Integrated Methane Inversion (IMI 1.0): A user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations

Varon, Daniel J.; Jacob, Daniel J.; Sulprizio, Melissa P.; Estrada, Lucas; Downs, William B.; Shen, Lu; Hancock, Sarah E.; Nesser, Hannah; Qu, Zhen; Penn, Elise; Chen, Zichong; Lu, Xiao; Lorente, Alba; Tewari, Ashutosh; Randles, C. A.

doi:10.5194/gmd-2022-45

Cited by 3 publications

(2 citation statements)

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“…Accounting for the relatively high overhead of the LETKF computation in the methane case, our approach represents a factor of 52 reduction in computational cost relative to an equivalent analytical inversion. Because of computational cost savings, we envision CHEEREIO's methane data assimilation can serve as a global complement to the regional nested-grid simulations offered by the Integrated Methane Inversion (IMI), a similar software platform designed for analytical methane inversions [Varon et al, 2022] More work can be done to improve CHEEREIO and expand its capability. Although CHEEREIO is designed as a lightweight software wrapper that is accessible to the GEOS-Chem community, future development will incorporate software components from the Joint Effort for Data Assimilation Integration (JEDI), a C++ and Fortran-based platform for model-generic data assimilation [Trémolet and Auligné, 2020].…”

Section: Posterior Solution and Evaluationmentioning

confidence: 99%

“…Further improvements to the LETKF parallelization routine, in particular methods to share memory resources within Python, can also be applied to reduce I/O overhead, reduce memory use, and improve assimilation wall time. CHEEREIO can be ported on the cloud, taking advantage of GEOS-Chem and satellite data already hosted there [Zhuang et al, 2019[Zhuang et al, , 2020Varon et al, 2022], thus bringing compute capacity to big data rather than requiring cumbersome data downloads. Cloud implementation would facilitate the development of nearreal-time chemical data assimilation products for emissions monitoring and air quality forecasts.…”

Section: Posterior Solution and Evaluationmentioning

confidence: 99%

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CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model

Pendergrass

Jacob

Nesser

et al. 2023

Preprint

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Abstract. We present a versatile, powerful, and user-friendly chemical data assimilation toolkit for simultaneously optimizing emissions and concentrations of chemical species based on atmospheric observations from satellites or suborbital platforms. The CHemistry and Emissions REanalysis Interface with Observations (CHEEREIO) exploits the GEOS-Chem chemical transport model and a localized ensemble transform Kalman filter algorithm (LETKF) to determine the Bayesian optimal (posterior) emissions and/or concentrations of a set of species based on observations and prior information, using an easy-to-modify configuration file with minimal changes to the GEOS-Chem or LETKF code base. The LETKF algorithm readily allows for non-linear chemistry and produces flow-dependent posterior error covariances from the ensemble simulation spread. The object-oriented Python-based design of CHEEREIO allows users to easily add new observation operators such as for satellites. CHEEREIO takes advantage of the HEMCO modular structure of input data management in GEOS-Chem to update emissions from the assimilation process independently from the GEOS-Chem code. It can seamlessly support GEOS-Chem version updates and is adaptable to other chemical transport models with similar modular input data structure. A postprocessing suite combines ensemble output into consolidated NetCDF files and supports a wide variety of diagnostic data and visualizations. We demonstrate CHEEREIO’s capabilities with an out-of-the-box application, assimilating global methane emissions and concentrations at weekly temporal resolution and 2°x2.5° spatial resolution for 2019 using TROPOMI satellite observations. CHEEREIO achieves a 50-fold improvement in computational performance compared to the equivalent analytical inversion of TROPOMI observations.

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Section: Posterior Solution and Evaluationmentioning

confidence: 99%

Section: Posterior Solution and Evaluationmentioning

confidence: 99%

CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model

Pendergrass

Jacob

Nesser

et al. 2023

Preprint

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Urban methane emission monitoring across North America using TROPOMI data: an analytical inversion approach

Hemati,

Mahdianpari,

Nassar

et al. 2024

Sci Rep

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Monitoring methane emissions is crucial in mitigating climate change as it has a relatively short atmospheric lifetime of about 12 years and a significant radiative forcing impact. To measure the impact of methane-controlling policies and techniques, a deep understanding of methane emissions is of great importance. Remote sensing offers scalable approaches for monitoring methane emissions at various scales, from point-source high-resolution monitoring to regional and global estimates. The TROPOMI satellite instrument provides daily XCH4 data globally, offering the opportunity to monitor methane at a moderate spatial resolution with an acceptable level of sensitivity. To infer emissions from TROPOMI data, we used the prior emission estimates from global and national inventories and the GEOS-Chem chemical transport model to simulate atmospheric methane along with actual observations of TROPOMI. In this study, methane emissions from Toronto, Montreal, New York, Los Angeles, Houston, and Mexico City have been estimated using the analytical solution of Bayesian inversion using the cloud-based Integrated Methane Inversion (IMI) framework. Using the result from ensemble inversions, and city boundaries, the average total emissions were as follows: Toronto 230.52 Gg a−1, Montreal 111.54 Gg a−1, New York 144.38 Gg a−1, Los Angeles 207.03 Gg a−1, Houston 650.16 Gg a−1, and Mexico City 280.81 Gg a−1. The resulting gridded scale factors ranged from 0.22 to 6.2, implying methane prior emission underestimations in most of these cities. As such, this study underscores the key role of remote sensing in accurately assessing urban methane emissions, informing essential climate mitigation efforts.

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Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane

et al. 2022

Self Cite

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Abstract. We review the capability of current and scheduled satellite observations of atmospheric methane in the shortwave infrared (SWIR) to quantify methane emissions from the global scale down to point sources. We cover retrieval methods, precision and accuracy requirements, inverse and mass balance methods for inferring emissions, source detection thresholds, and observing system completeness. We classify satellite instruments as area flux mappers and point source imagers, with complementary attributes. Area flux mappers are high-precision (<1 %) instruments with 0.1–10 km pixel size designed to quantify total methane emissions on regional to global scales. Point source imagers are fine-pixel (<60 m) instruments designed to quantify individual point sources by imaging of the plumes. Current area flux mappers include GOSAT (2009–present), which provides a high-quality record for interpretation of long-term methane trends, and TROPOMI (2018–present), which provides global continuous daily mapping to quantify emissions on regional scales. These instruments already provide a powerful resource to quantify national methane emissions in support of the Paris Agreement. Current point source imagers include the GHGSat constellation and several hyperspectral and multispectral land imaging sensors (PRISMA, Sentinel-2, Landsat-8/9, WorldView-3), with detection thresholds in the 100–10 000 kg h−1 range that enable monitoring of large point sources. Future area flux mappers, including MethaneSAT, GOSAT-GW, Sentinel-5, GeoCarb, and CO2M, will increase the capability to quantify emissions at high resolution, and the MERLIN lidar will improve observation of the Arctic. The averaging times required by area flux mappers to quantify regional emissions depend on pixel size, retrieval precision, observation density, fraction of successful retrievals, and return times in a way that varies with the spatial resolution desired. A similar interplay applies to point source imagers between detection threshold, spatial coverage, and return time, defining an observing system completeness. Expanding constellations of point source imagers including GHGSat and Carbon Mapper over the coming years will greatly improve observing system completeness for point sources through dense spatial coverage and frequent return times.

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Integrated Methane Inversion (IMI 1.0): A user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations

Cited by 3 publications

References 30 publications

CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model

CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model

Urban methane emission monitoring across North America using TROPOMI data: an analytical inversion approach

Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane

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