Properties of variational data assimilation for synthetic aperture radar wind retrieval
Bibliographic record
Abstract
The quality of marine wind vector retrieved from variational data assimilation (VAR) of Synthetic Aperture Radar (SAR) backscatter observation is assessed. It is found that the observation is most sensitive to wind speed. The retrieved wind direction from VAR is largely influenced by background wind direction and most of the SAR observation variability is assigned to wind speed. Non‐linearity of the Geophysical Model Function (GMF) introduces wind speed bias, modulated by wind direction anisotropy (up‐downwind/crosswind difference). The examination of the background wind vector departure from observation reveals two regimes: a quasi‐linear response to wind direction for high background wind speed; and a rather monotonic response with two sharp transitions located at crosswind directions for low background wind speed. Information content of SAR observation is estimated using the entropy reduction approach, both analytically and from Monte‐Carlo simulations. Crosswind directions have the lowest information content and correspond to those where non‐linearity introduces largest discrepancies between analytic and Monte‐Carlo estimations. The linear approximation of the GMF needed in the incremental VAR formulation is examined. The retrieved winds using the incremental formulation are in good agreement with those using the non‐linear GMF. Monte‐Carlo simulations reveal specific situations, around sharp transitions at crosswind directions, where both linear and non‐linear VAR formulations may produce more noise than extract information form observations.
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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 | 0.001 | 0.002 |
| 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".