the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Nov 2025
Integrating MethaneAIR aircraft and TROPOMI satellite observations in the Integrated Methane Inversion (IMI) to optimize methane emissions
Abstract. The MethaneAIR aircraft remote sensing instrument observes methane dry air column mixing ratios (XCH4) over ~100×100 km2 scenes with sub-km resolution, from which methane emissions can be inferred by inverse analysis with an atmospheric transport model. It emulates the MethaneSAT satellite instrument launched in March 2024 to quantify emissions from oil/gas production regions. We show here how the single day MethaneAIR observations can be integrated with the global continuous but relatively coarse and sparse observations from the TROPOMI satellite instrument into a common Integrated Methane Inversion (IMI) platform for optimizing methane emissions. The IMI, originally designed for TROPOMI, is used here with 12×12 km2 spatial resolution and lognormal error probability density functions (PDFs) for prior estimates. Application to two scenes in oil/gas production basins of the western US shows remarkable consistency between independent MethaneAIR (single day) and TROPOMI (monthly) inversions including for emission hotspots, with some differences that may reflect temporal variability of emissions. The IMI is able to optimize emissions even when starting from a very poor prior estimate. Using TROPOMI inversion results as prior estimate improves the MethaneAIR inversions by correcting emissions upwind of the MethaneAIR observation scenes and by adding information to the original prior estimate.
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Status: open (until 26 Jan 2026)
- RC1: 'Comment on egusphere-2025-4626', Anonymous Referee #1, 02 Jan 2026 reply
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- 1
This study presents a timely and strategic application of the Integrated Methane Inversion (IMI) framework, combining high-resolution MethaneAIR observations with the global context provided by TROPOMI. This approach offers a potential pathway for processing data from future missions with high-resolution measurements. The manuscript is well-structured, and the logic is easy to follow. Several points of clarification on the inversion strategy and the consistency between observing systems would strengthen both the methodology and the discussion.
Major comments:
1. The study adopts a step-wise approach, using the TROPOMI posterior as a prior for the MethaneAIR inversion. The authors mention that concatenating data was avoided due to temporal mismatches. However, have the authors considered or tested a joint inversion where both observation vectors are included in a single cost function with appropriate error covariance matrices as used in the paper "Global methane budget and trend, 2010–2017: Complementarity of inverse analyses using in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) observations"(https://acp.copernicus.org/articles/21/4637/2021/)? A discussion on why the step-wise approach is superior or more practical in this context would be valuable.
2. In the Permian case (RF06, Figure 1), TROPOMI XCH4 is significantly lower (11 ppb on average) than MethaneAIR. It is surprising that the independent inversions yield nearly identical total emissions (84.8 vs. 85.9 t/h) despite this substantial systematic offset. Could the authors clarify how the inversion achieves this consistency? Could this result primarily be driven by the adjustment of boundary conditions to compensate for the observational bias?
3. The coefficient 4.7 in equation (3) is unclear. Is this a fitted slope from the resampling experiment? Moreover, given that RF06 and RF08 likely differ in spatial heterogeneity, the authors should justify whether this single empirical constant is applicable to both flights, or if flight-specific coefficients would be more appropriate.
Minor comments:
1. The Introduction (Line 39) states that TROPOMI provides continuous data at a resolution of 5.5×7 km2. While this is true for this study period (2021), TROPOMI XCH4 products were initially provided with a 7×7 km2 resolution and switched to 5.5×7 km2 on August 6, 2019. This context should be corrected.
2. Please provide the specific temporal information and native spatial resolution for the GHGI and Omara et al. (2024) emission inventories.
3. MethaneAIR observations occur in the morning (10:00–12:00 LT), whereas the TROPOMI overpass typically occurs in the early afternoon (~13:30 LT). Apart from the transport errors mentioned, how are diurnal variations in emissions and boundary layer height accounted for when comparing or integrating these two datasets?
4. When aggregating very-high-resolution MethaneAIR pixels to the 12-km grid, is strict mass conservation or column-weighting maintained?