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The purpose of this work is to derive new NO2 vertical profiling data products from Pandora spectrometers and investigate the factors contributing to the bias of this dataset relative to established ground-based and spaceborne datasets. Possible applications of the NO2 vertical profile dataset include air quality monitoring and satellite validation studies. We explore the application of the optimal estimation method to Pandora multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements to retrieve vertical profile information for nitrogen dioxide (NO2). We use the Heidelberg Profile (HeiPro) retrieval algorithm to derive, for the first time, NO2 profiles and partial columns (0–4 km) from Pandora MAX-DOAS measurements from 2018–2020 at Downsview, a suburban neighbourhood in the north end of Toronto, Canada that is subject to local traffic emissions and urban influences. Validation of the new dataset was done via comparison with official Pandora direct-Sun measurements, in situ observations, satellite data, and an air quality forecasting model. We find that, for tropospheric partial column comparisons, the HeiPro dataset has a positive mean relative bias to Pandora direct-Sun (61 % ± 9.7 %) and TROPOMI (41 % ± 47 %) observations, as well as the GEM-MACH model output (61 % ± 7.5 %), with similar seasonal and diurnal cycles in the bias with Pandora direct-Sun and GEM-MACH. Contributing factors to the large bias of HeiPro to Pandora direct-Sun were investigated, and NO2 heterogeneity, combined with differences between direct-Sun and multi-axis viewing geometries, was found to contribute a maximum of 52 % of the total relative bias during morning measurement times. For surface NO2 comparisons, we find that HeiPro measurements capture the magnitude and diurnal variability of surface NO2 reasonably well (mean relative bias to in situ surface NO2: −8.9 % ± 7.6 %) but are low-biased compared to GEM-MACH (mean relative bias: −36 % ± 2.4 %). Compared to HeiPro, the GEM-MACH model profiles are high-biased in the lower boundary layer and low-biased in the free troposphere. [ABSTRACT FROM AUTHOR]

Measurements of Pollution in the Troposphere (MOPITT) is an instrument on NASA's Terra satellite that has measured tropospheric carbon monoxide (CO) from early 2000 to the present day. Validation of data from satellite instruments like MOPITT is often conducted using ground-based measurements to ensure the continued accuracy of the space-based instrument's measurements and its scientific results. Previous MOPITT validation studies generally found a larger bias in the MOPITT data poleward of 60 °N. In this study, we use data from 2006 to 2019 from the Bruker IFS 125HR Fourier Transform Infrared spectrometer (FTIR) located at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada to validate the MOPITT Version 8 retrievals. These comparisons utilize mid- and near-infrared FTIR measurements made as part of the Network for the Detection for Atmospheric Composition Change (NDACC) and the Total Carbon Column Observing Network (TCCON), respectively. All MOPITT version 8 retrievals within a 1° radius from the PEARL Ridge Laboratory and within a 24-hour time interval are used in this validation study. MOPITT retrieval products include those from the near-infrared (NIR) channel, the thermal infrared (TIR) channel, and a joint product from the thermal and near-infrared (TIR-NIR) channels. Each channel's detector has four pixels. We calculated the MOPITT pixel-to-pixel biases for each pixel, which were found to vary based on the season and surface type (land or water). The systematic bias for pixel 1 over land is larger than that for other pixels, which can reach up to 20 ppb. We use a small-region approximation method to find filtering criteria. We then apply the filters to the MOPITT dataset to minimize the MOPITT pixel bias and the number of outliers in the dataset. The sensitivity of each MOPITT pixel and each product is examined over the Canadian high Arctic. We then follow the methodologies recommended by NDACC and TCCON for the comparison between the FTIR and satellite total column retrievals. MOPITT averaging kernels are used to weight the NDACC and TCCON retrievals and take into account the different vertical sensitivities between the satellite and PEARL FTIR measurements. We use a modified Taylor diagram to present the comparison results from each pixel for each product over land and water with NDACC and TCCON measurements. Our results show overall consistency between MOPITT and the NDACC and TCCON measurements. When compared to the FTIR, the NIR MOPITT retrievals have a positive bias of 3-10 % depending on the pixel. The bias values are negative for the TIR product, with values between ~5 % and 0 %. The joint TIR-NIR products show differences of ~4 % to 7 %. The drift in MOPITT biases (in units of %year-1) relative to NDACC and TCCON varies by MOPITT data product. In the NIR, drifts versus TCCON are smaller than those versus NDACC, however, this scenario is reversed for the MOPITT TIR and joint TIR-NIR products. [ABSTRACT FROM AUTHOR]

This paper reports on ground-based validation of the atmospheric OClO data record produced in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC SAF) using the GOME2-A and -B instruments over the 2007-2016 and 2013-2016 periods, respectively. OClO slant column densities are compared to correlative measurements collected from 9 NDACC Zenith-Scattered-Light DOAS (ZSL-DOAS) instruments distributed in both the Arctic and Antarctic. Sensitivity tests are performed on the ground-based data to estimate the impact of the different OClO DOAS analysis settings. On this basis, we infer systematic uncertainties of about 25 % between the different ground-based data analysis, reaching total uncertainties ranging from about 26 % to 33 % for the different stations. Time-series at the different sites show good agreement between satellite and ground-based data, both for the inter-annual variability and the overall OClO seasonal behavior. GOME-2A results are found to be nosier than those of GOME-2B, especially after 2011, probably due to instrumental degradation effects. Daily linear regression analysis for OClO activated periods yield correlation coefficients of 0.8 for GOME-2A and 0.87 for GOME-2B, with slopes of 0.64 and 0.72, respectively. Biases are within 8 x 1013 molec/cm2 with some differences between GOME-2A and GOME- 2B, depending on the station. Overall, considering all the stations, a median bias of about -2.2 x 1013 molec/cm2 is found for both GOME-2 instruments. [ABSTRACT FROM AUTHOR]

Satellite retrievals of XCO2 at northern high latitudes currently have sparser coverage and lower data quality than most other regions of the world. We use a neural network (NN) to filter OCO-2 B10 bias-corrected XCO2 retrievals and compare the quality of the filtered data to the quality of the data filtered with the standard B10 quality control filter. To assess the performance of the NN filter, we use Total Carbon Column Observing Network (TCCON) data at selected northern high latitude sites as a truth proxy. We found that the NN filter decreases the overall bias by 0.25 ppm (~50%), improves the precision by 0.18 ppm (~12%), and increases the throughput by 16% at these sites when compared to the standard B10 quality control filter. Most of the increased throughput was due to an increase in throughput during the spring, fall, and winter seasons. There was a decrease in throughput during the summer, but as a result the bias and precision were improved during the summer months. The main drawback of using the NN filter is that it lets through fewer retrievals at the highest latitude Arctic TCCON sites compared to the B10 quality control filter, but the lower throughput improves the bias and precision. [ABSTRACT FROM AUTHOR]

ACE/TROPOMI Abstract for AMT submission The TROPOspheric Monitoring Instrument (TROPOMI) provides a daily, spatially-resolved (initially 7 × 7 km2, upgraded to 7 × 5.6 km2 in August 2019) global data set of CO columns, however, due to the relative sparseness of reliable ground-based data sources, it can be challenging to characterize the validity and accuracy of satellite data products in remote regions such as the high Arctic. In these regions, satellite inter-comparisons can supplement model- and ground-based validation efforts and serve to verify previously observed differences. In this paper, we compare the CO products from TROPOMI, the Atmospheric Chemistry Experiment (ACE) Fourier Transform Spectrometer (FTS), and a high-Arctic ground-based FTS located at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut (80.05° N, 86.42° W). A global comparison of TROPOMI reference profiles scaled by the retrieved total column with ACE-FTS CO partial columns for the period from 10 November 2017 to 31 May 2020 displays excellent agreement between the two data sets (R = 0.93), and a small relative bias of −0.68 ± 0.25 % (bias ± standard error). Additional comparisons were performed within five latitude bands; the north Polar region (60° N to 90° N), northern Mid-latitudes (20° N to 60° N), the Equatorial region (20° S to 20° N), southern Mid-latitudes (60° S to 20° S), and the south Polar region (90° S to 60° S). Latitudinal comparisons of the TROPOMI and ACE-FTS CO datasets show strong correlations ranging from R = 0.93 (southern Mid-latitudes) to R = 0.85 (Equatorial region) between the CO products, but display a dependence of the mean differences on latitude. Positive mean biases of 7.92 ± 0.58 % and 7.98 ± 0.51 % were found in the northern and southern Polar regions, respectively, while a negative bias of −9.16 ± 0.55 % was observed in the Equatorial region. To investigate whether these differences are introduced by cloud contamination which is reflected in the TROPOMI averaging kernel shape, the latitudinal comparisons were repeated for cloud-covered pixels and clear-sky pixels only, and for the unsmoothed and smoothed cases. Clear-sky pixels were found to be biased higher with poorer correlations on average than clear+cloudy scenes and cloud-covered scenes only. Furthermore, the latitudinal dependence on the biases was observed in both the smoothed and unsmoothed cases. To provide additional context to the global comparisons of TROPOMI with ACE-FTS in the Arctic, both satellite data sets were compared against measurements from the ground-based PEARL-FTS. Comparisons of TROPOMI with smoothed PEARL-FTS total columns in the period of 3 March 2018 to 27 March 2020 display a strong correlation (R = 0.88), however a positive mean bias of 14.3 ± 0.16 % was also found. A partial column comparison of ACE-FTS with the PEARL-FTS in the period from 25 February 2007 to 18 March 2020 shows good agreement (R = 0.82), and a mean positive bias of 9.83 ± 0.22 % in the ACE-FTS product relative to the ground-based FTS. The magnitude and sign of the mean relative differences are consistent across all inter-comparisons in this work, as well as with recent ground-based validation efforts, suggesting that current TROPOMI CO product exhibits a positive bias in the high-Arctic region. However, the observed bias is within the TROPOMI mission accuracy requirement of ±15 %, providing further confirmation that the data quality in these remote high-latitude regions meets this specification. [ABSTRACT FROM AUTHOR]

A better understanding of greenhouse gas surface sources and sinks is required in order to address the global challenge of climate change. Spaceborne remote estimations of greenhouse gas atmospheric concentrations can offer the global coverage that is necessary to improve the constraint on their fluxes, thus enabling a better monitoring of anthropogenic emissions. In this work, we introduce the Adaptable 4A Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters from any remote sensing observation. The algorithm is based on Bayesian optimal estimation relying on the Operational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along with the Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information (GEISA) spectroscopic database. Here, the 5AI scheme is applied to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) from measurements performed by the Orbiting Carbon Observatory-2 (OCO-2) mission, and uses an empirically corrected absorption continuum in the O2 A-band. For airmasses below 3.0, XCO2 retrievals successfully capture the latitudinal variations of CO2, as well as its seasonal cycle and long-term increasing trend. Comparison with ground-based observations from the Total Carbon Column Observing Network (TCCON) yields a difference of 1.33 ± 1.29 ppm, which is similar to the standard deviation of the Atmospheric CO2 Observations from Space (ACOS) official products. We show that the systematic differences between 5AI and ACOS results can be fully removed by adding an average calculated - observed spectral residual correction to OCO-2 measurements, thus underlying the critical sensitivity of retrieval results to forward modelling. These comparisons show the reliability of 5AI as a Bayesian optimal estimation implementation that is easily adaptable to any instrument designed to retrieve column-averaged dry-air mole fractions of greenhouse gases. [ABSTRACT FROM AUTHOR]

Ammonia (NH3) is a major source of nitrates in the atmosphere, and a major source of fine particulate matter. As such, there have been increasing efforts to measure the atmospheric abundance of NH3 and its spatial and temporal variability. In this study, long-term measurements of NH3 derived from multiscale datasets are examined. These NH3 datasets include 16 years of total column measurements using Fourier transform infrared (FTIR) spectroscopy, three years of surface in-situ measurements, and 10 years of total column measurements from the Infrared Atmospheric Sounding Interferometer (IASI). The datasets were used to quantify NH3 temporal variability over Toronto, Canada. The multiscale datasets were also compared to assess the observational footprint of the FTIR measurements. All three time series showed positive trends in NH3 over Toronto: 3.34 ± 0.46%/year from 2002 to 2018 in the FTIR columns, 8.88 ± 2.83%/year from 2013 to 2017 in the surface in-situ data, and 8.38 ± 0.77%/year from 2008 to 2018 in the IASI columns. To assess the observational footprint of the FTIR NH3 columns, correlations between the datasets were examined. The best correlation between FTIR and IASI was obtained with coincidence criteria of ≤ 25 km and ≤ 20 minutes, with r = 0.73 and a slope of 1.14 ± 0.06. Additionally, FTIR column and in-situ measurements were standardized and correlated. Comparison of 24-day averages and monthly averages resulted in correlation coefficients of r = 0.72 and r = 0.75, respectively, although correlation without resampling to reduce high-frequency variability led to a poorer correlation, with r = 0.39. The GEOS-Chem model, run at 2° × 2.5° resolution, was compared against FTIR and IASI to assess model performance and investigate correlation of observational data and model output, both with local column measurements (FTIR) and measurements on a regional scale (IASI). Comparisons on a regional scale (a domain spanning 35° N to 53° N, and 93.75° W to 63.75° W) resulted in r = 0.57, and thus a coefficient of determination, which is indicative of the predictive capacity of the model, of r² = 0.33, but comparing a single model grid point against the FTIR resulted in a poorer correlation, with r² = 0.13, indicating that a finer spatial resolution is needed for modeling NH3. [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI) on-board the Sentinel-5 Precursor satellite (launched on 13 October 2017) is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible, near-infrared, and shortwave infrared spectral ranges. The measured spectra are used to retrieve total columns of trace gases, including nitrogen dioxide (NO2). For ground validation of these satellite measurements, Pandora spectrometers, which retrieve high-quality NO2 total columns via direct-sun measurements, are widely used. In this study, Pandora NO2 measurements made at three sites located in or north of the Greater Toronto Area (GTA) are used to evaluate the TROPOMI NO2 data products, including standard Royal Netherlands Meteorological Institute (KNMI) tropospheric and stratospheric NO2 data product and a TROPOMI research data product developed by Environment and Climate Change Canada (ECCC) using a high-resolution regional air quality forecast model (used in the air mass factor calculation). It is found that these current TROPOMI tropospheric NO2 data products (standard and ECCC) met the TROPOMI design bias requirement. Using the statistical uncertainty estimation method, the estimated TROPOMI upper limit precision falls below the design requirement at a rural site, but above in the other two urban and suburban sites. The Pandora instruments are found to have sufficient precision to perform TROPOMI validation work. In addition to the traditional satellite validation method (i.e., pairing ground-based measurements with satellite measurements closest in time and space), we analyzed TROPOMI pixels located upwind and downwind from the Pandora site. This makes it possible to improve the statistics and better interpret the high-spatial-resolution measurements made by TROPOMI. By using this wind-based validation technique, the number of coincident measurements can be increased by about a factor of five. Using this larger number of coincident measurements, this work shows that both TROPOMI and Pandora instruments can reveal detailed spatial patterns (i.e., horizontal distributions) of local and transported NO2 emissions, which can be used to evaluate regional air quality changes. The TROPOMI ECCC NO2 research data product shows improved agreement with Pandora measurements compared to the TROPOMI standard tropospheric NO2 data product, demonstrating benefits from the high-resolution regional air quality forecast model. [ABSTRACT FROM AUTHOR]

A methodology for retrieving high-latitude winter water vapour columns from passive microwave satellite measurements from Perro et al. (2016) is extended to use measured surface reflectance ratios under more realistic surface reflection assumptions. Pan-Arctic wintertime water vapour is retrieved from Advanced Technology Microwave Sounder (ATMS) measurements made from January 2012 through March 2015 (December to March). The water vapour retrievals are validated using two ground based instruments: the G-band Vapor Radiometer (GVR) at Barrow, Alaska, and the Extended-Range Atmospheric Emitted Radiance Interferometer (E-AERI) at Eureka, Nunavut. E-AERI was chosen as an additional point of validation compared to Perro et al. (2016) due to the different technology and frequencies employed to determine water vapour column compared to the ATMS and GVR. For water vapour columns less than 6 kg m-2, the biases are +2.6 % and +0.01 % relative to the GVR and E-AERI, respectively. A comparison with radiosonde humidity measurements shows they are dry relative to the ATMS measurements in North America and Western Europe, and moist in Asia and Eastern Europe, with an apparent dependence on radiosonde manufacturer. Reanalyses (ERA-5, ERA-Interim, ASR V2, JRA-55 and NCEP) are systematically drier than the ATMS measurements for water vapour columns less than 6 kg m-2, with relative biases ranging from -10 % to -23 %. These differences could have implications for the understanding of the Arctic water budget and climate. [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]

Zenith-Sky scattered light Differential Optical Absorption Spectroscopy (ZS-DOAS) has been used widely to retrieve total column ozone (TCO). ZS-DOAS measurements have the advantage of being less sensitive to clouds than direct-sun measurements. However, the presence of clouds still affects the quality of ZS-DOAS TCO. Clouds are thought to be the largest contributor to random uncertainty in ZS-DOAS TCO, but their impact on data quality still needs to be quantified. This study has two goals: (1) to study whether clouds have a significant impact on ZS-DOAS TCO, and (2) to develop a cloud-screening algorithm to improve ZS-DOAS measurements in the Arctic under cloudy conditions. To quantify the impact of weather, eight years of measured and modelled TCO have been used, along with information about weather conditions at Eureka, Canada (80.05° N, 86.41° W). Relative to direct-sun TCO measurements by Brewer spectrophotometers and modelled TCO, a positive bias is found in ZS-DOAS TCO measured in cloudy weather, and a negative bias is found for clear conditions, with differences of up to 5 % between clear and cloudy conditions. A cloud-screening algorithm is developed for high-latitudes using the colour index calculated from ZS-DOAS spectra. The quality of ZS-DOAS TCO datasets is assessed using a statistical uncertainty estimation model, which suggests a 3-4 % random uncertainty. The new cloud-screening algorithm reduces the random uncertainty by 0.6 %. If all measurements collected during cloudy conditions, as identified using the weather station observations, are removed, the random uncertainty is reduced by 1.3 %. This work demonstrates that clouds are a significant contributor to uncertainty in ZS-DOAS TCO and proposes a method that can be used to screen clouds in high-latitude spectra. [ABSTRACT FROM AUTHOR]

High-resolution absorption spectra of the a¹Δg←X³Σg- O2 band measured using cavity ring-down spectroscopy were fitted using the Voigt and speed-dependent Voigt line shapes. We found that the speed-dependent Voigt line shape was better able to model the measured absorption coefficients than the Voigt line shape. Total columns of O2 were retrieved from ground-based high-resolution absorption spectra from four Total Carbon Column Observing Network (TCCON) sites using both Voigt and speed-dependent Voigt line shapes to calculate absorption coefficients. A lower O2 concentration was retrieved with the speed-dependent Voigt line shape, with the difference increasing as a function of solar zenith angle. CO2 total columns were also retrieved from the same spectra using a Voigt line shape and speed-dependent Voigt with line mixing. The column-averaged dry-air mole fraction of CO2 (XCO2) was calculated using the CO2 and O2 columns retrieved with both line shapes from measurements made over a one-year period at the four sites and compared. The inclusion of speed dependence reduces the airmass dependence of XCO2. The TCCON empirical airmass correction factor for XCO2 derived from a year of measurements from TCCON sites at Darwin, Lamont, and Park Falls for XCO2 improved from -0.0071±0.0057 to -0.0012±0.0054 when speed dependence was included. XCO2 retrieved with the Voigt and speed-dependent Voigt line shapes was compared to aircraft profiles measured at 13 TCCON sites. The bias between the TCCON measurements and the integrated aircraft profile measurements was reduced from 0.9897±0.0005 to 1.0041±0.0005 for XCO2 retrieved with the Voigt and speed-dependent Voigt line shapes respectively. These results suggest that speed dependence should be included in the forward model when fitting near-infrared CO2 and O2 spectra to improve the accuracy of XCO2 measurements. [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]

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]

The Differential Optical Absorption Spectroscopy (DOAS) method is a well-known remote sensing technique that is nowadays widely used for measurements of atmospheric trace gases, creating the need for harmonization and characterization efforts. In this study, an intercomparison exercise of DOAS retrieval codes from 17 international groups is presented focusing on NO2 slant columns. The study is based on data collected by one instrument during the Multi-Axis DOAS Comparison campaign for Aerosols and Trace gases (MAD-CAT) in Mainz, Germany, in summer 2013. As data from the same instrument is used by all groups, the results are free of biases due to instrumental differences, which is in contrast to previous intercomparison exercises. While in general an excellent correlation of NO2 slant columns between groups of > 99.98% (noon reference fits), and > 99.2% (sequential reference fits) for all elevation angles is found, differences between individual retrievals are as large as 8% for NO2 slant columns and 100% for RMS residuals. Two kinds of disagreements were identified: (1) Absolute slant column differences were found to result predominantly from the choice of the reference spectrum. (2) Relative differences were found to originate from the numerical approach for solving the DOAS equation as well as the treatment of the slit function. Differences in the implementations of the intensity offset correction lead to disagreements for retrievals close to sunrise (8-10% for NO2, 80% for RMS residual). Apart from this, the largest effect of ≈ 8% difference in NO2 was found to arise from the reference treatment, in particular for fits using a sequential reference. In terms of RMS fit residual, the reference treatment has only a minor impact. In contrast, the wavelength calibration as well as the intensity offset correction were found to have the largest impact (up to 80%) on RMS residual while having only a minor impact on retrieved NO2 slant columns. [ABSTRACT FROM AUTHOR]

This paper presents eight years (2006-2013) of measurements obtained from Fourier Transform Spectrometers (FTSs) in the high Arctic at the Polar Environment Atmospheric Research Laboratory (PEARL, 80.05°N, 86.42°W). These measurements were taken as part of the Canadian Arctic ACE (Atmospheric Chemistry Experiment) Validation Campaigns that have been carried out since 2004 during the polar sunrise period (from mid-February to mid-April). Each spring, two ground-based FTSs were used to measure total and partial columns of HF, O3, and trace gases that impact O3 depletion, namely, HCl, and HNO3. Additionally, some tropospheric greenhouse gases and pollutant species were measured, namely CH4, N2O, CO, and C2H6. During the same time period, the satellite-based ACE-FTS made measurements near Eureka and provided profiles of the same trace gases. Comparisons have been carried out between the measurements from PARIS-IR and the co-located high-resolution Bruker 125HR FTS, as well as with the latest version of the ACE-FTS retrievals (v3.5). The total column comparison between the two co-located ground-based FTSs, PARIS-IR and Bruker 125HR, found very good agreement for most of these species (except HF), with differences well below the estimated uncertainties (~6%) and with high correlations (R≥0.8). Partial columns have been used for the ground-based to space-borne comparison, with coincident measurements selected based on time, distance and scaled potential vorticity (sPV). The comparisons of the ground-based measurements with ACE-FTS show good agreement in the partial columns for most species within 6% (except for C2H6 and PARIS-IR HF), which are consistent with the total retrieval uncertainty of the ground-based instruments. The correlation coefficients (R) of the partial column comparisons for all eight species range from approximately 0.75 to 0.95. The comparisons show no significant increase in the mean differences over these eight years, indicating the consistency of these datasets and suggesting that the space-borne ACE-FTS measurements have been stable over this period. In addition, changes in the amounts of these trace gases during springtime between 2006 and 2013 are presented and discussed. Increased O3 (0.9%yr-1), HCl (1.7%yr-1), HF (3.8%yr-1), CH4 (0.5%yr-1) and C2H6 (2.3%yr-1, 2009-2013) have been found near PEARL from the Portable Atmospheric Research Interferometric Spectrometer for the InfraRed (PARIS-IR) dataset. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of the recently developed Pandora spectrometer by comparing it with the Brewer reference triad. This triad was established by Environment and Climate Change Canada (ECCC) in the 1980s and is used to calibrate Brewer instruments around the world, ensuring high quality total column ozone (TCO) measurements. To reduce stray light, the double Brewer instrument was introduced in 1992, and a new reference triad of double Brewers is also operational at Toronto. Since 2013, ECCC has deployed two Pandora spectrometers co-located with the old and new Brewer triads, making it possible to study the performance of three generations of ozone-monitoring instruments. The statistical analysis of TCO records from these instruments indicates that the random uncertainty for the Brewer is below 0.6 %, while that for the Pandora is below 0.4 %. However, there is a 1 % seasonal difference and a 3 % bias between the standard Pandora and Brewer TCO data, which is related to the temperature dependence and difference in ozone cross sections. A statistical model was developed to remove this seasonal difference and bias. It was based on daily temperature profiles from the European Centre for Medium-Range Weather Forecasts Interim data over Toronto and TCO from the Brewer reference triads. When the statistical model was used to correct Pandora data, the seasonal difference was reduced to 0.25 % and the bias was reduced to 0.04 %. Pandora instruments were also found to have low airmass dependence up to 81.6° solar zenith angle, comparable to double Brewer instruments. [ABSTRACT FROM AUTHOR]

The Measurements of Pollution in the Troposphere (MOPITT) satellite instrument provides the longest continuous dataset of carbon monoxide (CO) from space. We perform the first validation of MOPITT version 6 retrievals using total column CO measurements from ground-based remote sensing Fourier transform infrared spectrometers (FTSs). Validation uses data recorded at 14 stations, that span a wide range of latitudes (80° N to 78° S), in the Network for the Detection of Atmospheric Composition Change (NDACC). MOPITT measurements are spatially co-located with each station and different vertical sensitivities between instruments are accounted for by using MOPITT averaging kernels. All three MOPITT retrieval types are analyzed: thermal infrared (TIR-only), joint thermal and near infrared (TIR-NIR), and near infrared (NIR-only). Generally, MOPITT measurements overestimate CO relative to FTS measurements, but the bias is typically less than 10 %. Mean bias is 2.8 % for TIR-only, 5.4 % for TIR-NIR and 7.0 % for NIR-only. The TIR-NIR and NIR-only products consistently produce greater bias and lower correlation than the TIR-only. Validation performance of MOPITT for TIR-only and TIR-NIR retrievals over land or water scenes is equivalent. The four MOPITT detector element pixels are validated separately to account for their different uncertainty characteristics. Pixel 1 produces the highest standard deviation and lowest correlation, for all three MOPITT products. However, for TIR only and TIR-NIR, the error-weighted average that includes all four pixels often provides the best correlation, indicating compensating pixel biases and well captured error characteristics. We find that MOPITT bias does not depend on latitude, and rather is influenced by the proximity to rapidly changing atmospheric CO. MOPITT bias drift has been bound geographically to within ±0.5 % yr- or lower at almost all locations. [ABSTRACT FROM AUTHOR]

We present cross-validation of remote sensing measurements of methane profiles in the Canadian high Arctic. Accurate and precise measurements of methane are essential to understand quantitatively its role in the climate system and in global change. Here, we show a crossvalidation between three data sets: two from spaceborne instruments and one from a ground-based instrument. All are Fourier transform spectrometers (FTSs). We consider the Canadian SCISAT Atmospheric Chemistry Experiment (ACE)-FTS, a solar occultation infrared spectrometer operating since 2004, and the thermal infrared band of the Japanese Greenhouse Gases Observing Satellite (GOSAT) Thermal And Near infrared Sensor for carbon Observation (TANSO)-FTS, a nadir/off-nadir scanning FTS instrument operating at solar and terrestrial infrared wavelengths, since 2009. The ground-based instrument is a Bruker 125HR Fourier transform infrared (FTIR) spectrometer, measuring mid-infrared solar absorption spectra at the Polar Environment Atmospheric Research Laboratory (PEARL) Ridge Laboratory at Eureka, Nunavut (80° N, 86° W) since 2006. For each pair of instruments, measurements are collocated within 500 km and 24 h. An additional collocation criterion based on potential vorticity values was found not to significantly affect differences between measurements. Profiles are regridded to a common vertical grid for each comparison set. To account for differing vertical resolutions, ACE-FTS measurements are smoothed to the resolution of either PEARL-FTS or TANSO-FTS, and PEARL-FTS measurements are smoothed to the TANSO-FTS resolution. Differences for each pair are examined in terms of profile and partial columns. During the period considered, the number of collocations for each pair is large enough to obtain a good sample size (from several hundred to tens of thousands depending on pair and configuration). Considering full profiles, the degrees of freedom for signal (DOFS) are between 0.2 and 0.7 for TANSO-FTS and between 1.5 and 3 for PEARL-FTS, while ACE-FTS has considerably more information (roughly 1 DOFS per altitude level). We take partial columns between roughly 5 and 30 km for the ACE-FTS-PEARL-FTS comparison, and between 5 and 10 km for the other pairs. The DOFS for the partial columns are between 1.2 and 2 for PEARL-FTS collocated with ACE-FTS, between 0.1 and 0.5 for PEARL-FTS collocated with TANSO-FTS or for TANSO-FTS collocated with either other instrument, while ACE-FTS has much higher information content. For all pairs, the partial column differences are within ±3×1022 moleculescm-2. Expressed as median±median absolute deviation (expressed in partial column units or as a percentage), these differences are 0.11±9.60×1020 moleculescm-2(0.012±1.018 %) for TANSO-FTS-PEARL-FTS, -2:6±2:6×1021 moleculescm-2(-1:6±1:6 %) for ACEFTS- PEARL-FTS, and 7:4±6:0×1020 moleculescm-2(0:78±0:64 %) for TANSO-FTS-ACE-FTS. The differences for ACE-FTS-PEARL-FTS and TANSO-FTS-PEARL-FTS partial columns decrease significantly as a function of PEARL partial columns, whereas the range of partial column values for TANSO-FTS-ACE-FTS collocations is too small to draw any conclusion on its dependence on ACE-FTS partial columns. [ABSTRACT FROM AUTHOR]

Motivated by the initial selection of a highresolution solar occultation Fourier transform spectrometer (FTS) to fly to Mars on the ExoMars Trace Gas Orbiter, we have been developing algorithms for retrieving volume mixing ratio vertical profiles of trace gases, the primary component of which is a new algorithm and software for retrieving vertical profiles of temperature and pressure from the spectra. In contrast to Earth-observing instruments, which can rely on accurate meteorological models, a priori information, and spacecraft position, Mars retrievals require a method with minimal reliance on such data. The temperature and pressure retrieval algorithms developed for this work were evaluated using Earth-observing spectra from the Atmospheric Chemistry Experiment (ACE) FTS, a solar occultation instrument in orbit since 2003, and the basis for the instrument selected for a Mars mission. ACE-FTS makes multiple measurements during an occultation, separated in altitude by 1.5-5 km, and we analyse 10 CO2 vibration-rotation bands at each altitude, each with a different usable altitude range. We describe the algorithms and present results of their application and their comparison to the ACE-FTS data products. The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) provides vertical profiles of temperature up to 40 km with high vertical resolution. Using six satellites and GPS radio occultation, COSMIC's data product has excellent temporal and spatial coverage, allowing us to find coincident measurements with ACE with very tight criteria: less than 1.5 h and 150 km. We present an intercomparison of temperature profiles retrieved from ACE-FTS using our algorithm, that of the ACE Science Team (v3.5), and from COSMIC. When our retrievals are compared to ACE-FTS v3.5, we find mean differences between -5 and +2K and that our retrieved profiles have no seasonal or zonal biases but do have a warm bias in the stratosphere and a cold bias in the mesosphere. When compared to COSMIC, we do not observe a warm/cool bias and mean differences are between -4 and +1 K. COSMIC comparisons are restricted to below 40 km, where our retrievals have the best agreement with ACE-FTS v3.5. When comparing ACE-FTS v3.5 to COSMIC we observe a cold bias in COSMIC of 0.5 K, and mean differences are between -0.9 and +0.6 K. [ABSTRACT FROM AUTHOR]