AI for Natural Methane: Harmonizing Natural Methane Datasets using Knowledge Guided Machine Learning

This working group integrates scientific knowledge with machine learning to harmonize simulated and observed datasets from global wetlands and soil sinks. The team consolidates field-based methane measurements from chamber and eddy-covariance techniques alongside outputs from process-based models and atmospheric assimilation models to quantify spatial and temporal changes in global natural methane fluxes, generating publicly shared harmonized datasets spanning 1980 to present.

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フィールド	値
作成者	Youmi Oh
最終更新	4月 23, 2026, 20:05 (UTC)
Published	4月 7, 2026, 22:24 (UTC)
Citation	Youmi Oh 2024. AI for Natural Methane: Harmonizing Natural Methane Datasets using Knowledge Guided Machine Learning. CyVerse Data Commons.
説明	This working group integrates scientific knowledge with machine learning to harmonize simulated and observed datasets from global wetlands and soil sinks. The team consolidates field-based methane measurements from chamber and eddy-covariance techniques alongside outputs from process-based models and atmospheric assimilation models to quantify spatial and temporal changes in global natural methane fluxes, generating publicly shared harmonized datasets spanning 1980 to present.
PublicationYear	2024
Publisher	CyVerse Data Commons
Rights	This material is based upon work supported by the National Science Foundation under grant #2153040, the NSF ACCESS-CI program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296. This work used Jetstream2 at Indiana University through allocation BIO220085. CyVerse is based upon work supported by the NSF under Grant Nos. DBI-0735191, DBI-1265383, and DBI-1743442.
Subject	atmospheric methane, machine learning, wetlands, greenhouse gas, climate change, knowledge-guided modeling
de_created_date	2024-06-12T19:45:14Z
de_modified_date	2026-04-23T19:45:48Z