Estimating Random Uncertainty in Airborne Flux Measurements over Alaskan Tundra: Update on the Flux Fragment Method

Dobosy, Ronald; Sayres, David; Healy, Claire; Dumas, Edward; Heuer, Mark; Kochendorfer, John; Baker, Bruce; Anderson, James

doi:10.1175/JTECH-D-16-0187.1

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Estimating Random Uncertainty in Airborne Flux Measurements over Alaskan Tundra: Update on the Flux Fragment Method

2017
Datasets Available
By Dobosy, Ronald ; Sayres, David ; Healy, Claire ; ...
Source: Journal of Atmospheric and Oceanic Technology, 34(8), 1807-1822

[PDF-1.80 MB]

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Journal Title:

Journal of Atmospheric and Oceanic Technology
Personal Author:

Dobosy, Ronald ; Sayres, David ; Healy, Claire ; Dumas, Edward ; Heuer, Mark ; ... More +

Dobosy, Ronald ; Sayres, David ; Healy, Claire ; Dumas, Edward ; Heuer, Mark ; Kochendorfer, John ; Baker, Bruce ; Anderson, James Less -
Corporate Authors:

United States. National Oceanic and Atmospheric Administration. Office of Oceanic and Atmospheric Research ; Air Resources Laboratory (U.S.) ; Air Resources Atmospheric Turbulence and Diffusion Laboratory

United States. National Oceanic and Atmospheric Administration. Office of Oceanic and Atmospheric Research ; Air Resources Laboratory (U.S.) ; Air Resources Atmospheric Turbulence and Diffusion Laboratory Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; ARL (Air Resources Laboratory)

OAR (Oceanic and Atmospheric Research) ; ARL (Air Resources Laboratory) Less -
Description:

Airborne turbulence measurement gives a spatial distribution of air–surface fluxes that networks of fixed surface sites typically cannot capture. Much work has improved the accuracy of such measurements and the estimation of the uncertainty peculiar to streams of turbulence data measured from the air. A particularly significant challenge and opportunity is to distinguish fluxes from different surface types, especially those occurring in patches smaller than the necessary averaging length. The flux fragment method (FFM), a conditional-sampling variant of eddy covariance in the space–time domain, was presented in 2008. It was shown capable of segregating the mean flux density (CO2, H2O, sensible heat) in maize from that in soybeans over the patchwork farmlands of Illinois. This was, however, an ideal surface for the method, and the random-error estimate used a relatively rudimentary bootstrap resampling. The present paper describes an upgraded random-error estimate that accounts for the serial correlation of the time/space series and the heterogeneity of the signal. Results are presented from the Alaskan tundra. Though recognized as important, systematic error estimates are not covered in this paper. Some discussion is offered on the relation of the FFM to other approaches similarly motivated, particularly those using wavelets. Successful measurement of the variation of air–surface exchange over heterogeneous surfaces has value for developing and improving process models relating surface flux to remotely sensible quantities, such as the vegetative land-cover type and its condition.
Keywords:

[+]

Atmospheric Science Ocean Engineering Aircraft Calibration Carbon-Dioxide Climate Action CO2 Earth Sciences Eddy-Covariance Emissions Engineering Heat Meteorology Ocean Oceanography Probe Sustainable Cities And Communities Sustainable Development Goals System Turbulence Urban Heat Island
Source:

Journal of Atmospheric and Oceanic Technology, 34(8), 1807-1822
DOI:

https://doi.org/10.1175/JTECH-D-16-0187.1
ISSN:

0739-0572;1520-0426;
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PDF
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American Meteorological Society
Document Type:

Journal Article
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https://creativecommons.org/publicdomain/zero/1.0/
Rights Information:

CC0 Public Domain
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Main Document Checksum:

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urn:sha256:f9a3894c0cb852ca2575e24267bcd48865f6b4bf6310c776268a399b98ae8b85
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https://repository.library.noaa.gov/view/noaa/52414/noaa_52414_DS1.pdf
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Estimating Random Uncertainty in Airborne Flux Measurements over Alaskan Tundra: Update on the Flux Fragment Method

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