Enhanced automated meteorological observations at the Canadian Arctic Weather Science (CAWS) supersites
Notice bibliographique
Résumé
Abstract. The changing Arctic climate is creating increased economic, transportation, and recreational activities requiring reliable and relevant weather information. However, the Canadian Arctic is sparsely observed, and processes governing weather systems in the Arctic are not well understood. There is a recognized lack of meteorological data to characterize the Arctic atmosphere for operational forecasting and to support process studies, satellite calibration/validation, search and rescue operations (which are increasing in the region), high-impact weather (HIW) detection and prediction, and numerical weather prediction (NWP) model verification and evaluation. To address this need, Environment and Climate Change Canada commissioned two supersites, one in Iqaluit (63.74∘ N, 68.51∘ W) in September 2015 and the other in Whitehorse (60.71∘ N, 135.07∘ W) in November 2017 as part of the Canadian Arctic Weather Science (CAWS) project. The primary goals of CAWS are to provide enhanced meteorological observations in the Canadian Arctic for HIW nowcasting (short-range forecast) and NWP model verification, evaluation, and process studies and to provide recommendations on the optimal cost-effective observing system for the Canadian Arctic. Both sites are in provincial/territorial capitals and are economic hubs for the region; they also act as transportation gateways to the north and are in the path of several common Arctic storm tracks. The supersites are located at or next to major airports and existing Meteorological Service of Canada ground-based weather stations that provide standard meteorological surface observations and upper-air radiosonde observations; they are also uniquely situated in close proximity to frequent overpasses by polar-orbiting satellites. The suite of in situ and remote sensing instruments at each site is completely automated (no on-site operator) and operates continuously in all weather conditions, providing near-real-time data to operational weather forecasters, the public, and researchers via obrs.ca. The two sites have similar instruments, including mobile Doppler weather radars, multiple vertically profiling and/or scanning lidars (Doppler, ceilometer, water vapour), optical disdrometers, precipitation gauges in different shielded configurations, present weather sensors, fog monitoring devices, radiation flux sensors, and other meteorological instruments. Details on the two supersites, the suites of instruments deployed, the data collection methods, and example case studies of HIW events are discussed. CAWS data are publicly accessible via the Canadian Government Open Data Portal (https://doi.org/10.18164/ff771396-b22c-4bc3-844d-38fc697049e9, Mariani et al., 2022a, and https://doi.org/10.18164/d92ed3cf-4ba0-4473-beec-357ec45b0e78, Mariani et al., 2022b); this dataset is being used to improve our understanding of synoptic and fine-scale meteorological processes in the Arctic and sub-Arctic, including HIW detection and prediction and NWP verification, assimilation, and processes.
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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 | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».