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Record W2783752850 · doi:10.5194/amt-11-2187-2018

MIPAS observations of ozone in the middle atmosphere

2018· article· en· W2783752850 on OpenAlexafffund
M. López‐Puertas, Maya Garcı́a-Comas, B. Funke, A. Gardini, G. P. Stiller, T. von Clarmann, N. Glatthor, Alexandra Laeng, Martin Kaufmann, Viktoria Sofieva, L. Froidevaux, Kaley A. Walker, Masato Shiotani

Bibliographic record

VenueAtmospheric measurement techniques · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of Toronto
FundersCanadian Space AgencyMinisterio de Ciencia e InnovaciónJet Propulsion LaboratoryNational Aeronautics and Space Administration
KeywordsDaytimeMesosphereAtmospheric sciencesAtmosphere (unit)Environmental scienceOzoneThermosphereStratosphereMeteorologyPhysicsIonosphere

Abstract

fetched live from OpenAlex

Abstract. In this paper we describe the stratospheric and mesospheric ozone (version V5r_O3_m22) distributions retrieved from MIPAS observations in the three middle atmosphere modes (MA, NLC, and UA) taken with an unapodized spectral resolution of 0.0625 cm−1 from 2005 until April 2012. O3 is retrieved from microwindows in the 14.8 and 10 µm spectral regions and requires non-local thermodynamic equilibrium (non-LTE) modelling of the O3 v1 and v3 vibrational levels. Ozone is reliably retrieved from 20 km in the MA mode (40 km for UA and NLC) up to ∼ 105 km during dark conditions and up to ∼ 95 km during illuminated conditions. Daytime MIPAS O3 has an average vertical resolution of 3–4 km below 70 km, 6–8 km at 70–80 km, 8–10 km at 80–90, and 5–7 km at the secondary maximum (90–100 km). For nighttime conditions, the vertical resolution is similar below 70 km and better in the upper mesosphere and lower thermosphere: 4–6 km at 70–100 km, 4–5 km at the secondary maximum, and 6–8 km at 100–105 km. The noise error for daytime conditions is typically smaller than 2 % below 50 km, 2–10 % between 50 and 70 km, 10–20 % at 70–90 km, and ∼ 30 % above 95 km. For nighttime, the noise errors are very similar below around 70 km but significantly smaller above, being 10–20 % at 75–95 km, 20–30 % at 95–100 km, and larger than 30 % above 100 km. The additional major O3 errors are the spectroscopic data uncertainties below 50 km (10–12 %) and the non-LTE and temperature errors above 70 km. The validation performed suggests that the spectroscopic errors below 50 km, mainly caused by the O3 air-broadened half-widths of the v2 band, are overestimated. The non-LTE error (including the uncertainty of atomic oxygen in nighttime) is relevant only above ∼ 85 km with values of 15–20 %. The temperature error varies from ∼ 3 % up to 80 km to 15–20 % near 100 km. Between 50 and 70 km, the pointing and spectroscopic errors are the dominant uncertainties. The validation performed in comparisons with SABER, GOMOS, MLS, SMILES, and ACE-FTS shows that MIPAS O3 has an accuracy better than 5 % at and below 50 km, with a positive bias of a few percent. In the 50–75 km region, MIPAS O3 has a positive bias of ≈ 10 %, which is possibly caused in part by O3 spectroscopic errors in the 10 µm region. Between 75 and 90 km, MIPAS nighttime O3 is in agreement with other instruments by 10 %, but for daytime the agreement is slightly larger, ∼ 10–20 %. Above 90 km, MIPAS daytime O3 is in agreement with other instruments by 10 %. At night, however, it shows a positive bias increasing from 10 % at 90 km to 20 % at 95–100 km, the latter of which is attributed to the large atomic oxygen abundance used. We also present MIPAS O3 distributions as function of altitude, latitude, and time, showing the major O3 features in the middle and upper mesosphere. In addition to the rapid diurnal variation due to photochemistry, the data also show apparent signatures of the diurnal migrating tide during both day- and nighttime, as well as the effects of the semi-annual oscillation above ∼ 70 km in the tropics and mid-latitudes. The tropical daytime O3 at 90 km shows a solar signature in phase with the solar cycle.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.085
GPT teacher head0.241
Teacher spread0.156 · 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.

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".

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Citations26
Published2018
Admission routes2
Has abstractyes

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