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Record W220285038

Validation and time series analysis of global stratospheric data sets

2009· article· en· W220285038 on OpenAlexaboutno aff
A. Jones

Bibliographic record

VenueChalmers Publication Library (Chalmers University of Technology) · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
Fundersnot available
KeywordsStratosphereEnvironmental scienceRemote sensingTrace gasOzone layerMicrowave radiometerSatelliteMeteorologyRadiometerOzone depletionNorthern HemisphereAtmospheric sciencesGeologyGeographyPhysics
DOInot available

Abstract

fetched live from OpenAlex

The depletion of ozone from the stratosphere and the impact of global warming have been identified as phenomena caused as a result of anthropogenic activity. Scientists are thus trying to find quicker and more reliable ways to analyse these environmental problems. Remote sensing of the atmosphere using satellites is perhaps the most cost effective method. One beneficial application is the acquisition of vertical profiles of atmospheric species on a global scale with good spatial and temporal resolutions. An example of an instrument that can achieve this is the Sub-Millimetre Radiometer (SMR) aboard the Odin satellite that launched in 2001, which retrieves vertical limb profiles of numerous trace gases, including stratospheric ozone. This thesis presents a validation summary for primarily the Odin/SMR ozone data products, v1.2, v2.0 and v2.1 (for the 501.8 GHz band in the microwave region). Comparisons are made to v4.61 data obtained from the Interferometer for Passive Atmospheric Soundings (MIPAS) instrument, on board the ENVISAT satellite. Further analysis compares Odin/SMR mixing ratios to balloon sonde data. It is suggested from the results found here that Odin/SMR v2.1 ozone data should be used for scientific purposes. Odin/ SMR is also compared to its sister instrument, the Optical Spectrograph Infra-Red Imaging System (Odin/OSIRIS). Results show an excellent agreement between coincident profiles (typically < 10%). The fact that the two fundamentally different measurement geometries agree so well, provides confidence in the robustness of both techniques. Data assimilation is also shown to be a viable application for validation of satellite measurements when very few coincident data are available between satellite measurements and balloon sondes. \n\nThe second topic covered in this thesis concerns time series analysis. One study involves modelling ozone’s evolution using linear regression. It is shown that by combining various atmospheric data sets that it is possible to estimate trends of stratospheric ozone, using an example of six satellite instruments covering 1979-2008. It is found that the upper stratosphere (35-45 km) at mid-latitudes shows the most promising signs of ozone recovery (1.7%/decade), although trend values are found not to be significant at a 95% confidence level. A similar method is applied to other shorter data sets of various other molecules such as; water vapour (H2O), chlorine monoxide (ClO), and hydrochloric acid (HCl), all three of which play important roles in the stratosphere and ultimately the recovery of stratospheric ozone. Trend analyses of ClO and HCl show that since the late 1990s abundances of these molecules have reduced in the upper stratosphere (35-45 km). The results found here confirm how effective the 1987 Montreal protocol and its amendments have been in reducing the total amount of stratospheric chlorine. \n\n

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)0.007
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
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 head0.195
Teacher spread0.185 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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