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Enregistrement W4376491192 · doi:10.5194/amt-16-1923-2023

Differences in MOPITT surface level CO retrievals and trends from Level 2 and Level 3 products in coastal grid boxes

2023· article· en· W4376491192 sur OpenAlexafffund
Ian Ashpole, Aldona Wiacek

Notice bibliographique

RevueAtmospheric measurement techniques · 2023
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueAtmospheric and Environmental Gas Dynamics
Établissements canadiensSaint Mary's University
Organismes subventionnairesCanadian Space Agency
Mots-clésEnvironmental scienceTroposphereGridMeteorologyRemote sensingAtmospheric sciencesGeographyGeology

Résumé

récupéré en direct d'OpenAlex

Abstract. Users of MOPITT (Measurement of Pollution in the Troposphere) data are advised to discard retrievals performed over water from analyses. This is because MOPITT retrievals are more sensitive to near-surface CO when performed over land than water, meaning that they have a greater measurement component and are less tied to the a priori CO concentrations (which are taken from a model climatology) that are necessarily used in their retrieval. MOPITT Level 3 (L3) products are a 1∘ × 1∘ gridded average of finer-resolution (∼ 22 × 22 km) Level 2 (L2) retrievals. In the case of coastal L3 grid boxes, L2 retrievals performed over both land and water may be averaged together to create the L3 product, with L2 retrievals over land not contributing to the average at all in certain situations. This conflicts with data usage recommendations. The aim of this paper is to highlight the consequences that this has on surface level retrievals and their temporal trends in “as-downloaded” L3 data (L3O), by comparing them to those obtained if only the L2 retrievals performed over land are averaged to create the L3 product (L3L), for all identified coastal L3 MOPITT grid boxes. First, the difference between surface level retrievals in L3L and the corresponding L2 retrievals performed over water (L3W) is established for days when they are averaged together to create the L3O product for coastal grid boxes (yielding an L3O surface index of “mixed”, L3OM). Mean retrieved volume mixing ratios (VMRs) in L3L differ by over 10 ppbv from those in L3W, and temporal trends detected in L3L are between 0.28 and 0.43 ppbv yr−1 stronger than in L3W, on average. These L3L − L3W differences are clearly linked to retrieval sensitivity differences, with L3W being more heavily tied to the a priori CO profiles used in the retrieval, which are a model-derived monthly mean climatology that, by definition, has no trend year to year. VMRs in the resulting L3OM are significantly different to L3L for 45 % of all coastal grid boxes, corresponding to 75 % of grid boxes where the L3L − L3W difference is also significant. Just under half of the grid boxes that featured a significant L3L − L3W trend difference also see trends differing significantly between L3L and L3OM. Factors that determine whether L3OM and L3L differ significantly include the proportion of the surface covered by land/water and the magnitude of land–water contrast in retrieval sensitivity. Comparing the full L3O dataset to L3L, it is shown that if L3O is filtered so that only retrievals over land (L3OL) are analysed – as recommended – there is a huge loss of days with data for coastal grid boxes. This is because L2 retrievals over land are routinely discarded during the L3O creation process for these grid boxes. There is less data loss if L3OM retrievals are also retained, but the resulting L3O “land or mixed” (L3OLM) subset still has fewer data days than L3L for 61 % of coastal grid boxes. As shown, these additional days with data feature some influence from retrievals made over water, demonstrably affecting mean VMRs and their trends. Coastal L3 grid boxes contain 33 of the 100 largest coastal cities in the world, by population. Focusing on the L3 grid boxes containing these cities, it is shown that mean VMRs in L3OL and L3L differ significantly for 11 of the 27 grid boxes that can be compared (there are no L3OL data for 6 of the grid boxes studied), with 9 of the 18 grid boxes where temporal trend analysis can be performed in L3OL featuring a trend that is significantly different to that in L3L. These differences are a direct result of the data loss in L3OL – data that are available in L2 data (and are incorporated into the L3L product created for this study). The L3L − L3OLM mean VMR difference exceeds 10 (22) ppbv for 11 (3) of these 33 grid boxes, significant in 13 cases, with significant temporal trend differences in 5 cases. It is concluded that an L3 product based only on L2 retrievals over land – the L3L product analysed in this paper, available for public download – could be of benefit to MOPITT data users.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,066
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,074
Tête enseignante GPT0,247
Écart entre enseignants0,173 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission2
Résumé présentoui

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