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Record W2158422997 · doi:10.5194/angeo-26-795-2008

OH-equivalent temperatures derived from ACE-FTS and SABER temperature profiles – a comparison with OH*(3-1) temperatures from Maynooth (53.2° N, 6.4° W)

2008· article· en· W2158422997 on OpenAlexafffund
F. J. Mulligan, R. P. Lowe

Bibliographic record

VenueAnnales Geophysicae · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaUS-UK Fulbright CommissionNorges Teknisk-Naturvitenskapelige UniversitetIreland Canada University FoundationCanadian Space AgencyBergische Universität Wuppertal
KeywordsWeightingSolsticeAltitude (triangle)Atmospheric sciencesSatelliteLatitudeEnvironmental sciencePhysicsMeteorologyMathematicsAstronomyGeometry

Abstract

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Abstract. OH-equivalent temperatures were derived from all of the temperature profiles retrieved in 2004 and 2005 by the ACE-FTS instrument in a 5 degree band of latitude centred on a ground-based observing station at Maynooth. A globally averaged OH volume emission rate (VER) profile obtained from WINDII data was employed as a weighting function to compute the equivalent temperatures. The annual cycle of temperature thus produced was compared with the annual cycle of temperatures recorded at the ground-based station more than a decade earlier from the OH*(3-1) Meinel band. Both data sets showed excellent agreement in the absolute value of the temperature minimum (~162 K) and in its time of occurrence in the annual cycle at summer solstice. Away from mid-summer, however, the temperatures diverged and reach a maximum disagreement of more than 20 K in mid-winter. Comparison of the Maynooth ground-based data with the corresponding results from two nearby stations in the same time-period indicated that the Maynooth data are consistent with other ground stations. The temperature difference between the satellite and ground-based datasets in winter was reduced to 14–15 K by lowering the peak altitude of the weighting function to 84 km. An unrealistically low peak altitude would be required, however, to bring temperatures derived from the satellite into agreement with the ground-based data. OH equivalent temperatures derived from the SABER instrument using the same weighting function produced results that agreed well with ACE-FTS. When the OH 1.6 μm VER profile measured by SABER was used as the weighting function, the OH equivalent temperatures increased in winter as expected but the summer temperatures were reduced resulting in an approximately constant offset of 8.6±0.8 K between ground and satellite values with the ground values higher. Variability in both the altitude and width of the OH layer within a discernable seasonal variation were responsible for the changes introduced. The higher temperatures in winter were due to primarily to the lower altitude of the OH layer, while the colder summer temperatures were due to a thinner summer OH layer. We are not aware of previous reports of the effect of the layer width on ground-based temperatures. Comparison of OH-equivalent temperatures derived from ACE-FTS and SABER temperature profiles with OH*(3-1) temperatures from Wuppertal at 51.3° N which were measured during the same period showed a similar pattern to the Maynooth data from a decade earlier, but the warm offset of the ground values was lower at 4.5±0.5 K. This discrepancy between temperatures derived from ground-based instruments recording hydroxyl spectra and satellite borne instruments has been observed by other observers. Further work will be required by both the satellite and ground-based communities to identify the exact cause of this difference.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.215
Teacher spread0.201 · 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 designObservational
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

Citations16
Published2008
Admission routes2
Has abstractyes

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