The TROPOspheric Monitoring Instrument (TROPOMI), launched in
October 2017 on board the Sentinel-5 Precursor (S5P) satellite, monitors the
composition of the Earth's atmosphere at an unprecedented ...horizontal
resolution as fine as 3.5 × 5.5 km2. This paper assesses the performances
of the TROPOMI formaldehyde (HCHO) operational product compared to its
predecessor, the OMI (Ozone Monitoring Instrument) HCHO QA4ECV product, at different spatial and temporal
scales. The parallel development of the two algorithms favoured the
consistency of the products, which facilitates the production of long-term
combined time series. The main difference between the two satellite products
is related to the use of different cloud algorithms, leading to a positive
bias of OMI compared to TROPOMI of up to 30 % in tropical regions. We show
that after switching off the explicit correction for cloud effects, the two
datasets come into an excellent agreement. For medium to large HCHO vertical
columns (larger than 5 × 1015 molec. cm−2) the median bias between
OMI and TROPOMI HCHO columns is not larger than 10 % (< 0.4 × 1015 molec. cm−2). For lower columns, OMI observations present
a remaining positive bias of about 20 % (< 0.8 × 1015 molec. cm−2) compared to TROPOMI in midlatitude regions. Here, we also
use a global network of 18 MAX-DOAS (multi-axis differential optical absorption spectroscopy) instruments to validate both satellite
sensors for a large range of HCHO columns. This work complements the study
by Vigouroux et al. (2020), where a global FTIR (Fourier transform infrared) network is used to validate
the TROPOMI HCHO operational product. Consistent with the FTIR validation
study, we find that for elevated HCHO columns, TROPOMI data are
systematically low (−25 % for HCHO columns larger than 8 × 1015 molec. cm−2), while no significant bias is found for medium-range column
values. We further show that OMI and TROPOMI data present equivalent biases
for large HCHO levels. However, TROPOMI significantly improves the precision
of the HCHO observations at short temporal scales and for low HCHO columns.
We show that compared to OMI, the precision of the TROPOMI HCHO columns is
improved by 25 % for individual pixels and by up to a factor of 3 when
considering daily averages in 20 km radius circles. The validation precision
obtained with daily TROPOMI observations is comparable to the one obtained
with monthly OMI observations. To illustrate the improved performances of
TROPOMI in capturing weak HCHO signals, we present clear detection of HCHO
column enhancements related to shipping emissions in the Indian Ocean. This
is achieved by averaging data over a much shorter period (3 months) than
required with previous sensors (5 years) and opens new perspectives to
study shipping emissions of VOCs (volatile organic compounds) and related atmospheric chemical
interactions.
Carbon dioxide (CO2) is the most important long-lived greenhouse gas and can be retrieved using solar absorption spectra recorded by a ground-based Fourier-transform infrared spectrometer (FTIR). In ...this study, we investigate the CO2 retrieval strategy using the Network for the Detection of Atmospheric Composition Change–Infrared Working Group (NDACC–IRWG) type spectra between August 2018 and April 2022 (~4 years) at Xianghe, China, aiming to find the optimal observed spectra, retrieval window, and spectroscopy. Two spectral regions, near 2600 and 4800 cm−1, are analyzed. The differences in column-averaged dry-air mole fraction of CO2 (XCO2) derived from spectroscopies (ATM18, ATM20, HITRAN2016, and HITRAN2020) can be up to 1.65 ± 0.95 ppm and 7.96 ± 2.02 ppm for NDACC-type 2600 cm−1 and 4800 cm−1 retrievals, respectively, which is mainly due to the CO2 differences in air-broadened Lorentzian HWHM coefficient (γair) and line intensity (S). HITRAN2020 provides the best fitting, and the retrieved CO2 columns and profiles from both 2600 and 4800 cm−1 are compared to the co-located Total Column Carbon Observing Network (TCCON) measurements and the greenhouse gas reanalysis dataset from the Copernicus Atmosphere Monitoring Service (CAMS). The amplitude of XCO2 seasonal variation derived from the NDACC-type (4800 cm−1) is closer to the TCCON measurements than that from the NDACC-type (2600 cm−1). Moreover, the NDACC-type (2600 cm−1) retrievals are strongly affected by the a priori profile. For tropospheric XCO2, the correlation coefficient between NDACC-type (4800 cm−1) and CAMS model is 0.73, which is higher than that between NDACC-type (2600 cm−1) and CAMS model (R = 0.56).
Measurements of carbon dioxide (CO
2
), methane (CH
4
), and carbon monoxide (CO) are of great importance in the Qinghai-Tibetan region, as it is the highest and largest plateau in the world ...affecting global weather and climate systems. In this study, for the first time, we present CO
2
, CH
4
, and CO column measurements carried out by a Bruker EM27/SUN Fourier-transform infrared spectrometer (FTIR) at Golmud (36.42°E, 94.91°N, 2808 m) in August 2021. The mean and standard deviation of the column-average dry-air mixing ratio of CO
2
, CH
4
, and CO (XCO
2
, XCH
4
, and XCO) are 409.3 ± 0.4 ppm, 1905.5 ± 19.4 ppb, and 103.1 ± 7.7 ppb, respectively. The differences between the FTIR co-located TROPOMI/S5P satellite measurements at Golmud are 0.68 ± 0.64% (13.1 ± 12.2 ppb) for XCH
4
and 9.81 ± 3.48% (−10.7 ± 3.8 ppb) for XCO, which are within their retrieval uncertainties. High correlations for both XCH
4
and XCO are observed between the FTIR and S5P satellite measurements. Using the FLEXPART model and satellite measurements, we find that enhanced CH
4
and CO columns in Golmud are affected by anthropogenic emissions transported from North India. This study provides an insight into the variations of the CO
2
, CH
4
, and CO columns in the Qinghai-Tibetan Plateau.
TROPOMI (the TROPOspheric Monitoring Instrument), on board the Sentinel-5 Precursor (S5P) satellite, has been monitoring the Earth's atmosphere since October 2017 with an unprecedented horizontal ...resolution (initially 7 km.sup.2 x3.5 km.sup.2, upgraded to 5.5 km.sup.2 x3.5 km.sup.2 in August 2019). Monitoring air quality is one of the main objectives of TROPOMI; it obtains measurements of important pollutants such as nitrogen dioxide, carbon monoxide, and formaldehyde (HCHO). In this paper we assess the quality of the latest HCHO TROPOMI products versions 1.1.(5-7), using ground-based solar-absorption FTIR (Fourier-transform infrared) measurements of HCHO from 25 stations around the world, including high-, mid-, and low-latitude sites. Most of these stations are part of the Network for the Detection of Atmospheric Composition Change (NDACC), and they provide a wide range of observation conditions, from very clean remote sites to those with high HCHO levels from anthropogenic or biogenic emissions. The ground-based HCHO retrieval settings have been optimized and harmonized at all the stations, ensuring a consistent validation among the sites.
The nationwide lockdown due to the COVID-19 pandemic in 2020 reduced industrial and human activities in China. In this study, we investigate atmospheric carbon monoxide (CO) concentration changes ...during the lockdown from observations at the surface and from two satellites (TROPOspheric Monitoring Instrument (TROPOMI) and Infrared Atmospheric Sounding Interferometer (IASI)). It is found that the average CO surface concentration in 2020 was close to that in 2019 before the lockdown, and became 18.7% lower as compared to 2019 during the lockdown. The spatial variation of the change in the CO surface concentration is high, with an 8–27% reduction observed for Beijing, Shanghai, Chengdu, Zhengzhou, and Guangzhou, and almost no change in Wuhan. The TROPOMI and IASI satellite observations show that the CO columns decreased by 2–13% during the lockdown in most regions in China. However in South China, there was an 8.8% increase in the CO columns observed by TROPOMI and a 36.7% increase observed by IASI, which is contrary to the 23% decrease in the surface CO concentration. The enhancement of the CO column in South China is strongly affected by the fire emissions transported from Southeast Asia. This study provides an insight into the impact of COVID-19 on CO concentrations both at the surface and in the columns in China, and it can be extended to evaluate other areas using the same approach.
We analyzed seasonality and interannual variability of tropospheric hydrogen cyanide (HCN)
columns in densely populated eastern China for the first time. The results
were derived from solar ...absorption spectra recorded with a ground-based high-spectral-resolution Fourier transform infrared (FTIR) spectrometer in Hefei
(31∘54′ N, 117∘10′ E) between 2015 and
2018. The tropospheric HCN columns over Hefei, China, showed significant
seasonal variations with three monthly mean peaks throughout the year. The
magnitude of the tropospheric HCN column peaked in May, September, and December. The tropospheric HCN column reached a maximum monthly
mean of (9.8±0.78)×1015 molecules cm−2 in May
and a minimum monthly mean of (7.16±0.75)×1015 molecules cm−2 in November. In most cases, the tropospheric HCN columns
in Hefei (32∘ N) are higher than the FTIR observations in Ny-Ålesund (79∘ N), Kiruna (68∘ N), Bremen (53∘ N), Jungfraujoch (47∘ N), Toronto (44∘ N), Rikubetsu
(43∘ N), Izana (28∘ N), Mauna Loa (20∘ N), La
Reunion Maido (21∘ S), Lauder (45∘ S), and Arrival
Heights (78∘ S) that are affiliated with the Network for Detection
of Atmospheric Composition Change (NDACC). Enhancements of tropospheric HCN
column were observed between September 2015 and July 2016 compared to the
same period of measurements in other years. The magnitude of the enhancement
ranges from 5 % to 46 % with an average of 22 %. Enhancement of
tropospheric HCN (ΔHCN) is correlated with the concurrent
enhancement of tropospheric CO (ΔCO), indicating that enhancements
of tropospheric CO and HCN were due to the same sources. The GEOS-Chem tagged CO simulation, the global fire maps, and the potential source
contribution function (PSCF) values calculated using back trajectories
revealed that the seasonal maxima in May are largely due to the influence of
biomass burning in Southeast Asia (SEAS) (41±13.1 %), Europe
and boreal Asia (EUBA) (21±9.3 %), and Africa (AF) (22±4.7 %). The seasonal maxima in September are largely due to the influence
of biomass burnings in EUBA (38±11.3 %), AF (26±6.7 %),
SEAS (14±3.3 %), and North America (NA) (13.8±8.4 %).
For the seasonal maxima in December, dominant contributions are from AF (36±7.1 %), EUBA (21±5.2 %), and NA (18.7±5.2 %). The tropospheric HCN enhancement between September 2015 and July
2016 at Hefei (32∘ N) was attributed to an elevated influence of
biomass burnings in SEAS, EUBA, and Oceania (OCE) in this period. In
particular, an elevated number of fires in OCE in the second half of 2015
dominated the tropospheric HCN enhancement between September and December 2015. An
elevated number of fires in SEAS in the first half of 2016 dominated the
tropospheric HCN enhancement between January and July 2016.
Remote sensing of atmospheric state variables typically relies on the inverse solution of the radiative transfer equation. An adequately characterized retrieval provides information on the ...uncertainties of the estimated state variables as well as on how any constraint or a priori assumption affects the estimate. Reported characterization data should be intercomparable between different instruments, empirically validatable, grid-independent, usable without detailed knowledge of the instrument or retrieval technique, traceable and still have reasonable data volume. The latter may force one to work with representative rather than individual characterization data. Many errors derive from approximations and simplifications used in real-world retrieval schemes, which are reviewed in this paper, along with related error estimation schemes. The main sources of uncertainty are measurement noise, calibration errors, simplifications and idealizations in the radiative transfer model and retrieval scheme, auxiliary data errors, and uncertainties in atmospheric or instrumental parameters. Some of these errors affect the result in a random way, while others chiefly cause a bias or are of mixed character. Beyond this, it is of utmost importance to know the influence of any constraint and prior information on the solution. While different instruments or retrieval schemes may require different error estimation schemes, we provide a list of recommendations which should help to unify retrieval error reporting.
Monitoring the atmospheric CO2 columns inside and around a city is of great importance to understand the temporal–spatial variation of XCO2 near strong anthropogenic emissions. In this study, we use ...two FTIR CO2 column measurements in Beijing (Bruker EM27/SUN) and Xianghe (Bruker IFS 125HR) between 2019 and 2021 to investigate the differences of XCO2 between Beijing (urban) and Xianghe (suburb) in North China and to validate the OCO-2 and OCO-3 satellite XCO2 retrievals. The mean and standard deviation (std) of the ΔXCO2 between Beijing and Xianghe (Beijing–Xianghe) observed by two FTIR instruments are 0.206 ± 1.736 ppm, which has a seasonal variation and varies with meteorological conditions (wind speed and wind direction). The mean and std of the XCO2 differences between co-located satellite and FTIR measurements are −0.216 ± 1.578 ppm in Beijing and −0.343 ± 1.438 ppm in Xianghe for OCO-2 and 0.637 ± 1.594 ppm in Beijing and 1.206 ± 1.420 ppm in Xianghe for OCO-3. It is found that the OCO-3 snapshot area mode (SAM) measurements can capture the spatial gradient of XCO2 between urban and suburbs well. However, the FTIR measurements indicate that the OCO-3 SAM measurements are about 0.9–1.4 ppm overestimated in Beijing and Xianghe.
Among the more than 20 ground-based FTIR (Fourier transform infrared) stations currently operating around the globe, only a few have provided formaldehyde (HCHO) total column time series until now. ...Although several independent studies have shown that the FTIR measurements can provide formaldehyde total columns with good precision, the spatial coverage has not been optimal for providing good diagnostics for satellite or model validation. Furthermore, these past studies used different retrieval settings, and biases as large as 50 % can be observed in the HCHO total columns depending on these retrieval choices, which is also a weakness for validation studies combining data from different ground-based stations.
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 (AKs). 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 2.4 % for TIR-only, 5.1 % for TIR–NIR, and 6.5 % for NIR-only. The TIR–NIR and NIR-only products consistently produce a larger 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 but rather is influenced by the proximity to rapidly changing atmospheric CO. MOPITT bias drift has been bound geographically to within ±0.5 % yr−1 or lower at almost all locations.
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