Ship‐icing prediction methods applied in operational weather forecasting
Bibliographic record
Abstract
Sea‐spray wetting of ships operating in cold environments imposes a great safety risk, due to icing. For this reason, marine‐icing warnings have been a part of operational weather forecasting for the last five decades, yet verification of such warnings has only been done sparingly. This article evaluates different ship‐icing methods applied in operational weather forecasting. The methods are tested against a unique dataset from a single ship type from Arctic–Norwegian waters and two screened datasets from several ship types from Alaska and the east coast of Canada. Missing and uncertain parameters in the latter datasets are supplemented by reanalysis data from different sources. Continuous icing‐rate verification and sensitivity tests are presented for the physical icing models alongside categorical icing‐rate verification, which is applied in order also to evaluate icing nomograms, which are still used by several forecasting agencies. Furthermore, a newly proposed definition of the boundaries between icing‐rate severity categories is applied in the categorical verification procedure. The overall best verification scores for continuous and categorical icing rates are obtained by the Marine Icing model for the Norwegian COast Guard (MINCOG) and a physically based Overland model, updated from its initial version with more realistic heat transfer. Finally, sensitivity tests highlight that very low air and sea‐surface temperatures rarely occur over sea areas together with high waves, due to fetch limitations, even for strong winds. For this reason, models and nomograms that do not treat wind speed and wave height separately will provide inaccurate predictions of the icing rate in such areas. Consequently, it is preferable that methods applied in operational weather forecasting are replaced with methods capable of taking this effect into account.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".