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Record W2113821933 · doi:10.1029/2007jd008779

Evaluation of ozone total column measurements by the Ozone Monitoring Instrument using a data assimilation system

2008· article· en· W2113821933 on OpenAlexaff
Stefano Migliorini, R. Brugge, A. O’Neill, Marcel Dobber, Vitali Fioletov, P. F. Levelt, Richard McPeters

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change Canada
FundersNatural Environment Research CouncilSight Research UK
KeywordsOzone Monitoring InstrumentTotal Ozone Mapping SpectrometerOzoneEnvironmental scienceData assimilationSatelliteMeteorologyAtmospheric sciencesColumn (typography)Remote sensingOzone layerGeographyMathematicsGeologyPhysics

Abstract

fetched live from OpenAlex

On 15 July 2004, the Ozone Monitoring Instrument (OMI) on board the EOS Aura mission was launched. One of OMI's priorities is to continue the record of high spatial resolution ozone total column measurements provided by the various Total Ozone Mapping Spectrometer (TOMS) instruments since 1978. To this end, it is essential to estimate the errors affecting OMI ozone total column measurements and to see whether the actual accuracy is consistent with estimated values before launch. In this paper, data assimilation techniques are used to create a large comparison data set composed of ozone analyses resulting from assimilation of standard meteorological observations and ozone retrievals (independent of OMI measurements) into a numerical weather prediction model. This data set provides excellent global coverage and temporal resolution, not limited by the spatial and temporal distribution of other satellite or ground based information. The accuracy of the analyses is evaluated against ozone total column retrievals from Brewer measurements, while the assimilated ozone data set is compared to ozone predictions made using the ECMWF model, to check for the presence of bias. The OMI ozone column measurements considered here are obtained with the TOMS‐V8 total ozone algorithm and denoted as OMTO3 columns. They are compared with simulated OMI ozone columns, i.e., the quantities that the TOMS‐V8 algorithm would retrieve in the case when the atmospheric ozone profile at a specific location and time is equal to the one prescribed by the analysis. In this way, the comparison is statistically robust even when data acquired during a relatively short temporal interval or over a relatively small geographical area only is considered. A discussion of relevant error sources (including systematic components), vertical resolution, and contributions from prior information is provided. Special attention is given to determining the importance of representativeness errors. Our results show a solar zenith angle (SZA) dependence of the bias between measured and simulated OMI columns. This is believed to be due to moderate nonlinearity of the observation forward model and its effects on our definition of simulated OMI columns at high SZA. In view of these findings the final results of the intercomparison methodology used in this paper are obtained from OMI ozone columns retrieved using the basic implementation of the TOMS‐V8 algorithm applied to measurements taken at SZA not exceeding 70°. Intercomparison results between measured and simulated OMI ozone columns at SZA less than 70° show a relative bias of −3.2 ± 3.1% and a root‐mean‐square error of 4.5 ± 1.5%. The resulting bias is consistent with available estimates of the bias of OMTO3 columns with respect to SBUV/2 between 60°S and 60°N, as well as with respect to global Dobson data and Brewer measurements between 30°N and 60°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)0.003
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.296
GPT teacher head0.375
Teacher spread0.079 · 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
Published2008
Admission routes1
Has abstractyes

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