A Neural Net Data Science Approach for Inferring Annual CO2 Flux from Long Term Measurements. ...
Inferring CO2 flux by assimilating CO2 observations into ecosystem models have been shown to be largely inconsistent with station observations. An investigation of how the biosphere has reacted to changes in atmospheric CO2 is essential to our understanding of potential climate-vegetation feedbacks....
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Online Access: | https://dx.doi.org/10.13016/m2flga-y5bl http://mdsoar.org/handle/11603/15523 |
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ftdatacite:10.13016/m2flga-y5bl 2023-08-27T04:07:54+02:00 A Neural Net Data Science Approach for Inferring Annual CO2 Flux from Long Term Measurements. ... Radov, Asen Stoyanov 2017 https://dx.doi.org/10.13016/m2flga-y5bl http://mdsoar.org/handle/11603/15523 unknown Maryland Shared Open Access Repository This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu Text Collection article 2017 ftdatacite https://doi.org/10.13016/m2flga-y5bl 2023-08-07T14:24:23Z Inferring CO2 flux by assimilating CO2 observations into ecosystem models have been shown to be largely inconsistent with station observations. An investigation of how the biosphere has reacted to changes in atmospheric CO2 is essential to our understanding of potential climate-vegetation feedbacks. Thus, a global, seasonal to annual investigation into new approaches for calculating CO2-flux is necessary to improve the prediction of annual Net Ecosystem Exchange (NEE). In this study, a Feed Forward Neural Network model is employed to train the prediction of CO2 flux based on station data from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) for three different ecosystem sites; an arid plain near Oklahoma City, an arctic tundra at Barrows Alaska, and a tropical rainforest in the Amazon. The ARM program of DOE has been making long term CO2 flux measurements (in addition to an array of other meteorological quantities) at several towers and mobile sites located around the globe at half-hour ... Article in Journal/Newspaper Arctic Tundra Alaska DataCite Metadata Store (German National Library of Science and Technology) Arctic |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
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unknown |
description |
Inferring CO2 flux by assimilating CO2 observations into ecosystem models have been shown to be largely inconsistent with station observations. An investigation of how the biosphere has reacted to changes in atmospheric CO2 is essential to our understanding of potential climate-vegetation feedbacks. Thus, a global, seasonal to annual investigation into new approaches for calculating CO2-flux is necessary to improve the prediction of annual Net Ecosystem Exchange (NEE). In this study, a Feed Forward Neural Network model is employed to train the prediction of CO2 flux based on station data from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) for three different ecosystem sites; an arid plain near Oklahoma City, an arctic tundra at Barrows Alaska, and a tropical rainforest in the Amazon. The ARM program of DOE has been making long term CO2 flux measurements (in addition to an array of other meteorological quantities) at several towers and mobile sites located around the globe at half-hour ... |
format |
Article in Journal/Newspaper |
author |
Radov, Asen Stoyanov |
spellingShingle |
Radov, Asen Stoyanov A Neural Net Data Science Approach for Inferring Annual CO2 Flux from Long Term Measurements. ... |
author_facet |
Radov, Asen Stoyanov |
author_sort |
Radov, Asen Stoyanov |
title |
A Neural Net Data Science Approach for Inferring Annual CO2 Flux from Long Term Measurements. ... |
title_short |
A Neural Net Data Science Approach for Inferring Annual CO2 Flux from Long Term Measurements. ... |
title_full |
A Neural Net Data Science Approach for Inferring Annual CO2 Flux from Long Term Measurements. ... |
title_fullStr |
A Neural Net Data Science Approach for Inferring Annual CO2 Flux from Long Term Measurements. ... |
title_full_unstemmed |
A Neural Net Data Science Approach for Inferring Annual CO2 Flux from Long Term Measurements. ... |
title_sort |
neural net data science approach for inferring annual co2 flux from long term measurements. ... |
publisher |
Maryland Shared Open Access Repository |
publishDate |
2017 |
url |
https://dx.doi.org/10.13016/m2flga-y5bl http://mdsoar.org/handle/11603/15523 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Tundra Alaska |
genre_facet |
Arctic Tundra Alaska |
op_rights |
This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu |
op_doi |
https://doi.org/10.13016/m2flga-y5bl |
_version_ |
1775348614903627776 |