Differences in MOPITT surface-level CO retrievals and trends from Level 2 and Level 3 products in coastal grid boxes
Notice bibliographique
Résumé
Abstract. MOPITT retrievals are more sensitive to near-surface CO when performed over land than water. Data users are therefore advised to discard retrievals performed over water from analyses to limit the a priori influence on results. Level 3 (L3) products are a 1° x 1° gridded average of finer resolution Level 2 (L2) retrievals. For coastal grid boxes, these are retrievals that are either performed over land, water, or a combination of the two, on any given day. L3 data users therefore have limited ability to filter for retrievals performed over water for these grid boxes. The consequences that this has on retrievals and their temporal trends in “as-downloaded” L3 data (L3O) are examined in this paper, for all coastal L3 MOPITT grid boxes (n = 4299), by comparison to separate land- and water-only grid box averaged L2 retrievals (L3L and L3W, respectively). First, it is established that mean retrieved VMRs in L3L and L3W differ by over 10 ppbv, significant (p < 0.1) at 60 % of the coastal grid boxes. Trends are also stronger in L3L (mean difference between 0.28 ppbv y-1 and 0.43 ppbv y-1), with the L3L – L3W trend difference significant at 36 % of grid boxes. 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 is a model-derived monthly mean climatology. On days when L3O is created from the averaging together of L2 retrievals over both land and water (L3OM), the result is VMRs that are significantly different to L3L for 75 % of grid boxes where the L3L – L3W difference is also significant, 45 % of all coastal grid boxes. 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 significance of difference between L3OM and L3L include proportion of the surface covered by land/water, and the magnitude of sensitivity contrast. Comparing the full L3O dataset to L3L, it is shown that if L3O is filtered so that only retrievals over land (L3OL) are analysed, there is a huge loss of days with data. This is because L2 retrievals over land are routinely discarded during the L3O creation process, for coastal grid boxes. The problem can be lessened by also retaining L3OM retrievals, but the resulting L3O “land or mixed” (L3OLM) subset still has less data days than L3L for 61 % of coastal grid boxes. Moreover, as already shown, these additional days with data feature some influence from retrievals made over water that can affect results. 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 cities that can be compared (there are no L3OL data for 6 of the cities). The L3L – L3OLM mean VMR difference exceeds 10 (22) ppbv for 11 (3) of the 33 cities, significant in 13 cases. 9 of the 18 cities where WLS analysis can be performed in L3OL feature a trend that is significantly different to L3L. The trends in L3OLM and L3L differ significantly for 5 of the 33 cities. It is concluded that a L3 product based only on L2 retrievals over land would be of benefit to MOPITT data users, given the clear and sometimes significant differences in mean CO VMRs and trends that can be obtained for coastal grid boxes using L2 products in which retrievals performed over water can be more easily discarded.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».