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Record W2014408924 · doi:10.5194/acp-9-897-2009

Global temperature estimates in the troposphere and stratosphere: a validation study of COSMIC/FORMOSAT-3 measurements

2009· article· en· W2014408924 on OpenAlexaff
P. Kishore, S. P. Namboothiri, Jonathan H. Jiang, Venkataraman Sivakumar, K. Igarashi

Bibliographic record

VenueAtmospheric chemistry and physics · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsDalhousie University
FundersNational Institute of Information and Communications TechnologyJet Propulsion LaboratoryNational Oceanic and Atmospheric AdministrationMet OfficeNational Aeronautics and Space AdministrationCalifornia Institute of TechnologyIran Telecommunication Research Center
KeywordsStratosphereRadio occultationCOSMIC cancer databaseEnvironmental scienceTropopauseTroposphereClimatologyAtmospheric sciencesMeteorologyIonosphereLatitudeSatellitePolarGeographyPhysicsGeologyAstronomy

Abstract

fetched live from OpenAlex

Abstract. This paper mainly focuses on the validation of temperature estimates derived with the newly launched Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC)/Formosa Satellite 3 (FORMOSAT-3) system. The analysis is based on the radio occultation (RO) data samples collected during the first year observation from April 2006 to April 2007. For the validation, we have used the operational stratospheric analyses including the National Centers for Environmental Prediction - Reanalysis (NCEP), the Japanese 25-year Reanalysis (JRA-25), and the United Kingdom Met Office (MetO) data sets. Comparisons done in different formats reveal good agreement between the COSMIC and reanalysis outputs. Spatially, the largest deviations are noted in the polar latitudes, and height-wise, the tropical tropopause region noted the maximum differences (2–4 K). We found that among the three reanalysis data sets the NCEP data sets have the best resemblance with the COSMIC measurements.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.440
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.232
Teacher spread0.225 · 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 teacher head, 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

Citations53
Published2009
Admission routes1
Has abstractyes

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