A New Environment Canada Regional Ice Analysis System
Bibliographic record
Abstract
This paper describes a new regional ice analysis system developed at Environment Canada. It is primarily designed to satisfy the requirements for planning of marine transportation and other marine operations in ice-infested waters around North America, including Canada's two Arctic Metareas; regional sea-ice model initialization; and the needs of regional numerical weather prediction models. A three-dimensional variational approach (3D-Var) is used to assimilate various types of observations. In this first version, only analyses of ice concentration are produced at approximately 5 km resolution using a 6 h persistence forecast from the previous analysis as the background state. The assimilated observations are sea-ice concentrations from two sources of passive microwave satellite data, Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E) and Special Sensor Microwave/Imager (SSM/I), and manually derived ice charts from the Canadian Ice Service (CIS). Objective verification scores computed from independent data are used to evaluate the accuracy of the analyses in comparison with other available sources of ice information. This comparison demonstrates that the new analyses have consistently more accurate ice extent compared with the currently operational global sea-ice analyses at the Canadian Meteorological Centre. It also shows that the early morning analysis can provide marine transportation clients with a valuable update to the most recently available CIS daily ice chart. RÉSUMÉ [Traduit par la rédaction] Cet article décrit un nouveau système d'analyse régionale des glaces mis au point à Environnement Canada. Il est principalement conçu pour répondre aux exigences de la planification du transport maritime et autres opérations maritimes dans les eaux infestées de glace entourant l'Amérique du Nord, y compris les deux régions météorologiques arctiques du Canada, pour l'initialisation du modèle régional de glaces de mer et pour les besoins des modèles régionaux de prévision météorologique numérique. Nous utilisons une approche variationnelle tridimensionnelle (3D-VAR) pour assimiler différents types d'observations. Dans cette première version, seules des analyses de la concentration de la glace sont produites avec une résolution d'environ 5 km à l'aide d'une prévision de 6 h basée sur la persistance à partir de l'analyse précédente utilisée comme l'ébauche. Les observations assimilées sont les concentrations de la glace de mer de deux sources satellitaires de données hyperfréquences passives — le radiomètre à balayage hyperfréquences de pointe du Système d'observation de la Terre (AMSR-E) et le capteur hyperfréquences spécialisé/imageur (SSM/I) — et les cartes des glaces élaborées manuellement du Service canadien des glaces (SCG). Nous utilisons des indices de vérification objectifs calculés à partir de données indépendantes pour évaluer l'exactitude des analyses par comparaison avec d'autres sources d'information sur les glaces disponibles. Il ressort que les nouvelles analyses ont constamment des étendues de glaces plus exactes que les analyses globales opérationnelles courantes des glaces de mer employées au Centre météorologique canadien. Cela montre aussi que l'analyse préliminaire du matin peut fournir aux clients du transport maritime une mise à jour valable de la plus récente carte quotidienne des glaces du SCG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".