT TANSO–FTS SWIR Level 2 retrieval algorithm

The National Institute for Environmental Studies has provided the column-averaged dry-air mole fraction of carbon dioxide and methane (XCO$_2$ and XCH$_4$) products (L2 products) obtained from the Greenhouse gases Observing SATellite (GOSAT) for more than a decade. Recently, we updated the retrieval algorithm used to produce the new L2 product, V03.00. The main changes from the previous version (V02) of the retrieval algorithm are the treatment of cirrus clouds, the degradation model of the Thermal And Near-infrared Spectrometer for carbon Observation–Fourier Transform Spectrometer (TANSO–FTS), solar irradiance spectra, and gas absorption coefficient tables. The retrieval results from the updated algorithm showed improvements in fitting accuracies in the O$_2$ A, weak CO$_2$, and CH$_4$ bands of TANSO–FTS, although the residuals increase in the strong CO$_2$ band over the ocean. The direct comparison of the new product obtained from the updated (V03) algorithm with the previous version V02.90/91 and the validations using the Total Carbon Column Observing Network revealed that the V03 algorithm increases the amount of data without diminishing the data qualities of XCO$_2$ and XCH$_4$ over land. However, the negative bias of XCO$_2$ is larger than that of the previous version over the ocean, and bias correction is still necessary. Additionally, the V03 algorithm resolves the underestimation of the XCO$_2$ growth rate compared with the in situ measurements over the ocean recently found using V02.90/91 and V02.95/96