Benefits of assimilating thin sea-ice thickness from SMOS-Ice into the TOPAZ system
Bibliographic record
Abstract
Abstract. An observation product for thin sea ice thickness (SMOS-Ice) is derived from the brightness temperature data of the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) Mission, and available in real-time at daily frequency during the winter season. In this study, we investigate the benefit of assimilating SMOS-Ice into the TOPAZ system. TOPAZ is a coupled ocean-sea ice forecast system that assimilates SST, altimetry data, temperature and salinity profiles, ice concentration, and ice drift with the Ensemble Kalman Filter (EnKF). The conditions for assimilation of sea ice thickness thinner than 0.4 m are favorable, as observations are reliable below this threshold and their probability distribution is comparable to that of the model. Two paralleled runs of TOPAZ have been performed respectively in March and November 2014, with assimilation of thin sea ice thickness (thinner than 0.4 m) in addition to the standard ice and ocean observational data sets. It is found that the RMSD of thin sea-ice thickness is reduced by 11 % in March and 22 % in November suggesting that SMOS-Ice has a larger impact during the beginning of freezing season. There is a slight improvement of the ice concentration and no degradation of the ocean variables. The Degrees of Freedom for Signal (DFS) indicate that the SMOS-Ice contents important information (> 20 % of the impact of all observations) for some areas in the Arctic. The areas of largest impact are the Kara Sea, the Canadian archipelago, the Baffin Bay, the Beaufort Sea and the Greenland Sea. This study suggests that SMOS-Ice is a good complementary data set that can be safely included in the TOPAZ system as it improves the ice thickness and the ice concentration but does not degrade other quantities. Keywords: SMOS-Ice; EnKF; OSE; thin sea-ice thickness; DFS;
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".