Cloud, thermodynamic, and precipitation observations in West Africa during 2006
Notice bibliographique
Résumé
In 2006, the ARM Mobile Facility (AMF) completed a 1‐year deployment at Niamey, Niger, Africa, in support of the Radiative Atmospheric Divergence using ARM Mobile Facility, GERB data and AMMA Stations (RADAGAST) field campaign, which is the subject of this special issue. Observations from the AMF instrumentation are used to analyze the relationship between clouds, precipitation, and the thermodynamic environment in this rarely observed region and to evaluate the cloud fields in the National Center for Environmental Prediction Global Forecast System (GFS) initialization product. The 1‐year deployment period enabled measurements in the dry and wet (monsoon) seasons and through the transitions in May and September, respectively. Cirrus clouds in the 10‐ to 15‐km layer with modest monthly cloud fraction and mean depth of ∼1 km are ubiquitous through the observing period as observed in other regions of the tropics. The monsoon season from May to September is characterized by convective clouds of varying depth that produce precipitation of varying intensity, as indicated by cloud radar. Peak surface rainfall is observed during August, and the largest daily rainfall rates are observed during the period from July to September. The lifting condensation level (LCL) is observed to decrease as the monsoon season progresses, and a strong correlation between the height of the LCL and precipitation is demonstrated. Cooling of the lower troposphere is implicated as the probable cause of the lowering of the LCL. Conversely, the amount of convective available potential energy is found to be poorly correlated with precipitation. As in other tropical regions, the physical height at which the zero‐degree isotherm is observed corresponds to gradients in the thermodynamic profiles and a gradient in the profile of cloud occurrence. Comparisons with the GFS initialization data, which are derived from a number of sources including satellites, show some systematic biases when compared to AMF measurements. There is general correspondence between the locations of clouds and the profile of vertical velocity diagnosed by the GFS initialization early in the monsoon season, but vague correspondence thereafter. The relative humidity in the GFS initialization is too large above 10 km and too small in the monsoon layer near the surface, and it seriously underestimates the amount of cloud below 10 km during August, which is the height of the West African monsoon in Niamey.
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 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) | 0,000 | 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) | 0,000 | 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 tête enseignante, 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 ».