Contributors: I Aben, S Kurchaba, AI Lopez-Norena, KL Louzada, and JD Maasakkers
History of modifications
Acronyms
Introduction
CAMS Methane Hot Spot service shows an overview of methane plumes from methane super-emitters detected using data from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor satellite. The presented plumes are detected using a two-step machine learning pipeline, following the methodology described in Schuit et al. (2023), and manually verified by two experts. The map available through methane-explorer shows approximate source locations based on single TROPOMI plumes and initial source rate estimates calculated using the Integrated Mass Enhancement (IME) method. Due to factors such as varying emissions, cloud cover, and viewing geometry, the number of detected plumes fluctuates from one week to another. The provided detections focus on plume-like structures and thus exclude larger-scale enhancements such as those seen over the Permian Basin or over wetland areas. While other source sectors, such as livestock, are responsible for a large fraction of total emissions, those emissions are not spatially concentrated enough to be detected as individual plumes with TROPOMI and therefore do not show up on the Methane Explorer.
Please note that delivered plumes have only been subject to initial verification, precise quantification and final interpretation require more extensive evaluation.
1. TROPOMI observations
The TROPOspheric Monitoring Instrument (TROPOMI) (Veefkind et al., 2012) on board ESA's Sentinel-5P satellite was launched in 2017. Among others, it observes atmospheric dry-air methane column mixing ratios with a pixel size down to 7 km × 5.5 km and daily global coverage, resulting in a point source detection limit down to ∼8 t h−1 (Schuit et al., 2023). Detections are made using operational TROPOMI Level-2 methane data product available from the Copernicus Data Space.
2. Source location estimation
For each detected plume, we estimate its source location based on the most upwind TROPOMI pixel included in the detected plume (Schuit et al., 2023). Because of TROPOMI's kilometer-scale resolution and uncertainty in the inclusion of pixels in the plume, this estimated location does usually not exactly align with the facility responsible for the detected plume. For transient emissions, where plumes can disconnect from their sources and drift through the atmosphere, the distance between the reported source location estimate and the true source can be larger.
3. Source rate quantification
The plumes' source rates are quantified using the Integrated Mass Enhancement (IME) method which integrates the observed methane enhancement in the plume with an effective wind speed derived from an ensemble of reanalysis wind data (Frankenberg et al., 2016; Varon et al., 2018) using ECMWF ERA5 10-m winds (Hersbach et al., 2020), GEOS FP 10-m winds, and GEOS FP planetary boundary layer (PBL) winds (Molod et al., 2012). The emission rate (Q) is calculated as follows:
where τ is the plume's atmospheric lifetime, Ueff is the effective wind speed, L is the plume length, and ∆Ω denotes the methane column mass enhancement above the local background of pixel j, with the footprint Aj (Schuit et al., 2023).
Reanalysis wind data from GEOS-FP is obtained from portal.nccs.nasa.gov and 10-m wind data from ECMWF as included in the TROPOMI level-2 methane data product.
4. Source type classification
Because of the limited spatial resolution of TROPOMI (7 km x 5.5 km), the exact sources underlying detected plumes can usually not be identified. We therefore report a most likely source type based on the relative size of different bottom-up emission products at the location of the detected plume. For a given plume, the reported source type is determined based on gridded bottom-up emission inventories in a 0.7 × 0.7 [deg] square centered around the estimated source location, reporting the largest source sector. Schuit et al. (2023) found that using a window of this size mitigates errors in the estimated source location and spatial errors in the emission inventories.
As bottom-up emission inventories, we use 2019 oil, gas, and coal emissions from the updated Global Fuel Exploitation Inventory (GFEI v2; Scarpelli et al., 2022) and 2018 solid waste emissions from EDGAR V6.0 (Crippa et al., 2021). We do not include sectors that have a low probability to produce point source emissions that can be detected in single overpass TROPOMI data, such as rice cultivation and livestock.
Following Schuit et al., we also do not use this approach to attribute detections to wetlands. Nevertheless, we study the fluxes from the 2019 WetCHARTs v1.3.1 ensemble (Bloom et al., 2021) to identify regions where detections might be influenced by strong wetland fluxes, as, for example, in central Africa (Pandey et al., 2021). Such detections over Africa (Based on ArcGIS continent maps) are reported under the category “Unclassified”, as apart from wetlands there could potentially be other emission sources that are not accounted for in the emission inventories but are contributing to the detected plumes.
Data organization, access and format
An interactive overview of the detected methane plumes is available through the methane-explorer application.
Figure 1: Methane Hotspots Explorer map.
The complete file of methane plumes detected since the starting date of the project (1 May 2024) is available as a single CSV file. The file is updated on a weekly basis.
Spatial grid
The delivered methane plumes are detected based on TROPOMI/S5P data with the pixel size down to 7 km x 5.5 km at nadir. Source location estimates are reported with a resolution of 0.01, but have significant uncertainty as described under 'Source location estimation'.
Temporal frequency
The TROPOMI instrument used for the detection of methane plumes has daily global coverage. Only clear-sky observations can be used to detect methane plumes.
Data update frequency
Detected plumes are uploaded on a weekly basis (starting from 1 May 2024). The number of detections fluctuates from week to week because of varying emissions, cloud cover, and viewing geometry.
Output data file content
Table 1, provides the available output data for each methane plume detection (also in the CSV file) :
Table 1: Output data per detected plume
| Name | Description | Units |
|---|---|---|
| date | Observation date | YYYYMMDD |
| time_UTC | Observation time | HH:MM:SS |
| lat | Estimates source location latitude | degrees |
| lon | Estimates source location longitude | degrees |
| source_rate_t/h | Estimated emission rate based on the emission quantification ensemble | tonnes per hour |
| uncertainty_t/h | Uncertainty of the emission rate based on the emission quantification ensemble | tonnes per hour |
| source_type | Likely emission source type | Coal, Oil, Gas, Landfill (urban), and Unclassified |
| source_country | *Country where the emission was detected |
*The attribution to a specific country was performed based on ArcGIS country maps, accessed: February 2024.
Guidelines
Unavailability or delay of the NOAA's Suomi-NPP VIIRS cloud data used in the TROPOMI level-2 methane data product may result in the delay of the delivery of methane plumes. In the production of the TROPOMI level-2 methane data product, there is a 3-day waiting period for the VIIRS cloud data to become available. After that, if the VIIRS cloud data is still absent, an alternative cloud product based solely on TROPOMI observations is used (Borsdorff et al., 2024). As a result, the detection of plumes based on these orbits can be delayed with results reported in the next week.
References
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Borsdorff, T.; Martinez-Velarte, M.C.; Sneep, M.; ter Linden, M.; Landgraf, J. Random Forest Classifier for Cloud Clearing of the Operational TROPOMI XCH4 Product. Remote Sens., 16, 1208. https://doi.org/10.3390/rs16071208, 2024.
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