Publications by authors named "Dorit M Hammerling"

We propose a method for estimating methane emission durations on oil and gas sites, referred to as the Probabilistic Duration Model (PDM), that uses concentration data from continuous monitoring systems (CMS). The PDM probabilistically addresses a key limitation of CMS: nondetect times, or the times when wind blows emitted methane away from the CMS sensors (resulting in no detections). Output from the PDM can be used to bound the duration of emissions detected by survey-based technologies, such as plane or satellites, that have limited ability to characterize durations due to the typically low temporal frequency (e.

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Data visualization is typically a critical component of post-processing analysis workflows for floating-point output data from large simulation codes, such as global climate models. For example, images are often created from the raw data as a means for evaluation against a reference dataset or image. While the popular Structural Similarity Index Measure (SSIM) is a useful tool for such image comparisons, generating large numbers of images can be costly when simulation data volumes are substantial.

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Government policies and corporate strategies aimed at reducing methane emissions from the oil and gas sector increasingly rely on measurement-informed, site-level emission inventories, as conventional bottom-up inventories poorly capture temporal variability and the heavy-tailed nature of methane emissions. This work is based on an 11-month methane measurement campaign at oil and gas production sites. We find that operator-level top-down methane measurements are lower during the end-of-project phase than during the baseline phase.

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Methane mitigation from the oil and gas (O&G) sector represents a key near-term global climate action opportunity. Recent legislation in the United States requires updating current methane reporting programs for oil and gas facilities with empirical data. While technological advances have led to improvements in methane emissions measurements and monitoring, the overall effectiveness of mitigation strategies rests on quantifying spatially and temporally varying methane emissions more accurately than the current approaches.

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