A study on harmonizing total ozone assimilation with multiple sensors
Notice bibliographique
Résumé
Abstract. The impact of assimilating total column ozone datasets from single and multiple satellite data sources with and without bias correction has been examined with a version of the Environment and Climate Change Canada variational assimilation and forecasting system. The assimilated and evaluated data sources include the Global Ozone Monitoring Experiment-2 instruments on the MetOp-A and MetOp-B satellites (GOME-2A and GOME-2B), the total column ozone mapping instrument of the Ozone Mapping Profiler Suite (OMPS-NM) on the Suomi National Polar-orbiting Partnership (S-NPP) satellite, and the Ozone Monitoring Instrument (OMI) instrument on the Aura research satellite. Ground-based Brewer and Dobson spectrophotometers, and filter ozonometers, as well as the Solar Backscatter Ultraviolet satellite instrument (SBUV/2), served as independent validation sources for total column ozone. Regional and global mean differences of the OMI-TOMS data with measurements from the three ground-based instrument types for the three evaluated two month periods were found to be within 1 %, except for the polar regions with the largest differences from the comparatively small dataset in Antarctica exceeding 3 %. Values from SBUV/2 summed partial columns were typically larger than OMI-TOMS on average by 0.6 to 1.2 ± 0.7 %, with smaller differences than with ground-based over Antarctica. OMI-TOMS was chosen as the reference used in the bias correction instead of the ground-based observations due to OMI’s significantly better spatial and temporal coverage and interest in near-real time assimilation. Bias corrections as a function of latitude and solar zenith angle were performed with a two-week moving window using colocation with OMI-TOMS and three variants of differences with short-term forecasts. These approaches are shown to yield residual biases of less than 1 %, with the rare exceptions associated with bins with less data. These results were compared to a time-independent bias correction estimation that used colocations as a function of ozone effective temperature and solar zenith angle which, for the time period examined, resulted in larger changes in residual biases as a function of time for some cases. Assimilation experiments for the July-August 2014 period show a reduction of global and temporal mean biases for short-term forecasts relative to ground-based Brewer and Dobson data from a maximum of about 2.3 % in the absence of bias correction to less than 0.3 % in size when bias correction is included. Both temporally averaged and time varying mean differences of forecasts with OMI-TOMS are reduced to within 1 % for nearly all cases when bias corrected observations are assimilated for the latitudes where satellite data is present. The impact of bias correction on the standard deviations and anomaly correlation coefficients of forecast differences to OMI-TOMS is noticeable but small compared to the impact of introducing any total column ozone assimilation. The assimilation of total column ozone data can result in some improvement, as well as some deterioration, in the vertical structure of forecasts when comparing to Aura-MLS and ozonesonde profiles. The most significant improvement in the vertical domain from the assimilation of total column ozone alone is seen in the anomaly correlation coefficients in the tropical lower stratosphere, which increases from a minimum of 0.1 to about 0.6. Nonetheless, it is made evident that the quality of the vertical structure is most improved when also assimilating ozone profile data, which only weakly affects the total column short-term forecasts.
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 | 0,004 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 ».