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Temperature Induced Spectroscopic Line-broadening Effects in Open-path Eddy Covariance CO2 Flux

2020· article· en· W3094495399 on OpenAlexaff
Ivan Bogoev, David Holl

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsCampbell Scientific (Canada)
Fundersnot available
KeywordsGas analyzerEddy covarianceSpectrum analyzerFlux (metallurgy)ChemistryPhysicsAnalytical Chemistry (journal)Optics

Abstract

fetched live from OpenAlex

Open-path eddy covariance systems, based on broad-band non-dispersive infrared (NDIR) gas analyzers, are widely used for CO2 and H2O flux measurements in remote locations around the world, because of their low power consumption, fast response and reliable operation. Nevertheless, agreement between open- and closed-path CO2 fluxes has limited inter-site comparability, especially in cold or non-growing seasons and low-flux environments, where physiologically unreasonable CO2 uptake is often observed by the open-path systems. A possible explanation is sensor-surface heating from internal-electronics power dissipation and solar radiation, which causes unaccounted gas density changes in the optical path. Fast-response thermometers, co-located with the gas analyzer, have been used to correct these effects. However, the fragility of the thermometers has prevented the wide adoption of this approach. A challenge for the open-path sensor design is that in-situ air temperature affects not only the gas density but also the broadened half-width and intensity of the spectral absorption lines. We hypothesize that fast air temperature fluctuations in the optical path of the gas analyzer can change the amount of absorbed light and cause errors in the CO2 concentration measurement. Because of the natural covariance of sensible and CO2 fluxes, such errors are well correlated with the vertical wind and can potentially propagate into flux calculations. We used spectral-line parameters, obtained from the high-resolution transmission molecular spectroscopic database (HITRAN), to evaluate the temperature effects on the integrated absorption spectra of CO2-air-mixtures across the 4.2 to 4.3 μm infrared active region utilized by NDIR analyzers. Results show that air temperature strongly influences absorption, and if not properly corrected, potentially introduces biases in the CO2 concentration measurements. Strong lines exhibit Doppler broadening, where the line peak and width decline with increasing temperatures, causing underestimation of CO2 concentration. Weak lines exhibit the opposite behavior. Based on our simulations, optimizing the optical filter pass-band can balance these opposing effects and greatly reduce the temperature dependence. In practice, manufacturing tolerances, shifts in the center wavelength, and the temperature sensitivity of the optical filters prevent complete elimination of the temperature-line broadening. A 13 nanometer shift in the filter pass band can introduce a 0.008 mmol m-3 K-1 underestimation in the CO2 concentration, which is a 0.67 μmol m-2 s-1 systematic error in CO2 flux per 100 watts of sensible heat flux.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.228
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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