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Record W2588375651 · doi:10.5194/amt-10-2455-2017

Validation of 10-year SAO OMI Ozone Profile (PROFOZ) product using ozonesonde observations

2017· article· en· W2588375651 on OpenAlexaff
Guanyu Huang, Xiong Liu, K. Chance, Kai Yang, P. K. Bhartia, Zhaonan Cai, Marc Allaart, G. Ancellet, Bertrand Calpini, G. J. R. Coetzee, Emilio Cuevas, M. Cupeiro, H. De Backer, Manvendra K. Dubey, Henry E. Fuelberg, Masatomo Fujiwara, Sophie Godin‐Beekmann, Tristan J. Hall, B. J. Johnson, Everette Joseph, Rigel Kivi, B. Kois, Ninong Komala, Gert König‐Langlo, Giovanni Laneve, Thierry Leblanc, Marion Marchand, K. Minschwaner, Gary A. Morris, Michael J. Newchurch, Shin‐Ya Ogino, Nozomu Ohkawara, Ankie Piters, Françoise Posny, Richard Querel, Rinus Scheele, F. J. Schmidlin, R. C. Schnell, Otto Schrems, Henry B. Selkirk, Masato Shiotani, P. Skřivánková, René Stübi, Ghassan Taha, D. W. Tarasick, Anne M. Thompson, V. Thouret, Matthew B. Tully, Roeland Van Malderen, Holger Vömel, Peter von der Gathen, J. C. Witte, Margarita Yela

Bibliographic record

VenueAtmospheric measurement techniques · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Aeronautics and Space AdministrationSmithsonian Institution
KeywordsMiddle latitudesAtmospheric sciencesTroposphereEnvironmental scienceLatitudeOzoneTropopauseClimatologyStratosphereTropicsOzone Monitoring InstrumentOzone layerMeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

Abstract. We validate the Ozone Monitoring Instrument (OMI) Ozone Profile (PROFOZ) product from October 2004 through December 2014 retrieved by the Smithsonian Astrophysical Observatory (SAO) algorithm against ozonesonde observations. We also evaluate the effects of OMI row anomaly (RA) on the retrieval by dividing the dataset into before and after the occurrence of serious OMI RA, i.e., pre-RA (2004–2008) and post-RA (2009–2014). The retrieval shows good agreement with ozonesondes in the tropics and midlatitudes and for pressure < ∼ 50 hPa in the high latitudes. It demonstrates clear improvement over the a priori down to the lower troposphere in the tropics and down to an average of ∼ 550 (300) hPa at middle (high) latitudes. In the tropics and midlatitudes, the profile mean biases (MBs) are less than 6 %, and the standard deviations (SDs) range from 5 to 10 % for pressure < ∼ 50 hPa to less than 18 % (27 %) in the tropics (midlatitudes) for pressure > ∼ 50 hPa after applying OMI averaging kernels to ozonesonde data. The MBs of the stratospheric ozone column (SOC, the ozone column from the tropopause pressure to the ozonesonde burst pressure) are within 2 % with SDs of < 5 % and the MBs of the tropospheric ozone column (TOC) are within 6 % with SDs of 15 %. In the high latitudes, the profile MBs are within 10 % with SDs of 5–15 % for pressure < ∼ 50 hPa but increase to 30 % with SDs as great as 40 % for pressure > ∼ 50 hPa. The SOC MBs increase up to 3 % with SDs as great as 6 % and the TOC SDs increase up to 30 %. The comparison generally degrades at larger solar zenith angles (SZA) due to weaker signals and additional sources of error, leading to worse performance at high latitudes and during the midlatitude winter. Agreement also degrades with increasing cloudiness for pressure > ∼ 100 hPa and varies with cross-track position, especially with large MBs and SDs at extreme off-nadir positions. In the tropics and midlatitudes, the post-RA comparison is considerably worse with larger SDs reaching 2 % in the stratosphere and 8 % in the troposphere and up to 6 % in TOC. There are systematic differences that vary with latitude compared to the pre-RA comparison. The retrieval comparison demonstrates good long-term stability during the pre-RA period but exhibits a statistically significant trend of 0.14–0.7 % year−1 for pressure < ∼ 80 hPa, 0.7 DU year−1 in SOC, and −0. 33 DU year−1 in TOC during the post-RA period. The spatiotemporal variation of retrieval performance suggests the need to improve OMI's radiometric calibration especially during the post-RA period to maintain the long-term stability and reduce the latitude/season/SZA and cross-track dependency of retrieval quality.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.268
Teacher spread0.184 · 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 designBench or experimental
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

Citations91
Published2017
Admission routes1
Has abstractyes

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