Presentation | GC22F: New Technologies and Frameworks to Detect and Analyze Methane Emissions from the Oil and Gas Supply Chain: Methods, Data, and Insights II Oral
Oral
GC22F-06: Turbulence Parameter Bias in Reanalysis Data: Implications for Methane Emissions Detection and Global Climate Standards
Author(s): Kira Shonkwiler, Colorado State University Fort Collins (First Author) Michael Moy, Colorado State University Anna Hodshire, Colorado State University Fort Collins (Presenting Author) Daniel Zimmerle, Colorado State University
We evaluated how reliable weather reanalysis datasets are at estimating wind turbulence, which is important for determining how methane from oil and gas sites spreads and how much is emitted. Using data from our test site, we found that reanalysis data can differ enough to make emission estimates unreliable. This matters because regulations in places like the European Union require very accurate emissions reporting—and if companies can’t meet those standards, they might not be allowed to sell their product overseas. Our study helps show how to fix or work around these errors to make sure methane detection and reporting is more accurate.
