MAMAP2D-Light is an airborne passive remote sensing imaging push-broom spectrometer developed at the Institute for Environmental Physics at the University of Bremen to determine atmospheric methane (CH4) and carbon dioxide (CO2) column anomalies in the 1.6 mu m-band to quantify point-source emissions. In its initial version, as flown in 2022 in Canada, a significant stray light level of 5.6 % of the measured signal has been observed post-campaign, causing apparent error patterns in the retrieved CO2 and CH4 column anomalies. Measurement data collected during an airborne campaign in 2022 in Canada offer the unique opportunity to investigate the end-to-end impact of stray light and its correction on the retrieved CO2 and CH4 column anomalies, as well as the derived emission rates. We successfully developed and applied a stray light correction to the instrument and investigated its impact on the CH4/CO2 proxy method, the CH4 column, and derived point-source emissions. In nearly all cases, applying the CH4/CO2 proxy method reduced the stray-light-related column errors below the CH4 column noise. The derived emission rates for the proxy-retrieval with and without stray light corrected spectra are comparable, proving the ability of the CH4/CO2 proxy method to correct stray-light-related artifacts. In this paper, we additionally investigate the impact on the CH4 total column retrieval for a high contrast scene condition under which the correction by applying the proxy method is no longer sufficient. Following the initial campaign in 2022, the post-campaign stray light characterization and analysis revealed that a significant fraction of stray light was attributed to reflective surfaces in the object plane of the spectrometer. Based on these findings, the total stray light was reduced by similar to 63 % by implementing a hardware modification from 2023 onward.

Abstract. Regulating the emission of methane (CH4) from anthropogenic sources plays an important role in global climate mitigation strategies. The waste sector is responsible for some of the strongest localized sources and therefore its control has the potential for high impact reduction measures. As shown in a related preceding publication, airborne lidar and imaging spectrometer data provide reliable measurements of CH4 columns and can be used to estimate CH4 fluxes from sources such as landfills. However, incomplete knowledge of the transport of greenhouse gases from the source to the measurement point is one of the main sources of uncertainty in flux determination. In this paper, we improve the accuracy over previous emission estimates by applying a new analysis method, based on high-resolution regional-scale weather simulations to describe the atmospheric transport. We use this model to perform a combined fit to the CH4 measurements from airborne lidar and passive remote sensing, as well as in situ measurements. The key improvement over previous estimates comes from explicitly accounting for the complex, time-varying wind field on the measurement day, which caused significant CH4 accumulation that biased earlier cross-sectional flux estimates. This investigation focuses on two waste facilities close to Madrid, Spain, which were overflown by the German research aircraft HALO during a flight of the CoMet 2.0 Arctic mission. The estimated emission rates for the two sites were determined to be 4.0 ± 1.2 t h-1 (Pinto landfill) and 4.7 ± 0.7 t h-1 (Valdemingómez waste site).

In 2018, the CoMet series of airborne missions was launched under the auspices of the German Aerospace Center (DLR). From this point onward, large-scale field campaigns are organized regularly every few years, in collaboration with partner institutions, to understand the anthropogenic and natural fluxes of the greenhouse gases CO2 and CH4, using the German research aircraft HALO. In addition to scientific objectives, the CoMet field campaigns aim to promote technological developments necessary for new and future Earth observation satellites, validate current greenhouse gas satellite measurements, and prepare new satellite missions, in particular the upcoming German-French methane lidar mission MERLIN. One of the core instruments of CoMet is the IPDA lidar CHARM-F, which was developed at DLR as an airborne demonstrator, testbed, and validation tool for MERLIN.

Governments and industries worldwide are seeking methods to accurately estimate their methane inventories, particularly in the open-cut coal mining sector, where quantifying facility-level emissions remains challenging and robust verification methods are not yet widespread. Here, we compare methane emission rates estimated from two aircraft-based measurement platforms with operator-reported emissions from an open-cut coal mine in the Bowen Basin (Queensland, Australia). Coarse-resolution satellite-based data identified the mine as a significant emitter, making it ideal for case studies using airborne in situ and remote sensing platforms that provide high-resolution measurements to isolate mine-scale emissions. Using airborne in situ measurements, we estimated methane emission rates of 14.0 +/- 3.3 (+/- 2 sigma) t h-1 during May and June 2022. In September 2023, airborne in situ and remote sensing measurements yielded consistent emission rate estimates of 9.6 +/- 1.9 (+/- 2 sigma) t h-1 and 11.3 +/- 5.3 (+/- 2 sigma) t h-1, respectively. If sustained, these rates would equate to annual emissions of 1.5-4.2 Mt of CO2 equivalents (CO2-e) year-1, 3-8 times higher than operator-reported annual Scope 1 emissions (0.53-0.54 Mt of CO2-e year-1). Beyond highlighting the potential for under-reporting of emissions at this mine, our results indicate that aircraft-based technologies are valuable tools for supporting accurate reporting of facility-scale methane emissions from open-cut coal mines.

Methane (CH4) is the second most important anthropogenic greenhouse gas (GHG), and its emissions reduction has been identified as an essential mitigation target to slow down climate change. According to inventories, fossil fuel production and usage account for roughly 17% of the global CH4 emissions, of which approximately 33% originate from coal mining. Accurate identification of coal mining-related CH4 sources and quantification of their annual emission rate is needed for corporate reporting requirements, national inventory verification, and the development of CH4 mitigation strategies.A previous study estimated CH4 emissions for six coal mines in the Bowen Basin in Queensland, Australia, using TROPOMI satellite measurements. It covered a sub-area of the Bowen Basin, where coal is mined at over 40 active mining locations distributed over 60,000 km2. The study showed a significant discrepancy compared to inventory estimates by a factor of 7 during 2018 and 2019.To further verify satellite estimates and improve knowledge of the distribution, persistence, and strength of emissions of this mining region, the Bowen Basin CH4 Mapping (BBCMap) Campaign was conducted in September-October 2023, funded by and performed in collaboration with UNEP's International Methane Emissions Observatory. During this campaign, two HK36 Eco-Dimona research aircraft carrying complementary sensing instrumentation were deployed. The MAMAP2D-Light (Methane Airborne MAPper 2D – Light) imaging spectrometer for estimating atmospheric CH4 and CO2 column anomalies and a lidar for topography scans were deployed on one DIMONA HK36 research aircraft, while the second identical aircraft was equipped with an in-situ payload consisting of an LGR OA-ICOS gas analyser for simultaneous measurements of atmospheric CH4, CO2, and water vapor concentrations, a turbulence probe for wind statistics, and a bag sampler for collecting multiple gas samples during each flight for later 13C isotope analyses in the laboratory. This two-aircraft strategy allowed coordinated measurements of CH4 emissions from different coal mines with both remote sensing and in-situ instruments and simultaneous wind measurements, which is essential for deriving a robust flux estimate.During the campaign, 39 flights were conducted, covering approximately 33 mines across roughly 20,000 km2, focussing on the northern part of the Bowen Basin. Preliminary MAMAP2D-Light measurements of atmospheric CH4 column anomalies and emission estimates for both open-cut and underground coal mines will be presented and discussed.

The CoMet 2.0 Arctic airborne measurement campaign of 2022 targeted a variety of natural as well as anthropogenic sources of CH4, mostly in Canada, such as landfills, coal mines, power plants or fossil fuel exploitation sites. Many anthropogenic emission targets consist of a few strong emitters with small or negligible spatial extension. In these cases, emission plumes can readily be observed by passive imaging spectrometers, through the observed enhancement in column averaged CH4. However, over oil and gas fields such as the Lloydminster area at the Alberta/Saskatchewan border, with numerous individual wells extending over large areas, this is much more difficult since individual plumes are lower in magnitude and may even overlap. In such cases it may not be possible to resolve plumes from individual sources, but the total flux can still be estimated using a budget approach. Nevertheless, limitations arise from spatial and temporal variations in the wind field, regarding proper quantification of the source strengths. In this contribution we present our strategy for source attribution, combining measurements by the airborne CHARM-F greenhouse-gas lidar and the MAMAP2DL imaging spectrometer with emission inventories and inverse modeling. A similar approach has already been successfully applied to CHARM-F data recorded over the Upper Silesian Coal Basin during the CoMet 1.0 campaign. CHARM-F is an Integrated-Path Differential-Absorption (IPDA) lidar that provides vertical column concentrations of CO2 and CH4 up to the flight altitude along the flight track. The advantages of lidar are the insensitivity to illumination conditions and a low intrinsic bias. MAMAP2DL is a passive airborne push broom imaging spectrometer that measures spatially resolved changes in relative column concentrations of CH4 and CO2. During the CoMet 2.0 Arctic campaign in August and September 2022, CHARM-F and MAMAP2DL have been deployed onboard the German research aircraft HALO, alongside a suite of complementary instruments for in-situ measurements of CH4, CO2 and other trace gases. We introduce our methods for data treatment and inverse modelling and show first results from this approach.

To reduce and mitigate anthropogenic greenhouse gas surface fluxes from industrial sites, their sources must be, firstly, identified or localized and, secondly, accurately quantified. For methane (CH4), the second most important anthropogenic greenhouse gas, the quantification of its diverse emitters is still a challenge. Due to their nature, these emitters can reach dimensions from point sources to hundreds of square kilometres for fossil fuel (gas, oil, coal) exploitation sites or up to several square kilometres in case of waste disposal sites. Although, CH4 emissions from, e.g., waste disposal sites can be computed from activity data combined with landfill models, a high potential for unintended and poorly quantified leakages remain due to, e.g., potential ruptures in the landfill cover. Consequently, the exact localization and quantification of those leakages is a necessary step towards reducing CH4 emissions from waste disposal sites. To have better knowledge and insights into anthropogenic and natural greenhouse gas emissions, a team of scientists has assembled a comprehensive suite of instruments aboard the German Research aircraft HALO (High Altitude and Long Range Research Aircraft) during the CoMet 2.0 Arctic mission conducted in Canada in August and September 2022. Although the campaign was primarily intended to observe and quantify CH4 and CO2 emissions and disentangle anthropogenic from natural sources at the high northern latitudes of Canada, a test flight over Spain revealed unexpectedly high and still persistent emissions from two landfills in Madrid - Valdemingomez and Pinto, previously also pointed out in an ESA story based on satellite observations. Both were investigated by means of passive and active remote sensing, as well as in situ airborne techniques. The measurements of the passive airborne remote sensing instrument MAMAP2D-Light, developed at the University of Bremen, delivers atmospheric concentration anomaly maps of CH4 and CO2. Here, its imaging capabilities are used to pin-point the origin of the CH4 emissions across the targeted landfills and to quantify their emissions. MAMAP2D-Light’s concentration maps are combined with highly accurate CH4 column concentration measurements from the integrated-path differential-absorption lidar CHARM-F (CO2 and CH4 Remote Monitoring-Flugzeug), developed by German Aerospace Center (DLR) in Oberpfaffenhofen. Additionally, airborne CH4 in situ mole fractions were measured by the Jena Instrument for Greenhouse Gases (JIG) and supplemented with wind data within the emission plume in order to complement the remote sensing observations. This contribution will present top-down emission estimates from measurements of all aforementioned instruments, operated quasi-simultaneously, i.e. within a time span of approximate 2 hours, over the targeted area in Madrid in August 2022.

Methane (CH4), alongside carbon dioxide (CO2), is a key driver of anthropogenic climate change. Reducing CH4 is crucial for short-term climate mitigation. Waste-related activities, such as landfills, are a major CH4 source, even in developed countries. Atmospheric concentration measurements using remote sensing (RS) offer a powerful way to quantify these emissions. We study waste facilities near Madrid, Spain, where satellite data indicated high CH4 emissions. For the first time, we combine passive imaging (Methane Airborne Mapper 2D – Light, MAMAP2DL) and active lidar (CO2 and CH4 Atmospheric Remote Monitoring – Flugzeug, CHARM-F) remote sensing aboard the German High Altitude and Long Range Research Aircraft (HALO), supported by in situ instruments, to quantify CH4 emissions. Using the CH4 column data and European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis v5 (ERA5) model wind information validated by airborne measurements, we estimate landfill emissions through a cross-sectional mass balance approach. Strong emission plumes are traced up to 20 km downwind on 4 August 2022, with the highest CH4 column anomalies observed over active landfill areas in the vicinity of Madrid, Spain. Total emissions are estimated to be up to ∼ 13 t h−1. Single co-located plume crossings from both instruments agree well within 1.2 t h−1 (or 13 %). Flux errors range from ∼ 25 % to 40 %, mainly due to boundary layer (BL) and wind speed variability. This case study not only showcases the capabilities of applying a simple but fast cross-sectional mass balance approach, along with its limitations due to challenging atmospheric boundary layer conditions, but also demonstrates, to our knowledge, the first successful use of both active and passive airborne remote sensing to quantify methane emissions from hotspots and independently verify their emissions.

Power plants and large industrial facilities contribute more than half of global anthropogenic CO2 emissions. Quantifying the emissions of these point sources is therefore one of the main goals of the planned constellation of anthropogenic CO2 monitoring satellites (CO2M) of the European Copernicus program. Atmospheric transport models may be used to study the capabilities of such satellites through observing system simulation experiments and to quantify emissions in an inverse modeling framework. How realistically the CO2 plumes of power plants can be simulated and how strongly the results may depend on model type and resolution, however, is not well known due to a lack of observations available for benchmarking. Here, we use the unique data set of aircraft in situ and remote sensing observations collected during the CoMet (Carbon Dioxide and Methane Mission) measurement campaign downwind of the coal-fired power plants at Bełchatów in Poland and Jänschwalde in Germany in 2018 to evaluate the simulations of six different atmospheric transport models. The models include three large-eddy simulation (LES) models, two mesoscale numerical weather prediction (NWP) models extended for atmospheric tracer transport, and one Lagrangian particle dispersion model (LPDM) and cover a wide range of model resolutions from 200 m to 2 km horizontal grid spacing. At the time of the aircraft measurements between late morning and early afternoon, the simulated plumes were slightly (at Jänschwalde) to highly (at Bełchatów) turbulent, consistent with the observations, and extended over the whole depth of the atmospheric boundary layer (ABL; up to 1800 m a.s.l. (above sea level) in the case of Bełchatów). The stochastic nature of turbulent plumes puts fundamental limitations on a point-by-point comparison between simulations and observations. Therefore, the evaluation focused on statistical properties such as plume amplitude and width as a function of distance from the source. LES and NWP models showed similar performance and sometimes remarkable agreement with the observations when operated at a comparable resolution. The Lagrangian model, which was the only model driven by winds observed from the aircraft, quite accurately captured the location of the plumes but generally underestimated their width. A resolution of 1 km or better appears to be necessary to realistically capture turbulent plume structures. At a coarser resolution, the plumes disperse too quickly, especially in the near-field range (0–8 km from the source), and turbulent structures are increasingly smoothed out. Total vertical columns are easier to simulate accurately than the vertical distribution of CO2, since the latter is critically affected by profiles of vertical stability, especially near the top of the ABL. Cross-sectional flux and integrated mass enhancement methods applied to synthetic CO2M data generated from the model simulations with a random noise of 0.5–1.0 ppm (parts per million) suggest that emissions from a power plant like Bełchatów can be estimated with an accuracy of about 20 % from single overpasses. Estimates of the effective wind speed are a critical input for these methods. Wind speeds in the middle of the ABL appear to be a good approximation for plumes in a well-mixed ABL, as encountered during CoMet.

<p>Remote sensing measurements of greenhouse gases from aircraft to detect and quantify greenhouse gas emissions began about 15 years ago. These measurements have been exploited to detect and quantify predominantly anthropogenic emissions. However, with new satellite systems targeting especially methane (CH<sub>4</sub>) emissions on different scales, high-precision airborne measurements are needed to validate these satellite systems and detect and quantify emissions too small to be detected from space-based sensors.</p> <p>For this, the MAMAP2D family of airborne passive imaging remote sensing instruments has been and is being built at the Institute of Environmental Physics of the University of Bremen. MAMAP2D-Light, the first of this family, is a lightweight, compact spectrometer measuring carbon dioxide (CO<sub>2</sub>) and CH<sub>4</sub> enhancements in a short-wave infrared band around 1.6 µm with a spectral resolution of ~1.1 nm. It was flown successfully on a Diamond HK36 TTC-ECO motor glider aircraft of the Jade University of Applied Sciences in Wilhelmshaven and the High Altitude Long Range operations (HALO) aircraft of DLR during the COMET 2.0 Arctic campaign in Canada. The MAMAP2D instrument, the next biggest in the MAMAP2D family, covers the SWIR band with a higher spectral resolution and additionally contains a near-infrared channel covering O<sub>2</sub> absorption around 760 µm for path-length correction and is currently assembled in the laboratory.</p> <p>In this poster, we will present the spectral characterization of the MAMAP2D-Light instrument as flown during the COMET 2.0 Arctic campaign and assess its performance for detecting local CH<sub>4</sub> and CO<sub>2</sub> gradients. Additionally, initial laboratory characterizations of the MAMAP2D breadboarding activity will be presented.</p>

Anthropogenic greenhouse gas (GHG) emissions remain the main concern for global climate change. To reduce and mitigate those emissions both anthropogenic and natural sources must be identified and quantified. However, high northern latitude wetland regions may also overlap with, e.g., fossil fuel extraction sites. Consequently, commonly used passive satellite sensors are often challenged to observe and disentangle those emissions due to challenging illumination conditions and their large ground scene size, respectively.To investigate anthropogenic and wetland GHG emissions, a team of scientists deployed a comprehensive suite of instruments aboard the German Research aircraft HALO (High Altitude and Long Range Research) during the CoMet 2.0 Arctic mission conducted in Canada in August and September 2022. During the campaign, passive airborne remote sensing measurements by MAMAP2D-Light (Methane airborne mapper 2D light) were combined with active airborne remote sensing measurements by CHARM-F (CH4 Airborne Remote Monitoring – Flugzeug) and in situ GHG concentration measurements, also including an extensive suite of meteorological parameters.Those column and in-situ concentration observations of CH4 and CO2 will be used to identify and quantify emissions over a wide range of source types and scales in Canada (and Europe). This comprises single point source emissions (e.g., power plants), small areal sources such as landfills (e.g, the Valdemingomez and Pinto landfills in Madrid) and opencast coal mines, and extensive oil and gas exploration sites, including oil sand areas, which might be embedded in natural wetland regions or river deltas. The imaging capabilities of the MAMAP2D-Light instrument enable precise localisation of emissions and therefore mitigation strategies in the case of, e.g., leakages. This work will summarize and present first results and emission estimates from the CoMet 2.0 Arctic mission with a focus on localised emitters observed by the airborne imaging instrument MAMAP2D-Light.

<p>In order to reliably predict the climate of our planet, and to help inform political conventions on greenhouse gas emissions such as the Paris Agreement of 2015, adequate knowledge of both natural and anthropogenic sources of the greenhouse gases Carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>) and their feedbacks is needed. Despite the recognized importance of this issue, our current understanding about sources and sinks of CO<sub>2</sub> and CH<sub>4</sub> is still inadequate. This is particularly true for the Arctic, where large wetlands and permafrost areas constitute the most relevant but least quantified ecosystems for the global carbon budget.</p> <p>The CoMet 2.0 Arctic mission wants to help remedy this deficiency with a multi-disciplinary approach providing relevant measurements from Arctic regions using a suite of sophisticated scientific instrumentation onboard the German research aircraft HALO (High Altitude and LOng Range Research Aircraft, https://halo-research.de) to support state-of-the-art Earth System Models. At the same time, CoMet intends to support and improve current and future satellite missions, which still struggle to make high-quality measurements given the low sun elevation, low albedo, and adverse cloud conditions in the Arctic.</p> <p>CoMet 2.0 Arctic (https://comet2arctic.de/) has successfully been conducted within a six-week intensive operation period from August 10th to September 16th, 2022 targeting greenhouse gas emissions from boreal wetlands and permafrost areas in the Canadian Arctic, from wildfires, and from anthropogenic emission sources such oil, gas, and coal extraction sites and landfills.</p> <p>For that mission, HALO was equipped with a suite of remote sensing and in-situ instruments for the measurement of greenhouse gases and meteorological parameters. The remote sensing package comprised the CH<sub>4</sub> and CO<sub>2</sub> lidar CHARM-F (operated by DLR), the imaging spectrometer MAMAP2D-Light (operated by University of Bremen) and the hyperspectral imager specMACS (operated by LMU Munich). The remote sensors were supported by several in-situ instruments (operated by MPI Jena and DLR) to measure the main greenhouse gases and related trace species as well as an air sampler that collects air samples at flight level for later analysis (e.g. w.r.t. isotopes) in the laboratory. Furthermore, instruments to provide detailed information about the standard meteorological parameters (pressure, wind, humidity) were operated and several small meteorological sondes were launched in order to link those in-flight data to profiles.</p> <p>A total of 135 flight hours including a test flight to landfills in Spain and transfer flights from Europe have been performed. 16 scientific flights took place out of Edmonton, Alberta, to various regions all over Canada.</p> <p>CoMet 2.0 Arctic has partly been coordinated with the Arctic-Boreal Vulnerability Experiment field program by NASA (ABoVE, https://above.nasa.gov/). Both missions, ABoVE and CoMet 2.0 Arctic, are linked through the transatlantic initiative AMPAC (Arctic Methane and Permafrost Challenge, https://www.ampac-net.info/) that has recently been inaugurated by the US and European Space Agencies, NASA and ESA.</p> <p>Thus, a valuable data set was acquired to help better understand the methane and carbon dioxide cycles in the Arctic and emissions from natural and anthropogenic sources.</p>

A reduction of the anthropogenic emissions of CO2 (carbon dioxide) is necessary to stop or slow down man-made climate change. To verify mitigation strategies, a global monitoring system such as the envisaged European Copernicus anthropogenic CO2 monitoring mission (CO2M) is required. Those satellite data are going to be complemented and validated with airborne measurements. UAV (unmanned aerial vehicle) based measurements can provide a cost-effective way to contribute to these activities. Here we present the development of a sUAS (small unmanned aircraft system) to quantify the CO2 emissions of a nearby point source from its downwind mass flux without the need for any ancillary data. Specifically, CO2 is measured by an in situ NDIR (non-dispersive infrared) detector and the wind speed and direction is measured with a 2D ultrasonic acoustic resonance anemometer. In order to minimize the effect of rotor downwash, we calibrate the anemometer by analyzing wind measurements taken while following a suitable flight pattern and assuming stationary wind conditions. We quantify the quality of the CO2 and wind measurements with an in-flight validation at the ICOS (Integrated Carbon Observation System) atmospheric station Steinkimmen (STE) near Bremen, Germany. By means of two flights downwind of the ExxonMobil natural gas processing facility in Großenkneten about 40km east of Bremen, Germany, we demonstrate how the measurements of elevated CO2 concentrations can be used to infer mass fluxes of atmospheric CO2 related to the emissions of the facility.

Power plants are a major source of CO2 globally. Although their emissions are routinely monitored in many countries especially in the developed world, these numbers are often not publicly available and a complete global record is still far from reality. An important goal of Europe's planned Copernicus CO2 satellite mission CO2M is therefore to provide an independent quantification of power plant emissions worldwide. Emissions may be estimated from satellite XCO2 observations by simulating the plumes with an atmospheric transport model and finding those emissions that minimize a cost function of the differences between simulation and observations. Here we present a comparison of CO2 plume simulations from six high-resolution models, three Large Eddy Simulation models, two mesoscale Eulerian models, and one Lagrangian particle dispersion model. Simulations were conducted for two large coal-fired power plants, Belchatow in Poland and Janschwalde in Germany, which were extensively observed with aircraft in situ and remote sensing measurements during the CoMet campaign in May-June 2018. The observations provide a unique opportunity to study the capability of the models to simulate such plumes in a realistic manner and to design optimal modelling and emission quantification strategies. The Belchatow plume was sampled under highly convective and turbulent conditions whereas the Janschwalde plume was observed in a more stable weather situation. The models are able to reproduce these differences by simulating a highly structured turbulent plume for Belchatow and a more Gaussian-shaped plume for Janschwalde. However, the models differ in many details including the horizontal and vertical spread of the plumes, suggesting that in addition to resolution the specific choices of turbulence and advection scheme have a significant impact on the results. Our findings suggest that estimating emissions from individual images is particularly challenging for turbulent plumes. Since turbulence intensity evolves with the build-up of the convective boundary layer, a satellite overpass well before noon would likely be an advantage.

Methane is the second most important anthropogenic greenhouse gas in the Earth's atmosphere. To effectively reduce these emissions, a good knowledge of source locations and strengths is required. Airborne remote sensing instruments such as the Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) with meter-scale imaging capabilities are able to yield information about the locations and magnitudes of methane sources. In this study, we successfully applied the weighting function modified differential optical absorption spectroscopy (WFM-DOAS) algorithm to AVIRIS-NG data measured in Canada and the Four Corners region. The WFM-DOAS retrieval is conceptually located between the statistical matched filter (MF) and the optimal-estimation-based iterative maximum a posteriori DOAS (IMAP-DOAS) retrieval algorithm, both of which were already applied successfully to AVIRIS-NG data. The WFM-DOAS algorithm is based on a first order Taylor series approximation of the Lambert–Beer law using only one precalculated radiative transfer calculation per scene. This yields the fast quantitative processing of large data sets. We detected several methane plumes in the AVIRIS-NG images recorded during the Arctic-Boreal Vulnerability Experiment (ABoVE) Airborne Campaign and successfully retrieved a coal mine ventilation shaft plume observed during the Four Corners measurement campaign. The comparison between IMAP-DOAS, MF, and WFM-DOAS showed good agreement for the coal mine ventilation shaft plume. An additional comparison between MF and WFM-DOAS for a subset of plumes showed good agreement for one plume and some differences for the others. For five plumes, the emissions were estimated using a simple cross-sectional flux method. The retrieved fluxes originated from well pads, cold vents, and a coal mine ventilation shaft and ranged between (155 ± 71) kg (CH4) h−1 and (1220 ± 450) kg (CH4) h−1. The wind velocity was a significant source of uncertainty in all plumes, followed by the single pixel retrieval noise and the uncertainty due to atmospheric variability. The noise of the retrieved CH4 imagery over bright surfaces (>1 µW cm−2 nm−1 sr−1 at 2140 nm) was typically ±2.3 % of the background total column of CH4 when fitting strong absorption lines around 2300 nm but could reach over ±5 % for darker surfaces (< 0.3 µW cm−2 nm−1 sr−1 at 2140 nm). Additionally, a worst case large-scale bias due to the assumptions made in the WFM-DOAS retrieval was estimated to be ±5.4 %. Radiance and fit quality filters were implemented to exclude the most uncertain results from further analysis mostly due to either dark surfaces or surfaces where the surface spectral reflection structures are similar to CH4 absorption features at the spectral resolution of the AVIRIS-NG instrument.

Methane (CH4) is the second most important anthropogenic greenhouse gas, whose atmospheric concentration is modulated by human-induced activities, and it has a larger global warming potential than carbon dioxide (CO2). Because of its short atmospheric lifetime relative to that of CO2, the reduction of the atmospheric abundance of CH4 is an attractive target for short-term climate mitigation strategies. However, reducing the atmospheric CH4 concentration requires a reduction of its emissions and, therefore, knowledge of its sources. For this reason, the CO2 and Methane (CoMet) campaign in May and June 2018 assessed emissions of one of the largest CH4 emission hot spots in Europe, the Upper Silesian Coal Basin (USCB) in southern Poland, using top-down approaches and inventory data. In this study, we will focus on CH4 column anomalies retrieved from spectral radiance observations, which were acquired by the 1D nadir-looking passive remote sensing Methane Airborne MAPper (MAMAP) instrument, using the weighting-function-modified differential optical absorption spectroscopy (WFM-DOAS) method. The column anomalies, combined with wind lidar measurements, are inverted to cross-sectional fluxes using a mass balance approach. With the help of these fluxes, reported emissions of small clusters of coal mine ventilation shafts are then assessed. The MAMAP CH4 column observations enable an accurate assignment of observed fluxes to small clusters of ventilation shafts. CH4 fluxes are estimated for four clusters with a total of 23 ventilation shafts, which are responsible for about 40 % of the total CH4 mining emissions in the target area. The observations were made during several overflights on different days. The final average CH4 fluxes for the single clusters (or sub-clusters) range from about 1 to 9 t CH4 h−1 at the time of the campaign. The fluxes observed at one cluster during different overflights vary by as much as 50 % of the average value. Associated errors (1σ) are usually between 15 % and 59 % of the average flux, depending mainly on the prevailing wind conditions, the number of flight tracks, and the magnitude of the flux itself. Comparison to known hourly emissions, where available, shows good agreement within the uncertainties. If only emissions reported annually are available for comparison with the observations, caution is advised due to possible fluctuations in emissions during a year or even within hours. To measure emissions even more precisely and to break them down further for allocation to individual shafts in a complex source region such as the USCB, imaging remote sensing instruments are recommended.

Despite its key role for climate change, large uncertainties persist in our knowledge of the anthropogenic emissions of carbon dioxide (CO2) and no global observing system exists allowing to monitor emissions from localized CO2 sources with sufficient accuracy. The Orbiting Carbon Observatory-2 (OCO-2) satellite allows retrievals of the column-average dry-air mole fractions of CO2 (XCO2). However, regional column-average enhancements of individual point sources are usually small compared to the background concentration and its natural variability and often not much larger than the satellite’s measurement 5 noise. This makes the unambiguous identification and quantification of anthropogenic emission plume signals challenging. NO2 is co-emitted with CO2 when fossil fuels are combusted at high temperatures. It has a short lifetime of the order of hours so that NO2 columns often greatly exceed background and noise levels of modern satellite sensors near sources which makes it a suitable tracer of recently emitted CO2. Based on six case studies (Moscow, Russia; Lipetsk, Russia; Baghdad, Iraq; Medupi and Matimba power plants, South Africa; Australian wildfires; and Nanjing, China), we demonstrate the usefulness 10 of simultaneous satellite observations of NO2 and XCO2. For this purpose, we analyze co-located regional enhancements of XCO2 observed by OCO-2 and NO2 from the Sentinel-5 Precursor (S5P) satellite and estimate the CO2 plume’s cross-sectional fluxes. We take advantage of the nearly simultaneous NO2 measurements with S5P’s wide swath and small measurement noise by identifying the source of the observed XCO2 enhancements, excluding interference with remote upwind sources, allowing to adjust the wind direction, and by constraining the shape of the CO2 plumes. We compare the inferred cross-sectional 15 fluxes with the Emissions Database for Global Atmospheric Research (EDGAR), the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC), and, in the case of the Australian wildfires, with the Global Fire Emissions Database (GFED). The inferred cross-sectional fluxes range from 31 MtCO2/a to 153 MtCO2/a with uncertainties (1σ) between 23% and 72%. For the majority of analyzed emission sources, the estimated cross-sectional fluxes agree within their uncertainty with either EDGAR or ODIAC or lie in between them. We assess the contribution of multiple sources of uncertainty and find that the 20 dominating contributions are related to the computation of the effective wind speed normal to the plume’s cross-section. The ::: flux ::::::::::: uncertainties ::: are :::::::: expected :: to ::: be ::::::: reduced :: by :::: the planned European Copernicus anthropogenic CO2 monitoring mission (CO2M) ::::: which : will not only provide precise measurements with high spatial resolution but also imaging capabilities with a

Despite its key role in climate change, large uncertainties persist in our knowledge of the anthropogenic emissions of carbon dioxide (CO2) and no global observing system exists that allows us to monitor emissions from localized CO2 sources with sufficient accuracy. The Orbiting Carbon Observatory-2 (OCO-2) satellite allows retrievals of the column-average dry-air mole fractions of CO2 (XCO2). However, regional column-average enhancements of individual point sources are usually small, compared to the background concentration and its natural variability, and often not much larger than the satellite's measurement noise. This makes the unambiguous identification and quantification of anthropogenic emission plume signals challenging. NO2 is co-emitted with CO2 when fossil fuels are combusted at high temperatures. It has a short lifetime on the order of hours so that NO2 columns often greatly exceed background and noise levels of modern satellite sensors near sources, which makes it a suitable tracer of recently emitted CO2. Based on six case studies (Moscow, Russia; Lipetsk, Russia; Baghdad, Iraq; Medupi and Matimba power plants, South Africa; Australian wildfires; and Nanjing, China), we demonstrate the usefulness of simultaneous satellite observations of NO2 and XCO2. For this purpose, we analyze co-located regional enhancements of XCO2 observed by OCO-2 and NO2 from the Sentinel-5 Precursor (S5P) satellite and estimate the CO2 plume's cross-sectional fluxes. We take advantage of the nearly simultaneous NO2 measurements with S5P's wide swath and small measurement noise by identifying the source of the observed XCO2 enhancements, excluding interference with remote upwind sources, allowing us to adjust the wind direction, and by constraining the shape of the CO2 plumes. We compare the inferred cross-sectional fluxes with the Emissions Database for Global Atmospheric Research (EDGAR), the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC), and, in the case of the Australian wildfires, with the Global Fire Emissions Database (GFED). The inferred cross-sectional fluxes range from 31 MtCO2 a−1 to 153 MtCO2 a−1 with uncertainties (1σ) between 23 % and 72 %. For the majority of analyzed emission sources, the estimated cross-sectional fluxes agree, within their uncertainty, with either EDGAR or ODIAC or lie somewhere between them. We assess the contribution of multiple sources of uncertainty and find that the dominating contributions are related to the computation of the effective wind speed normal to the plume's cross section. The flux uncertainties are expected to be reduced by the planned European Copernicus anthropogenic CO2 monitoring mission (CO2M), which will provide not only precise measurements with high spatial resolution but also imaging capabilities with a wider swath of simultaneous XCO2 and NO2 observations. Such a mission, particularly if performed by a constellation of satellites, will deliver CO2 emission estimates from localized sources at an unprecedented frequency and level of accuracy.