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Atmospheric emissions from anthropogenic hotspots, i.e., cities, power plants and industrial facilities, can be determined from remote sensing images obtained from airborne and space-based imaging spectrometers. In this paper, we present a Python library for data-driven emission quantification (ddeq) that implements various computationally light methods such as the Gaussian plume inversion, cross-sectional flux method, integrated mass enhancement method and divergence method. The library provides a shared interface for data input and output and tools for pre- and post-processing of data. The shared interface makes it possible to easily compare and benchmark the different methods. The paper describes the theoretical basis of the different emission quantification methods and their implementation in the ddeq library. The application of the methods is demonstrated using Jupyter notebooks included in the library, for example, for NO 2 images from the Sentinel-5P/TROPOMI satellite and for synthetic CO 2 and NO 2 images from the Copernicus CO 2 Monitoring (CO2M) satellite constellation. The library can be easily extended for new datasets and methods, providing a powerful community tool for users and developers interested in emission monitoring using remote sensing images. [ABSTRACT FROM AUTHOR]

In this paper, we present the new multi-wavelength dataset of aerosol extinction profiles, which are retrieved from the averaged transmittance spectra by the Global Ozone Monitoring by Occultation of Stars instrument aboard the Envisat satellite. Using monthly and zonally averaged transmittances as a starting point for the retrievals enables us to improve the signal-to-noise ratio and eliminate possible modulation of transmittance spectra by uncorrected scintillations. The two-step retrieval method is used: the spectral inversion is followed by the vertical inversion. The spectral inversion relies on the removal of contributions from ozone, NO2 , NO3 and Rayleigh scattering from the optical depth spectra for each ray perigee altitude. In the vertical inversion, the profiles of aerosol extinction coefficients at several wavelengths are retrieved from the collection of slant aerosol optical depth profiles. The retrieved aerosol extinction profiles (FMI-GOMOSaero dataset v1) are provided in the altitude range 10–40 km at wavelengths of 400, 440, 452, 470, 500, 525, 550, 672 and 750 nm for the whole GOMOS operating period from August 2002 to March 2012. Extensive intercomparisons of the retrieved FMI-GOMOSaero aerosol profiles with aerosol profile data from other satellite instruments at several wavelengths have been performed. It is found that the average difference between FMI-GOMOSaero and other datasets is within 20 %–40 % in the lower and middle stratosphere, the standard deviation is ∼ 20 %–50 %, and the correlation coefficient of the time series is 0.65–0.85. The created FMI-GOMOSaero dataset can be used in merged datasets of stratospheric aerosols. It might be also used as a priori information for satellite retrievals during 2002–2012. [ABSTRACT FROM AUTHOR]

The largest anthropogenic emissions of carbon dioxide (CO2) come from local sources such as cities and power plants. The upcoming Copernicus CO2 Monitoring Mission (CO2M) will provide satellite images of the CO2 and NO2 plumes associated with these sources at a resolution of 2 km x 2 km and with a swath of 250 km. These images could be exploited with atmospheric plume inversion methods to estimate local CO2 emissions at the time of the satellite overpass and the corresponding uncertainties. To support the development of the operational processing of satellite column-average XCO2 and NO2 imagery, this study evaluates "data-driven inversion methods", i.e., computationally light inversion methods that directly process information from satellite images, local winds and meteorological data, without resorting to computationally expensive dynamical atmospheric transport models. We have designed an objective benchmarking exercise to analyse and compare the performance of five different data-driven inversion methods: two implementations with different complexity for the cross-sectional flux approach (CSF and LCSF) and one implementation for the Integrated Mass Enhancement (IME), the Divergence (Div) and the Gaussian Plume model inversion (GP) approaches. This exercise is based on pseudo-data experiments with simulations of synthetic "true" emissions, meteorological and concentration fields, and CO2M observations in a domain of 750 km x 650 km centred on Eastern Germany over 1-year. The performance of the methods is quantified in terms of accuracy in the single-image (from individual images) or annual average (from the full series of images) emission estimates and in terms of number of instant estimates for the city of Berlin and 15 power plants in this domain. Several ensembles of estimations are conducted, using different scenarios for the available synthetic datasets. These ensembles are used to analyse the sensitivity of the performance to the loss of data due to cloud cover, to the uncertainty in the wind or to the added value of simultaneous NO2 images. The GP and the LCSF methods generate the most accurate estimates from individual images with similar Interquartile Ranges (IQR) in the deviations between the emission estimates and the true emissions between ~20 % and ~60 % for all scenarios. When taking the cloud cover into account, these methods produce respectively 274 and 318 instant estimates from the *500 daily images that cover significant portions of the plumes from the sources. Filtering the results based on the associated uncertainty estimates can improve the statistics of the IME and CSF methods, but at the cost of a large decrease in the number of estimates. Due to a reliable estimation of uncertainty and thus a suitable selection of estimates, the CSF method achieves similar if not better statistics of accuracy for instant estimates compared to the GP and LCSF methods after filtering. In general, the performances for retrieving single-image estimates are improved when, in addition to XCO2 data, collocated NO2 data are used to characterise the structure of plumes. With respect to the estimates of annual emissions, the root mean square errors (RMSE) are for the most realistic benchmarking scenario 20 % (GP), 27 % (CSF), 31 % (LCSF), 55 % (IME) and 79 % (Div). This study suggests that the Gaussian plume and/or the cross-sectional approaches are currently the most efficient tools to provide estimates of CO2 emissions from satellite images and their relatively light computational cost will enable analysis of the massive amount of data provided by future missions of satellite XCO2 imagery. [ABSTRACT FROM AUTHOR]

In this paper, we present the new multi-wavelength dataset of aerosol extinction profiles, which are retrieved from the averaged transmittance spectra by the Global Ozone Monitoring by Occultation of Stars instrument on board the Envisat satellite. Using monthly and zonally averaged transmittances as a starting point for the retrievals enables us to improve the signal-to-noise ratio and eliminate possible modulation of transmittance spectra by uncorrected scintillations. The two-step retrieval method is used: the spectral inversion is followed by the vertical inversion. The spectral inversion relies on the removal of contributions from ozone, NO2, NO3 and Rayleigh scattering from the optical depth spectra, for each ray perigee altitude. In the vertical inversion, the profiles of aerosol extinction coefficients at several wavelengths are retrieved from the collection of slant aerosol optical depth profiles. The retrieved aerosol extinction profiles (FMI-GOMOSaero dataset v1) are provided in the altitude range 10-40 km at wavelengths 400, 440, 452, 470, 500, 525, 550, 672 and 750 nm, for the whole GOMOS operating period from August 2002 to March 2012. The retrieved aerosol extinction profiles show a realistic wavelength dependence. Intercomparisons have shown that FMI-GOMOSaero aerosol profiles are in good agreement with other datasets and have suggested a better data quality compared to the previous GOMOS aerosol data. [ABSTRACT FROM AUTHOR]

To be able to meet global grand challenges (climate change; biodiversity loss; environmental pollution; scarcity of water, food and energy supplies; acidification; deforestation; chemicalization; pandemics), which all are closely interlinked with each other, we need comprehensive open data with proper metadata. The large data sets from ground-base in situ observations, ground and satellite remote sensing and multiscale modelling need to be utilized seamlessly. In this opinion paper, we describe the SMEAR (Station for Measuring Earth surface – Atmosphere Relations) concept. We also demonstrate its power via several examples, such as detection of new particle formation and their subsequent growth, quantifying atmosphere-ecosystem feedback loops, combining comprehensive observations with emergency science and services, as well as studying the effect of COVID restrictions on different air quality and climate variables. The future needs and the potential of comprehensive observations of the environment are summarized. [ABSTRACT FROM AUTHOR]

In this paper, we present the updated SAGE-CCI-OMPS + climate data record of monthly zonal mean ozone profiles. This dataset covers the stratosphere and combines measurements by nine limb and occultation satellite instruments – SAGE II (Stratospheric Aerosol and Gases Experiment II), OSIRIS (Optical Spectrograph and InfraRed Imaging System), MIPAS (Michelson Interferometer for Passive Atmospheric Sounding), SCIAMACHY (SCanning Imaging Spectrometer for Atmospheric CHartographY), GOMOS (Global Ozone Monitoring by Occultation of Stars), ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer), OMPS-LP (Ozone Monitor Profiling Suite Limb Profiler), POAM (Polar Ozone and Aerosol Measurement) III, and SAGE III/ISS (Stratospheric Aerosol and Gases Experiment III on the International Space Station). Compared to the original version of the SAGE-CCI-OMPS dataset (Sofieva et al., 2017b), the update includes new versions of MIPAS, ACE-FTS, and OSIRIS datasets and introduces data from additional sensors (POAM III and SAGE III/ISS) and retrieval processors (OMPS-LP). In this paper, we show detailed intercomparisons of ozone profiles from different instruments and data versions, with a focus on the detection of possible drifts in the datasets. The SAGE-CCI-OMPS + dataset has a better coverage of polar regions and of the upper troposphere and the lower stratosphere (UTLS) than the previous dataset. We also studied the influence of including new datasets on ozone trends, which are estimated using multiple linear regression. The changes in the merged dataset do not change the overall morphology of post-1997 ozone trends; statistically significant trends are observed in the upper stratosphere. The largest changes in ozone trends are observed in polar regions, especially in the Southern Hemisphere. The updated SAGE-CCI-OMPS + dataset contains profiles of deseasonalized anomalies and ozone concentrations from 1984 to 2021, in 10 ∘ latitude bins from 90 ∘ S to 90 ∘ N and in the altitude range from 10 to 50 km. The dataset is open access and available at https://climate.esa.int/en/projects/ozone/data/ (last access: 9 March 2023) and at ftp://cci_web@ftp-ae.oma.be/esacci (ESA Climate Office; last access: 9 March 2023). [ABSTRACT FROM AUTHOR]

Recent advances in satellite observations of methane provide increased opportunities for inverse modeling. However, challenges exist in the satellite observation optimization and retrievals for high latitudes. In this study, we examine possibilities and challenges in the use of the total column averaged dry-air mole fractions of methane ( XCH 4 ) data over land from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite in the estimation of CH 4 fluxes using the CarbonTracker Europe- CH 4 (CTE- CH 4 ) atmospheric inverse model. We carry out simulations assimilating two retrieval products: Netherlands Institute for Space Research's (SRON) operational and University of Bremen's Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). For comparison, we also carry out a simulation assimilating the ground-based surface data. Our results show smaller regional emissions in the TROPOMI inversions compared to the prior and surface inversion, although they are roughly within the range of the previous studies. The wetland emissions in summer and anthropogenic emissions in spring are lesser. The inversion results based on the two satellite datasets show many similarities in terms of spatial distribution and time series but also clear differences, especially in Canada, where CH 4 emission maximum is later, when the SRON's operational data are assimilated. The TROPOMI inversions show higher CH 4 emissions from oil and gas production and coal mining from Russia and Kazakhstan. The location of hotspots in the TROPOMI inversions did not change compared to the prior, but all inversions indicated spatially more homogeneous high wetland emissions in northern Fennoscandia. In addition, we find that the regional monthly wetland emissions in the TROPOMI inversions do not correlate with the anthropogenic emissions as strongly as those in the surface inversion. The uncertainty estimates in the TROPOMI inversions are more homogeneous in space, and the regional uncertainties are comparable to the surface inversion. This indicates the potential of the TROPOMI data to better separately estimate wetland and anthropogenic emissions, as well as constrain spatial distributions. This study emphasizes the importance of quantifying and taking into account the model and retrieval uncertainties in regional levels in order to improve and derive more robust emission estimates. [ABSTRACT FROM AUTHOR]