Sea Ice Products for EUMETSAT Satellite Application Facility
Bibliographic record
Abstract
RÉSUMÉLe projet Océan et glace de mer (Ocean and Sea Ice project) du Centre SAF (Satellite Application Facility) est mené par EUMETSAT (European Meteorological Satellite Organization) et un consortium de centres météorologiques nationaux sousa l'égide de Météo France. À titre de membres de ce réseau, les Instituts météorologiques de Norvège et du Danemark ont développé et implanté des méthodes automatiques et opérationnelles pour la détermination des conditions de glace de mer à partir de données satellitaires. Un algorithme SSM/I amélioré de la concentration de glace et une nouvelle méthode multi-capteurs pour l'estimation du couvert de glace de mer à partir de données SSM/I, de scattéromètre et AVHRR ont été développés et testés. Dans cet article, on présente les produits concernant la glace de mer développés par le SAF avec une description des méthodes et quelques exemples.SUMMARYThe Satellite Application Facility (SAF) Ocean & Sea Ice project is organized by EUMETSAT (European Meteorological Satellite Organization) and a consortium of national meteorological centres led by Meteo France. As a part of this, the Norwegian and Danish Meteorological Institutes have developed and implemented methods for automatic operational sea ice retrieval from satellite data. An improved SSM/I sea ice concentration algorithm and a new multi-sensor method for estimating sea ice coverage from SSM/I, scatterometer and A VHRR data have been developed and tested. In the current paper the SAF Sea Ice products are presented with a description of the methods and with some examples. Additional informationNotes on contributorsL.-A. Breivik• Lars-Anders Breivik, Steinar Eastwood, Øystein Godøy and Harald Schyberg are with the Norwegian Meteorological Institute, P.O. Box 43 Blindem, N-0313, Oslo, Norway E-mail: l.a.breivik@dnmi.no.S. Eastwood• Lars-Anders Breivik, Steinar Eastwood, Øystein Godøy and Harald Schyberg are with the Norwegian Meteorological Institute, P.O. Box 43 Blindem, N-0313, Oslo, Norway E-mail: l.a.breivik@dnmi.no.Ø. Godøy• Lars-Anders Breivik, Steinar Eastwood, Øystein Godøy and Harald Schyberg are with the Norwegian Meteorological Institute, P.O. Box 43 Blindem, N-0313, Oslo, Norway E-mail: l.a.breivik@dnmi.no.H. Schyberg• Lars-Anders Breivik, Steinar Eastwood, Øystein Godøy and Harald Schyberg are with the Norwegian Meteorological Institute, P.O. Box 43 Blindem, N-0313, Oslo, Norway E-mail: l.a.breivik@dnmi.no.S. Andersen• Søren Andersen and Rasmus Tonboe are with the Danish Meteorological Institute, Lyngbyvej 100, Copenhagen DK-2100, Denmark.R. Tonboe• Søren Andersen and Rasmus Tonboe are with the Danish Meteorological Institute, Lyngbyvej 100, Copenhagen DK-2100, Denmark.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".