The TROPOMI instrument onboard Sentinel-5 Precursor (S5P) satellite provides global methane concentrations since October 2017 at an unprecedented spatial and temporal resolution, and data over land has proved to be of highest quality, fundamental to studies that estimate global and regional methane emissions. Measurements over ocean under sun-glint geometries contribute to an improvement of the coverage of the TROPOMI column averaged dry air volume mixing ratio methane (XCH4) data, enhancing the monitoring capabilities of the instrument. In this contribution we present for the first time a full assessment of ocean measurements for three years which we validate with ground-based and satellite measurements. We compare the data to results from a non-scattering retrieval to identify challenging scenes where non accounted scattering might be a source of error in the RemoTeC full-physics retrieval algorithm. Furthermore, we assess the consistency of the retrieved methane for different spectral windows in the shortwave-infrared spectral range for scenes where the scattering effects are negligible using the ‘upper edge method’.

Over land, interference of remotely sensed methane concentrations with complex surface features over specific geographical areas still imposes a challenge for the retrieval algorithm. Spectral variations in the surface reflectance can be accounted for by fitting higher order polynomial in the inversion. Given the optimal spectral resolution of the TROPOMI instrument, we show that a third order polynomial fit in the shortwave-infrared (SWIR, 2305-2385 nm) spectral range is optimal to remove interferences, resulting in an improved fitting quality and more realistic methane column concentration retrieved over specific areas around the globe.

Through multiple mechanisms, the Government of Canada is acquiring up to 100 scenes from GHGSat, a private company that has developed and launched three commercial, high spatial resolution methane-sensing nano-satellites. GHGSat targets a location, acquires 1.6 um spectra across a 12x12 km2 scene at a 25-50 m spatial resolution using a Fabry-Perot spectrometer, and from this derives excess-methane for each pixel in the scene. If a plume is detected, GHGSat then estimates the methane emission rate and its uncertainty using an Integrated Mass Enhancement approach. Beginning with excess methane scenes, the goal of this project is to independently evaluate the quality of GHGSat observations, with a focus on understanding detection limits and emissions accuracy, and ultimately its utility for methane emissions monitoring in Canada. This paper will describe results from an initial evaluation, including quantifying the precision of the excess-methane for all scenes acquired to date, and how their precision varies with factors such as reflectivity and solar zenith angle. Precision is important as it significantly influences the emissions rate detection limit. Further, the GHGSat emissions algorithm will be implemented as completely as possible and applied to scenes with identified plumes. Alternative emissions algorithms will also be applied to understand how emissions rates vary among them and the strengths and weaknesses of each method. The final activity is generating synthetic GHGSat observations using the MLDP (Modèle Lagrangien de dispersion de particules) dispersion model run at high spatial resolution together with GHGSat instrument characteristics such as spatial resolution and calculated precision. These synthetic observations will be used to further understand GHGSat performance through different emissions estimation methodologies and detection limits, particularly at locations for which GHGSat scenes are not available.

The ESA Methane+ project investigates synergies between SWIR and TIR retrieval approaches, using data from TROPOMI and IASI, and their application in global inverse modelling of methane. The consistency of the information provided by SWIR and TIR retrievals is evaluated using vertical profile information from the TM3 and TM5 models, supported further by independent in situ measurements. This approach is important for SWIR and TIR retrievals, because of their different vertical sensitivities, complicating a direct comparison of retrievals. The use of two transport models, and two retrieval datasets for the SWIR (RemoTeC and WFMD) and TIR (LMD and RAL) retrievals, helps in distinguishing robust variations in methane from uncertainties in transport models and satellite retrievals.

Global methane inversions have been performed using the four retrieval datasets for a two-year period starting from the beginning of the operational processing of TROPOMI data in spring 2018, until the first months of 2020. The results show an encouraging level of consistency between the datasets that are compared, which can only partly be explained by the bias correction scheme that is used, linking global scale variations to those observed by the surface network.
The comparison between inversion results for 2019 and 2020 is interesting a particular, because of the exceptional rapid increase in global CH4 during the COVID-19 pandemic reported by the global surface network. Inversions using either surface measurements or satellite data, show differences in the regional attribution of the global methane increase in this period. In contrast, the inversions using satellite data are in relatively good agreement with each other, although it is difficult to relate the inversion-derived emission changes to specific processes. This might be due to the inversion setup, which so far only allows optimization of emissions, whereas variations in the OH sink have been hypothesized as a potential important cause of the 2020 CH4 growth rate increase.
In the presentation, we will present the main outcomes of the Methane+ project and discuss that added value of combining SWIR and TIR satellite data.

The thermal infrared nadir spectra of IASI (Infrared Atmospheric Sounding Interferometer) are successfully applied for retrievals of different atmospheric trace gas profiles. However, these retrievals offer generally reduced information about the lowermost tropospheric layer due to the lack of thermal contrast close to the surface. Earth surface reflected solar spectra observed in the short wave infrared, for instance by TROPOMI (TROPOspheric Monitoring Instrument) offer higher sensitivity near ground and are used for the retrieval of total column averaged mixing ratios of a variety of atmospheric trace gases. Here we present a method for the synergetic use of IASI profile and TROPOMI total column data. Our method uses the output of the individual retrievals and consists of linear algebra a posteriori calculations in form of a Kalman filter (i.e. calculation after the individual retrievals). We show that this approach is mathematically very similar to applying the spectra of the different sensors together in a single retrieval procedure, but with the substantial advantage of being usable together with different individual retrieval processors, of being very time efficient, and of directly benefiting from the high quality and most recent improvements of the individual retrieval processors.

We apply the method to atmospheric methane (CH4) and use IASI products generated by the processor MUSICA (MUlti-platform remote Sensing of Isotopologues for the investigation of the Cycle of Atmospheric water). We perform a theoretical evaluation and show that the level 2 data combination method yields total column averaged CH4 products (XCH4) that have a slightly improved sensitivity if compared to the respective TROPOMI products and upper tropospheric and lower stratospheric (UTLS) CH4 profile data with the same good sensitivity as the IASI product. In addition, the combined product offers sensitivity for the tropospheric partial column, which is not provided by the individual TROPOMI nor the individual IASI product. This theoretically predicted synergetic effect is verified by comparisons to CH4 reference data obtained from TCCON (Total Carbon Column Observing Network), AirCore soundings, and Global Atmospheric Watch (GAW) mountain stations. The comparisons clearly demonstrate that the combined product can reliably detect XCH4 signals and allows to distinguish between tropospheric and UTLS CH4 partial column averaged mixing ratios, which is not possible by the individual TROPOMI and IASI products. The approach has the particular attraction, that IASI and TROPOMI successor instruments will be jointly aboard the upcoming Metop Second Generation satellites (guaranteeing observations from the 2020s to the 2040s). There will be more than 1 million globally distributed and perfectly collocated observations (over land) of IASI and TROPOMI successor instruments per day, for which combined products can be generated in a computationally very efficient way.

We disucss two fields of application of this new methane profile data. First, we use the data for a space-based estimation of local methane emission rates from European waste disposal sites and coal mines. Second, we show the usefulness of this combination method for a very efficient identification of outliers in the TROPOMI XCH4 data on a global scale.

Methane (CH4) is the second most important atmospheric greenhouse gas after carbon dioxide (CO2). Global concentrations of CH4 have been rising in the last decade and our understanding of what is driving the increase remains incomplete. Although there are significant global anthropogenic emissions of CH4 such as those from fossil fuel use, large natural sources of CH4 such as wetlands contribute to the uncertainty surrounding the CH4 budget. The combination of CH4’s high contribution to radiative forcing and its relatively short lifetime (approximately 9 years) means that reducing its anthropogenic sources could partially mitigate the human contribution to climate change on a relatively short timescale whilst global emissions of CO2 are gradually reduced.

The largest global mean annual increase of CH4 in the atmosphere on record (~15 ppb) was observed during the year 2020. This was a unique year due to the global pandemic with atmospheric CH4 concentrations continuing to rise despite a reduction in economic activity.

In this study we investigate the global and regional growth rate of CH4 during 2020 using high-resolution observations of column CH4 from the Tropospheric Monitoring Instrument (TROPOMI) on Sentinel 5P and a three-dimensional chemical transport model, TOMCAT. TROPOMI provides a unique opportunity to explore the anthropogenic and biospheric factors driving the large increase in atmospheric CH4 during 2020 due to its regular global coverage and high horizontal resolution, relative to previous satellites such as GOSAT. Through comparison with TOMCAT simulations of CH4 in 2020 with preceding years, we isolate the geographical regions driving the atmospheric increase. We use these findings to infer how different anthropogenic CH4 emission sectors might have been affected during these atypical economic and social conditions and whether natural sources, unaffected by the pandemic, contributed to the increase.

TROPOMI observations show that during September, October & November (SON) 2020 there were large emissions of CH4 over eastern Africa, India and China. The concentrations over these regions peaked during the SON season. Concentrations over some parts of eastern Africa were more than 75 ppb higher in 2020 than in 2019, relative to the global mean annual growth rate. CH4 emissions over this region were already unusually high during 2019 and these observations suggest that emissions continued to rise in this region during the 2020 SON season. There were also above-average increases in CH4 in China during SON and in the boreal northern hemisphere during SON, March, April & May (MAM) and December, January & February (DJF). Comparisons of TROPOMI with TOMCAT show that prior bottom-up emission estimates overestimate the growth in CH4 during 2020. TOMCAT simulations overestimate CH4 in regions such as western Russia, northern China, Ethiopia and Somalia. The model also underestimates CH4 in the Sudan region where observed concentrations were unusually high 2019. In each of these disparate regions there were large CH4 concentrations for 2020, and when compared to the already significant observed global mean increase during that year it indicates that a mixture of natural and anthropogenic sources were responsible for the large increase in concentrations. These high concentrations during SON 2020 could be related to resuming economic activity after lockdowns, above average temperatures in boreal autumn and increased rainfall and flooding in Africa.

Since its launch on May 4, 2019, the OCO-3 instrument has collected millions of CO2 observations globally, including dense, fine-scale XCO2 Snapshot Area Maps (SAMs) of emission hotspots like cities, power plants, and volcanoes. In 2020, NASA released the first public version of the OCO-3 XCO2 data, called vEarly. The intent of the vEarly product was not only to evaluate early mission performance but also to identify key areas to improve for future data releases. Here, we present an overview of the new and improved OCO-3 V10 XCO2 data product.

Compared to vEarly, the V10 data product uses an advanced radiometric calibration procedure which accounts for lamp degradation and icing on the instrument’s detectors. Further, accurate information about the instrument’s pointing reduces geolocation errors to less than 0.3 km, which mitigates XCO2 errors in regions with large topographic variations. A new set of quality filters was derived which increases the number of identified good quality soundings compared to vEarly for all observational modes. The optimized post-processing bias correction accounts for footprint biases and spurious variability in XCO2 correlated with retrieval parameters (parametric bias), and reduces swath biases that were apparent in OCO-3’s vEarly SAM and target mode observations. Direct comparisons against TCCON, models, and a small area truth proxy indicate that the OCO-3 V10 data product is of comparable quality to the OCO-2 V10 data.

This presentation will discuss improvements and changes in the new OCO-3 V10 data product, as well as comparisons against independent truth metrics.