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Global emissions inventories for methane have been determined through international agreements. However, evaluations of reported country inventories are limited by spatially variable biases and limited observation sensitivity. Additionally, there is poorly characterized information content. To address these limitations, a study by Worden et al. [2023] used Bayesian, optimal estimation (OE) to evaluate a state-of-the-art inventory (EDGAR v6.0) using satellite-based emissions data from 2009 to 2018.

The study found robust differences between satellite and EDGAR data for total livestock, rice, and coal emissions. Both satellite and EDGAR show that livestock emissions are increasing primarily in the Indo-Pakistan, sub-tropical Africa, and the Southern Brazilian regions. East Asia rice emissions are also increasing. However, low information content for waste and fossil emission trends makes it difficult to compare EDGAR and satellite data accurately. The authors suggest that increased sampling and spatial resolution of satellite observations are needed to address these challenges.

This research highlights the importance of using atmospheric methane data to verify methane inventories and trends. The Bayesian, optimal estimation approach provides valuable insights into uncertainty reduction, sectoral attribution, and spatial resolution. By disentangling the effect of smoothing error, this method improves the evaluation process.

Overall, this study contributes to our understanding of methane emissions and the limitations of current inventories. It emphasizes the need for improved data collection and analysis methods to accurately assess and track global methane emissions.

The post Verifying Methane Inventories and Trends with Atmospheric Methane Data appeared first on ISP Today.