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Tower-based greenhouse gas measurement network design—The National Institute of Standards and Technology North East Corridor Testbed

Overview of attention for article published in Advances in Atmospheric Sciences, August 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

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2 policy sources
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3 X users

Citations

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29 Dimensions

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32 Mendeley
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Title
Tower-based greenhouse gas measurement network design—The National Institute of Standards and Technology North East Corridor Testbed
Published in
Advances in Atmospheric Sciences, August 2017
DOI 10.1007/s00376-017-6094-6
Pubmed ID
Authors

Israel Lopez-Coto, Subhomoy Ghosh, Kuldeep Prasad, James Whetstone

Abstract

The North-East Corridor (NEC) Testbed project is the 3rd of three NIST (National Institute of Standards and Technology) greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions (footprints). Unlike other network design algorithms, an iterative selection algorithm, based on a k-means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and, therefore, biasing the retrieved fluxes.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 28%
Student > Ph. D. Student 4 13%
Other 3 9%
Student > Master 3 9%
Student > Doctoral Student 2 6%
Other 3 9%
Unknown 8 25%
Readers by discipline Count As %
Earth and Planetary Sciences 11 34%
Environmental Science 5 16%
Social Sciences 2 6%
Agricultural and Biological Sciences 2 6%
Biochemistry, Genetics and Molecular Biology 1 3%
Other 2 6%
Unknown 9 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 January 2022.
All research outputs
#4,536,601
of 25,382,440 outputs
Outputs from Advances in Atmospheric Sciences
#369
of 954 outputs
Outputs of similar age
#72,858
of 327,343 outputs
Outputs of similar age from Advances in Atmospheric Sciences
#12
of 25 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 954 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 35.5. This one has gotten more attention than average, scoring higher than 61% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 327,343 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.