Tower-Based Greenhouse Gas Measurement Network Design---The National Institute of Standards and Technology North East Corridor Testbed.

Adv Atmos Sci 2017 Sep 5;34(9):1095-1105. Epub 2017 Aug 5.

National Institute of Standards and Technology, Gaithersburg, MD20899, USA.

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 -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.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00376-017-6094-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5695685PMC
September 2017
5 Reads

Publication Analysis

Top Keywords

greenhouse gas
16
standards technology
8
national institute
8
gas emissions
8
larger uncertainties
8
measurement network
8
institute standards
8
network
7
consisting differing
4
differing numbers
4
towers tower
4
configurations consisting
4
numbers towers
4
network configurations
4
system performance
4
observing system
4
cover largest
4
performance performances
4
performances measurement
4
suboptimal cover
4

References

(Supplied by CrossRef)

F. M. Bréon et al.
Atmos. Chem. Phys 2015

J. Brioude et al.
J. Geophys. Res. 2012

M. O. L. Cambaliza et al.
Atmos. Chem. Phys. 2014

M. C. Coniglio et al.
Wea. Forecasting 2013

R. M. Duren et al.
Nature Clim. Change 2012

E. W. Forgy et al.
Biometrics 1965

C. Gerbig et al.
J. Geophys. Res. 2003

J. A. Hartigan et al.
Applied Statistics 1979

K. Hungershoefer et al.
Chem. Phys. 2010

IPCC et al.
2013

Z. I. Janjic et al.
Mon. Wea. Rev. 1994

Similar Publications