Trace gas measurements from different spectral regions using FTIR spectroscopy

This work focuses on the identification and quantification of differences between trace gas measurements from different spectral regions using FTIR spectroscopy. A modified analysis strategy is developed which is necessary when a direct comparison between measurements from different spectral regions is performed. Differences between long-term measurements of carbon monoxide abundances are analyzed aiming at harmonizing data records from the two ground-based FTIR networks NDACC and TCCON

Comparison of OCO-2 target observations to MUCCnet – is it possible to capture urban XCO2_{CO2} gradients from space?

In this paper, we compare Orbiting Carbon Observatory 2 (OCO-2) measurements of column-averaged dry-air mole fractions (DMF) of CO$_{2}$ (X$_{CO2}$) and its urban–rural differences against ground-based remote sensing data measured by the Munich Urban Carbon Column network (MUCCnet). Since April 2020, OCO-2 has regularly conducted target observations in Munich, Germany. Its target-mode data provide high-resolution X$_{CO2}$ within a 15 km × 20 km target field of view that is greatly suited for carbon emission studies from space in cities and agglomerated areas. OCO-2 detects urban X$_{CO2}$ with a root mean square different (RMSD) of less than 1 ppm when compared to the MUCCnet reference site. OCO-2 target X$_{CO2}$ is biased high against the ground-based measurements. The close proximity of MUCCnet\u27s five fully automated remote sensing sites enables us to compare spaceborne and ground-based X$_{CO2}$ in three urban areas of Munich separately (center, north, and west) by dividing the target field into three smaller comparison domains. Due to this more constrained collocation, we observe improved agreement between spaceborne and ground-based X$_{CO2}$ in all three comparison domains. For the first time, X$_{CO2}$ gradients within one OCO-2 target field of view are evaluated against ground-based measurements. We compare X$_{CO2}$ gradients in the OCO-2 target observations to gradients captured by collocated MUCCnet sites. Generally, OCO-2 detects elevated X$_{CO2}$ in the same regions as the ground-based monitoring network. More than 90 % of the observed spaceborne gradients have the same orientation as the X$_{CO2}$ gradients measured by MUCCnet. During our study, urban–rural enhancements are found to be in the range of 0.1 to 1 ppm. The low urban–rural gradients of typically well below 1 ppm in Munich during our study allow us to test OCO-2\u27s lower detection limits for intra-urban X$_{CO2}$ gradients. Urban X$_{CO2}$ gradients recorded by the OCO-2 instruments and MUCCnet are strongly correlated (R$^{2}$=0.68) with each other and have an RMSD of 0.32 ppm. A case study, which includes a comparison of one OCO-2 target overpass to WRF-GHG modeled X$_{CO2}$ , reveals a similar distribution of enhanced CO$_{2}$ column abundances in Munich. In this study, we address OCO-2\u27s capability to detect small-scale spatial X$_{CO2}$ differences within one target observation. Our results suggest OCO-2\u27s potential to assess anthropogenic emissions from space

How bias correction goes wrong: measurement of X_(CO_2) affected by erroneous surface pressure estimates

All measurements of X_(CO_2) from space have systematic errors. To reduce a large fraction of these errors, a bias correction is applied to X_(CO_2) retrieved from GOSAT and OCO-2 spectra using the ACOS retrieval algorithm. The bias correction uses, among other parameters, the surface pressure difference between the retrieval and the meteorological reanalysis. Relative errors in the surface pressure estimates, however, propagate nearly 1:1 into relative errors in bias-corrected X_(CO_2). For OCO-2, small errors in the knowledge of the pointing of the observatory (up to ∼130 arcsec) introduce a bias in X_(CO_2) in regions with rough topography. Erroneous surface pressure estimates are also caused by a coding error in ACOS version 8, sampling meteorological analyses at wrong times (up to 3 h after the overpass time). Here, we derive new geolocations for OCO-2's eight footprints and show how using improved knowledge of surface pressure estimates in the bias correction reduces errors in OCO-2's v9 X_(CO_2) data

How bias correction goes wrong: measurement of X_(CO_2) affected by erroneous surface pressure estimates

All measurements of X_(CO_2) from space have systematic errors. To reduce a large fraction of these errors, a bias correction is applied to X_(CO_2) retrieved from GOSAT and OCO-2 spectra using the ACOS retrieval algorithm. The bias correction uses, among other parameters, the surface pressure difference between the retrieval and the meteorological reanalysis. Relative errors in the surface pressure estimates, however, propagate nearly 1:1 into relative errors in bias-corrected X_(CO_2). For OCO-2, small errors in the knowledge of the pointing of the observatory (up to ∼130 arcsec) introduce a bias in X_(CO_2) in regions with rough topography. Erroneous surface pressure estimates are also caused by a coding error in ACOS version 8, sampling meteorological analyses at wrong times (up to 3 h after the overpass time). Here, we derive new geolocations for OCO-2's eight footprints and show how using improved knowledge of surface pressure estimates in the bias correction reduces errors in OCO-2's v9 X_(CO_2) data

Tracking CO2 emission reductions from space: A case study at Europe’s largest fossil fuel power plant

We quantify CO2 emissions from Europe’s largest fossil fuel power plant, the Bełchatόw Power Station in Poland, using CO2 observations from NASA’s Orbiting Carbon Observatory (OCO) 2 and 3 missions on 10 occasions from March 2017 to June 2022. The space-based CO2 emission estimates reveal emission changes with a trend that is consistent with the independent reported hourly power generation trend that results from both permanent and temporary unit shutdowns. OCO-2 and OCO-3 emission estimates agree with the bottom-up emission estimates within their respective 1σ uncertainties for 9 of the 10 occasions. Different methods for defining background values and corresponding uncertainties are explored in order to better understand this important potential error contribution. These results demonstrate the ability of existing space-based CO2 observations to quantify emission reductions for a large facility when adequate coverage and revisits are available. The results are informative for understanding the expected capability and potential limitations of the planned Copernicus Anthropogenic CO2 Monitoring (CO2M) and other future satellites to support monitoring and verification of CO2 emission reductions resulting from climate change mitigation efforts such as the Paris Agreement