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Record W2162615883 · doi:10.1002/qj.938

On the feasibility of a fast forward model for Doppler interferometry in the infrared

2011· article· en· W2162615883 on OpenAlexaffabout
D. S. Turner, Yves Rochon

Bibliographic record

VenueQuarterly Journal of the Royal Meteorological Society · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsMichelson interferometerRadiative transferStratosphereAtmospheric soundingAtmospheric radiative transfer codesRemote sensingEnvironmental scienceDoppler effectLongwaveInterferometryAtmospheric modelMeteorologyPhysicsOpticsGeology

Abstract

fetched live from OpenAlex

Abstract A fast forward radiative transfer model to compute the emitted limb radiances for use in recovering line‐of‐sight Doppler winds from thermal line emission has been developed and evaluated. This study was formulated around the Stratospheric Wind Interferometer for Transport Studies (SWIFT) instrument—a limb imaging, field widened, phase‐stepping Michelson interferometer. The fast forward model is required to simulate a four‐point interferogram from which line‐of‐sight winds may be computed. The model, RT‐SWIFT (Radiative Transfer for SWIFT), is part of an effort to develop the capability of providing simultaneous measurements of horizontal wind velocity vectors and ozone concentration in the stratosphere for improving our understanding of global stratospheric dynamics and for studies in ozone transport. Based in part on RT‐MIPAS (Radiative Transfer for the Michelson Interferometer for Passive Atmospheric Sounding), RT‐SWIFT uses a linear regression algorithm to parametrize the effective layer optical depths and can simulate the effect of variable ozone, nitrous oxide, water vapour and atmospheric winds. Trace gases are included as fixed climatological profiles. The model development involved the selection of two suitable databases consisting of appropriate atmospheric absorber and wind profiles, and the accurate line‐by‐line modelling of their transmittances; one for generating regression coefficients and one consisting of simulated measurements for independent evaluation. The development also required selecting suitable predictors for the absorbers, winds and viewing geometry. One‐dimensional inversion results using RT‐SWIFT with simulated measurements indicate that the model leads to wind and ozone error levels of about 3 to 5 m s−1 and 3 to 15% respectively for altitudes above ∼24 km. While further investment in model development and optimization is warranted, this demonstration study indicates that a sufficiently accurate fast forward model for near real‐time assimilation, and inversion, of horizontal Doppler wind measurements from thermal line emission is feasible. © 2011 Crown in the right of Canada. Published by John Wiley & Sons Ltd.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.059
GPT teacher head0.252
Teacher spread0.193 · 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 designSimulation or modeling
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

Citations3
Published2011
Admission routes2
Has abstractyes

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