Korrektur Mehrjahres Meereis Konzentration Schätzungen Mikrowelle Satelliten-Beobachtungen mit der Lufttemperatur , Meereis Drift und dynamische Verbindungspunkte
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Arctic sea ice cover is a sensitive climate indicator. Due to the warming climate, it has decreased dramatically in the Arctic over the past three decades. Moreover, multiyear ice (MYI), ice which has survived at least one summer, is decreasing at a much higher rate. MYI concentration can be retrieved from microwave remote sensing data. However, the retrieval shows flaws under specific weather conditions. The current thesis is motivated by the need of better estimates of MYI distribution. It introduces three methods to improve/correct the MYI concentration estimates from microwave satellite observations. The first method builds upon the NASA Team algorithm and uses dynamic tie points to compensate the temporal variations of tie points (typical brightness temperatures of each surface type at all the channels). The MYI retrievals in winters (Oct-May) of the years 1989-2012 show that the method with dynamic tie points yields higher estimates than the original method in most years. Both methods show clear declining trends of the MYI area from 1989 to 2012, which is consistent with the sea ice extent minimum. The MYI concentration retrieval with the NASA Team algorithm is most sensitive to the tie points of MYI and FYI at 19 GHz vertical polarized channel. These tie points should be treated with more caution when dynamic tie points are used. The second and third methods are two correction schemes used to account for radiometric anomalies that trigger the erroneous MYI concentration retrievals from microwave satellite observations. The correction based on air temperature is introduced to restore the underestimated MYI concentration under warm conditions. It utilizes the fact that the warm spell in autumn lasts for a few days and replaces the erroneous MYI concentrations with interpolated ones. It is applied to MYI retrievals from the Environment Canada Ice Concentration Extractor (ECICE) using inputs from QuikSCAT and AMSR-E data, acquired over the Arctic in a series of autumn seasons (Sep-Dec) from 2003 to 2008. The correction works well by identifying and correcting the anomalous MYI concentrations. For September of the six years, it introduces over 1.0x105 km2 MYI area, except for 2005. The correction based on ice drift is designed to correct the overestimated MYI concentrations that are impacted by factors such ice deformation, snow wetness and metamorphism. It utilizes ice drift records to constrain the MYI changes within a predicted contour and uses two thresholds of passive microwave radiometric parameters to account for snow wetness and metamorphism. It is applied to the MYI concentration retrievals from ECICE in winters (Oct-May) from 2002 to 2009. Qualitative comparison with Radarsat-1 SAR images and quantitative comparison against results from previous studies show that the correction works well by removing the anomalous high MYI concentrations. On average, the correction reduces 5.2x105 km2 of the estimated MYI area in Arctic except for the April-May time frame, when the reduction is larger as the warmer weather prompts the condition of the anomalous snow radiometric signatures. Both corrections can be used as post-processings to all the microwave-based MYI concentration retrieval algorithms. Due to the regional effect of weather conditions, they could be important in the operational applications. In addition, both corrections take the spatial and temporal continuity of MYI into account, which gives a new insight that instantaneous observations alone of sea ice may lead to ambiguities in determination of partial ice concentrations. This approach may be applicable to the retrieval of other sea ice parameters as well.
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.
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
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 découle