MARS-S2L ML model detects methane plumes in multispectral satellite imagery at 78% recall with 8% false positives on unseen sites and has enabled verified permanent mitigation at six persistent emitters including a long-running super-emitter in Algeria.
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A flow matching deep learning emulator trained on multiple SSPs generates forced climate responses for unseen scenarios and is validated against the MESMER-M statistical emulator.
A side-by-side test of statistical methods for the pattern effect finds broad agreement on global radiative responses to internal temperature variability but large disagreements on regional feedbacks and on responses to CO2-forced temperature patterns.
citing papers explorer
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Artificial intelligence for methane detection: from continuous monitoring to verified mitigation
MARS-S2L ML model detects methane plumes in multispectral satellite imagery at 78% recall with 8% false positives on unseen sites and has enabled verified permanent mitigation at six persistent emitters including a long-running super-emitter in Algeria.
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Emulating the Forced Response of Climate Models with Flow Matching
A flow matching deep learning emulator trained on multiple SSPs generates forced climate responses for unseen scenarios and is validated against the MESMER-M statistical emulator.
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Quantifying the radiative response to surface temperature variability: A critical comparison of current methods
A side-by-side test of statistical methods for the pattern effect finds broad agreement on global radiative responses to internal temperature variability but large disagreements on regional feedbacks and on responses to CO2-forced temperature patterns.