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Emulating Atmospheric Transport Using Machine Learning for Greenhouse Gas Emission Flux Estimation.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Emulating Atmospheric Transport Using Machine Learning for Greenhouse Gas Emission Flux Estimation./
作者:	Dadheech, Nikhil.
面頁冊數:	1 online resource (37 pages)
附註:	Source: Masters Abstracts International, Volume: 86-01.
Contained By:	Masters Abstracts International86-01.
標題:	Physics. -
電子資源:	click for full text (PQDT)
ISBN:	9798383222652

Emulating Atmospheric Transport Using Machine Learning for Greenhouse Gas Emission Flux Estimation.
Dadheech, Nikhil.

Emulating Atmospheric Transport Using Machine Learning for Greenhouse Gas Emission Flux Estimation. - 1 online resource (37 pages)

Source: Masters Abstracts International, Volume: 86-01.

Thesis (M.Sc.)--University of Washington, 2024.

Includes bibliographical references

Carbon dioxide and methane are the two strongest anthropogenic greenhouse gases (GHGs) and together they account for more than 85% of the GHG radiative forcing since pre-industrial times. The future states of our climate have a profound impact due to their past, current and future emissions. Quantifying their emissions is important to understand why the global concentrations of GHGs are rising. Densely spaced measurements are required to study the emissions from the point sources which are responsible for a large percentage of the total emission budget. Estimating GHG emissions using atmospheric measurements is typically done by constructing source-receptor relationships (also known as "footprints"). Constructing these footprints using full-physics atmospheric transport models (ATMs) can be computationally expensive while working with densely spaced measurements at high spatio-temporal resolution. The outputs of these ATMs are storage expensive as well. Here we developed FootNet, a deep learning emulator for atmospheric transport at a kilometer scale. The emulator is trained and evaluated using footprints simulated using a Lagrangian Particle Dispersion Model (LPDM). This emulator is completely independent of any full-physics atmospheric transport model and only uses meteorological parameters as inputs. The emulator predicts magnitude and spatial pattern of the footprints in near-real-time and hence addresses the computational bottlenecks of the GHG flux inversion frameworks.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383222652Subjects--Topical Terms:

564049
Physics.
Subjects--Index Terms:

Atmospheric transportIndex Terms--Genre/Form:

554714
Electronic books.

Emulating Atmospheric Transport Using Machine Learning for Greenhouse Gas Emission Flux Estimation.
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502 $a Thesis (M.Sc.)--University of Washington, 2024.
504 $a Includes bibliographical references
520 $a Carbon dioxide and methane are the two strongest anthropogenic greenhouse gases (GHGs) and together they account for more than 85% of the GHG radiative forcing since pre-industrial times. The future states of our climate have a profound impact due to their past, current and future emissions. Quantifying their emissions is important to understand why the global concentrations of GHGs are rising. Densely spaced measurements are required to study the emissions from the point sources which are responsible for a large percentage of the total emission budget. Estimating GHG emissions using atmospheric measurements is typically done by constructing source-receptor relationships (also known as "footprints"). Constructing these footprints using full-physics atmospheric transport models (ATMs) can be computationally expensive while working with densely spaced measurements at high spatio-temporal resolution. The outputs of these ATMs are storage expensive as well. Here we developed FootNet, a deep learning emulator for atmospheric transport at a kilometer scale. The emulator is trained and evaluated using footprints simulated using a Lagrangian Particle Dispersion Model (LPDM). This emulator is completely independent of any full-physics atmospheric transport model and only uses meteorological parameters as inputs. The emulator predicts magnitude and spatial pattern of the footprints in near-real-time and hence addresses the computational bottlenecks of the GHG flux inversion frameworks.
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538 $a Mode of access: World Wide Web
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