Model Based Estimation of Sea Ice Parameters
Bibliographic record
Abstract
Abstract The work focuses on retrieving sea ice parameters using reanalysis, climatological and remote sensing data. A numerical sea ice model was implemented with a data assimilation scheme on a high performance computer. The model input includes atmospheric reanalysis and ocean climatological data. The assimilation of data acquired from satellite microwave radiometer improves model accuracy. The advantage of the model is the possibility to forecast ice parameters such as concentration, thickness, draft, ridging etc. on a high resolution scale. The modeled ice parameters can be used for risk analysis for offshore infrastructure and ship navigation in the ice covered regions. The results can also be used in regional climate studies by coupling with ocean-atmospheric models. The model was extensively tested and evaluated with satellite data and field measurements. The simulated ice draft results demonstrated a good agreement with the measurements from upward looking sonar (ULS) deployed on the Makkovik Bank (in the Labrador Sea). For example, the standard deviation (STD) of level ice draft is less than 5.0 cm and the bias is less than 0.2 cm for March-April of 2009. The simulated ice thickness was also compared with the thickness derived from Soil Moisture Ocean Salinity - Microwave Imaging Radiometer using Aperture Synthesis (SMOS-MIRAS) (). The results show that the estimated thickness from the model is within the uncertainty limits of the SMOS product.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".