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Record W3162000080 · doi:10.5194/egusphere-egu21-13671

Weather-condition-regulated, heated 3-D sonic anemometers (CSAT3AH and CSAT3BH): Working rationale, operation algorithm, and performance assessment

2021· article· en· W3162000080 on OpenAlexaff
Hayden Mahan, Tian Gao, Xiufen Li, Troy Forbush, K. Cole Payne, Quan Yang, Yanlei Li, Haitao Zhou, Shangming Wu, Ning Zheng, Xinhua Zhou

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsCampbell Scientific (Canada)
Fundersnot available
KeywordsAnemometerEddy covarianceAcousticsWind speedUltrasonic sensorSupersonic speedTransducerMeteorologyFlux (metallurgy)Environmental sciencePhysicsMaterials scienceMechanics

Abstract

fetched live from OpenAlex

Three-dimensional (3D) sonic anemometers are commonly used to measure 3-D wind in eddy-covariance systems for the fluxes of momentum, sonic temperature, and when integrated with fast-response gas analyzers, the fluxes of CO2/H2O. A 3-D sonic anemometer has three pairs of sonic transducers spatially positioned with optimized geometry for 3-D wind measurements. The three pairs form three individual sonic paths, each of which is between paired transducers mutually emitting and receiving ultrasonic signals. The transmitting time of the signals in reference to the sonic path length is used to calculate air flow speed and sonic temperature at high frequencies, which can be used for flux computations. However, under unfavorable weather conditions the dew, frost, snow, and/or ice often deposit on the transducer signal transmitting surface. The deposition interferes with the transducer emitting and receiving signals, bringing significant uncertainties to the wind and sonic temperature measurements. These uncertainties degrade the quality of flux data and even interrupt the data continuity, especially in climates where this deposition is most frequent. To minimize the uncertainties, and to optimize the data quality and continuity as much as possible, Campbell Scientific developed the weather-condition-regulated, heated 3-D sonic anemometers: CSAT3AH and CSAT3BH. The former is a heated CSAT3A used for Campbell Scientific open-path and closed-path eddy-covariance systems. The latter is a heated CSAT3B, universally configured with any other gas analyzer for eddy-covariance measurements or used as a stand-alone sensor for wind aerodynamic measurements. Both models use the same heating technology equipping a sonic anemometer with an electronic heating controller (CSAT3H) programmatically regulating the power to heat sonic transducers, arms, and the strut. Based on weather conditions (air temperature, relative humidity, wind speed, and atmospheric pressure) and sonic anemometer operation status (diagnosis codes), the controller regulates heat to prevent frozen and liquid deposition from interfering with the sonic signal. The two new models of sonic anemometers were tested and assessed at several locations, including inside an environment-controlled laboratory chamber, over a forest canopy in a cold region, and at a snow-covered field station at a high plateau. This poster addresses working rationale, operating heating algorithm, and sensor performances.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.008

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 head0.030
GPT teacher head0.249
Teacher spread0.219 · 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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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