Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods

This paper presents the development and application of a deep learning-based method for inverting CO2 atmospheric plumes from power plants using satellite imagery of the CO2 total column mixing ratios (XCO2). We present an end-to-end CNN approach, processing the satellite XCO2 images to derive estimates of the power plant emissions, that is resilient to missing data in the images due to clouds or to the partial view of the plume due to the limited extent of the satellite swath. The CNN is trained and validated exclusively on CO2 simulations from 8 power plants in Germany in 2015. The evaluation on this synthetic dataset shows an excellent CNN performance with relative errors close to 20 %, which is only significantly affected by substantial cloud cover. The method is then applied to 39 images of the XCO2 plumes from 9 power plants, acquired by the Orbiting Carbon Observatory-3 Snapshot Area Maps (OCO3-SAMs), and the predictions are compared to average annual reported emissions. The results are very promising, showing a relative difference of the predictions to reported emissions only slightly higher than the relative error diagnosed from the experiments with synthetic images. Furthermore, the analysis of the area of the images in which the CNN-based inversion extract the information for the quantification of the emissions, based on integrated gradient techniques, demonstrates that the CNN effectively identifies the location of the plumes in the OCO-3 SAM images. This study demonstrates the feasibility of applying neural networks that have been trained on synthetic datasets for the inversion of atmospheric plumes in real satellite imagery of XCO2, and provides the tools for future applications

Analysis of the potential of near-ground measurements of CO2 and CH4 in London, UK, for the monitoring of city-scale emissions using an atmospheric transport model

Carbon dioxide (CO2) and methane (CH4) mole fractions were measured at four near-ground sites located in and around London during the summer of 2012 with a view to investigating the potential of assimilating such measurements in an atmospheric inversion system for the monitoring of the CO2 and CH4 emissions in the London area. These data were analysed and compared with simulations using a modelling framework suited to building an inversion system: a 2 km horizontal resolution south of England configuration of the transport model CHIMERE driven by European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological forcing, coupled to a 1 km horizontal resolution emission inventory (the UK National Atmospheric Emission Inventory). First comparisons reveal that local sources, which cannot be represented in the model at a 2 km resolution, have a large impact on measurements. We evaluate methods to filter out the impact of some of the other critical sources of discrepancies between the measurements and the model simulation except that of the errors in the emission inventory, which we attempt to isolate. Such a separation of the impact of errors in the emission inventory should make it easier to identify the corrections that should be applied to the inventory. Analysis is supported by observations from meteorological sites around the city and a 3-week period of atmospheric mixing layer height estimations from lidar measurements. The difficulties of modelling the mixing layer depth and thus CO2 and CH4 concentrations during the night, morning and late afternoon lead to focusing on the afternoon period for all further analyses. The discrepancies between observations and model simulations are high for both CO2 and CH4 (i.e. their root mean square (RMS) is between 8 and 12 parts per million (ppm) for CO2 and between 30 and 55 parts per billion (ppb) for CH4 at a given site). By analysing the gradients between the urban sites and a suburban or rural reference site, we are able to decrease the impact of uncertainties in the fluxes and transport outside the London area and in the model domain boundary conditions. We are thus able to better focus attention on the signature of London urban CO2 and CH4 emissions in the atmospheric CO2 and CH4 concentrations. This considerably improves the statistical agreement between the model and observations for CO2 (with model–data RMS discrepancies that are between 3 and 7 ppm) and to a lesser degree for CH4 (with model–data RMS discrepancies that are between 29 and 38 ppb). Between one of the urban sites and either the rural or suburban reference site, selecting the gradients during periods wherein the reference site is upwind of the urban site further decreases the statistics of the discrepancies in general, though not systematically. In a further attempt to focus on the signature of the city anthropogenic emission in the mole fraction measurements, we use a theoretical ratio of gradients of carbon monoxide (CO) to gradients of CO2 from fossil fuel emissions in the London area to diagnose observation-based fossil fuel CO2 gradients, and compare them with the fossil fuel CO2 gradients simulated with CHIMERE. This estimate increases the consistency between the model and the measurements when considering only one of the two urban sites, even though the two sites are relatively close to each other within the city. While this study evaluates and highlights the merit of different approaches for increasing the consistency between the mesoscale model and the near-ground data, and while it manages to decrease the random component of the analysed model–data discrepancies to an extent that should not be prohibitive to extracting the signal from the London urban emissions, large biases, the sign of which depends on the measurement sites, remain in the final model–data discrepancies. Such biases are likely related to local emissions to which the urban near-ground sites are highly sensitive. This questions our current ability to exploit urban near-ground data for the atmospheric inversion of city emissions based on models at spatial resolution coarser than 2 km. Several measurement and modelling concepts are discussed to overcome this challenge

Development and deployment of a mid-cost CO2 sensor monitoring network to support atmospheric inverse modeling for quantifying urban CO2 emissions in Paris

To effectively monitor highly heterogeneous urban CO2 emissions using atmospheric observations, there is a need to deploy cost-effective CO2 sensors at multiple locations within the city with sufficient accuracy to capture the concentration gradients in urban environments. These dense measurements could be used as input of an atmospheric inversion system for the quantification of emissions at the sub-city scale or to separate specific sectors. Such quantification would offer valuable insights into the efficacy of local initiatives and could also identify unknown emission hotspots that require attention. Here we present the development and evaluation of a mid-cost CO2 instrument designed for continuous monitoring of atmospheric CO2 concentrations with a target accuracy of 1 ppm for hourly mean measurements. We assess the sensor sensitivity in relation to environmental factors such as humidity, pressure, temperature and CO2 signal, which leads to the development of an effective calibration algorithm. Since July 2020, eight mid-cost instruments have been installed within the city of Paris and its vicinity to provide continuous CO2 measurements, complementing the seven high-precision cavity ring-down spectroscopy (CRDS) stations that have been in operation since 2016. A data processing system, called CO2calqual, has been implemented to automatically handle data quality control, calibration and storage, which enables the management of extensive real-time CO2 measurements from the monitoring network. Colocation assessments with the high-precision instrument show that the accuracies of the eight mid-cost instruments are within the range of 1.0 to 2.4 ppm for hourly afternoon (12:00–17:00 UTC) measurements. The long-term stability issues require manual data checks and instrument maintenance. The analyses show that CO2 measurements can provide evidence for underestimations of CO2 emissions in the Paris region and a lack of several emission point sources in the emission inventory. Our study demonstrates promising prospects for integrating mid-cost measurements along with high-precision data into the subsequent atmospheric inverse modeling to improve the accuracy of quantifying the fine-scale CO2 emissions in the Paris metropolitan area.</p

Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets

Conventional calculations of the global carbon budget infer the land sink as a residual between emissions, atmospheric accumulation, and the ocean sink. Thus, the land sink accumulates the errors from the other flux terms and bears the largest uncertainty. Here, we present a Bayesian fusion approach that combines multiple observations in different carbon reservoirs to optimize the land (B) and ocean (O) carbon sinks, land use change emissions (L), and indirectly fossil fuel emissions (F) from 1980 to 2014. Compared with the conventional approach, Bayesian optimization decreases the uncertainties in B by 41% and in O by 46%. The L uncertainty decreases by 47%, whereas F uncertainty is marginally improved through the knowledge of natural fluxes. Both ocean and net land uptake (B + L) rates have positive trends of 29 ± 8 and 37 ± 17 Tg C⋅y−2 since 1980, respectively. Our Bayesian fusion of multiple observations reduces uncertainties, thereby allowing us to isolate important variability in global carbon cycle processes

Analyzing nitrogen dioxide to nitrogen oxide scaling factors for data-driven satellite-based emission estimation methods: A case study of Matimba/Medupi power stations in South Africa

In this paper, we propose improved nitrogen dioxide (NO2) to nitrogen oxide (NOx) scaling factors for several data-driven methods that are used for the estimation of NOx power plant emissions from satellite observations of NO2. The scaling factors are deduced from high-resolution simulations of power plant plumes with the MicroHH large-eddy simulation model with a simplified chemistry and then applied to Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) NO2 satellite observations over the Matimba/Medupi power stations in South Africa. We show that due to the non-linear chemistry the optimal NO2 to NOx scaling factors depend on both the method employed and the specific segments of the plume from which emission estimate is derived. The scaling factors derived from the MicroHH simulations in this study are substantially (more than 50%) higher than the typical values used in the literature with actual NO2 observations. The results highlight the challenge in appropriately accounting for the conversion from NO2 to NOx when estimating point source emissions from satellite NO2 observations

The CO2 Human Emissions (CHE) Project: First steps towards a European operational capacity to monitor anthropogenic CO2 emissions

The Paris Agreement of the United Nations Framework Convention on Climate Change is a binding international treaty signed by 196 nations to limit their greenhouse gas emissions through ever-reducing Nationally Determined Contributions and a system of 5-yearly Global Stocktakes in an Enhanced Transparency Framework. To support this process, the European Commission initiated the design and development of a new Copernicus service element that will use Earth observations mainly to monitor anthropogenic carbon dioxide (CO2) emissions. The CO2 Human Emissions (CHE) project has been successfully coordinating efforts of its 22 consortium partners, to advance the development of a European CO2 monitoring and verification support (CO2MVS) capacity for anthropogenic CO2 emissions. Several project achievements are presented and discussed here as examples. The CHE project has developed an enhanced capability to produce global, regional and local CO2 simulations, with a focus on the representation of anthropogenic sources. The project has achieved advances towards a CO2 global inversion capability at high resolution to connect atmospheric concentrations to surface emissions. CHE has also demonstrated the use of Earth observations (satellite and ground-based) as well as proxy data for human activity to constrain uncertainties and to enhance the timeliness of CO2 monitoring. High-resolution global simulations (at 9 km) covering the whole of 2015 (labelled CHE nature runs) fed regional and local simulations over Europe (at 5 km and 1 km resolution) and supported the generation of synthetic satellite observations simulating the contribution of a future dedicated Copernicus CO2 Monitoring Mission (CO2M

Caractérisation des erreurs de modélisation pour l'assimilation de données dans un modèle océanique régional du Golfe de Gascogne

A data assimilation system for ocean models, the SEEK (Singular Evolutive Extended Kalman) filter, is studied to control a Bay of Biscay configuration. This 1/15° configuration, nested in a 1/3° North Atlantic configuration, through the use of Open (sea) Boundaries Conditions, is developed using HYCOM (Hybrid Coordinate Ocean Model). This study focuses on the parametrization of the model error in the SEEK filter, and more generally in low rank Kalman filters, in order to control regional models. Classic parametrizations of these data assimilation systems, which have been developed initially for basin models, are not adapted to the regional dynamics complexity. Ensemble methods are used to get a realistic estimation of the model error due to bad determination of atmospheric and open boundary forcings. These forcings influence is supposed to be very important on regional dynamics. Model error statistics are characterized using the method of representers, which demonstrates the impact of the assimilation of various type of observations to control the oceanic state. The propagation of the error generated at open boundaries is weak. The use of the error due to atmospheric forcings to parameterize the SEEK filter for surface temperature assimilation experiments gives good results. Their comparison with those given by a more classical parametrization shows the benefits of this study on model error.Cette thèse porte sur l'application du filtre SEEK (Singular Evolutive Extended Kalman filter), un système d'assimilation de données pour les modèles océaniques, au contrôle d'une configuration du Golfe de Gascogne. Cette configuration au 1/15°, emboîtée dans une configuration au 1/3° de l'Atlantique Nord à travers l'emploi de Conditions aux Frontières Ouvertes (en mer), est développée à l'aide du modèle HYCOM (Hybrid Coordinate Ocean Model) à coordonnée verticale hybride. L'étude porte essentiellement sur la paramétrisation de l'erreur modèle dans le filtre SEEK, et plus généralement dans les filtres de Kalman de rangs réduits, pour le contrôle des modèles régionaux. Les paramétrisations classiques de ces systèmes d'assimilation, développés jusqu'à présent pour les modèles de bassin, sont inadaptées à la complexité de la dynamique régionale. On utilise des méthodes d'ensemble pour estimer de façon réaliste l'erreur modèle liée à la mauvaise détermination des forçages aux limites, forçages atmosphériques et CFO, dont l'influence est a priori très importante sur la dynamique régionale. La caractérisation des statistiques de l'erreur modèle est réalisée à l'aide de la méthode des représenteurs qui montre l'impact de l'assimilation de divers types d'observations pour le contrôle de l'état océanique. La propagation de l'erreur générée aux frontières ouvertes est faible. Les bons résultats donnés par l'emploi de l'erreur liée aux forçages atmosphériques, pour paramétrer le filtre SEEK dans des expériences d'assimilation de température de surface, que l'on compare à ceux donnés par une paramétrisation plus classique, montrent l'apport de cette étude sur l'erreur modèle