Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates
Bibliographic record
Abstract
In this study, a new method to assimilate freeboard (FB) derived from satellite radar altimetry is presented with the goal of improving the initial state of sea ice thickness predictions in the Arctic. In order to quantify the improvement in sea ice thickness gained by assimilating FB, we compare three different model runs: one reference run (refRun), one that assimilates only sea ice concentration (SIC) (sicRun), and one that assimilates both SIC and FB (fbRun). It is shown that estimates for both SIC and FB can be improved by assimilation, but only fbRun improved the FB. The resulting sea ice thickness is evaluated by comparing sea ice draft measurements from the Beaufort Gyre Exploration Project (BGEP) and sea ice thickness measurements from 19 ice mass balance (IMB) buoys deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The sea ice thickness of fbRun compares better than refRun and sicRun to the longer BGEP observations more poorly to the shorter MOSAiC observations. Further, the three model runs are compared to the Alfred Wegener Institute (AWI) weekly CryoSat-2 sea ice thickness, which is based on the same FB observations as those that were assimilated in this study. It is shown that the FB and sea ice thickness from fbRun are closer to the AWI CryoSat-2 values than the ones from refRun or sicRun. Finally, comparisons of the abovementioned observations and both the fbRun sea ice thickness and the AWI weekly CryoSat-2 sea ice thickness were performed. At the BGEP locations, both fbRun and the AWI CryoSat-2 sea ice thickness perform equally. The total root-mean-square error (RMSE) at the BGEP locations equals 30 cm for both sea ice thickness products. At the MOSAiC locations, fbRun's sea ice thickness performs significantly better, with a total 11 cm lower RMSE.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".