Maximum a Posteriori Based Ocean Surface Current Inversion for Doppler Scatterometer
Bibliographic record
Abstract
Doppler Scatterometer (DopScat) is a new tool for sea surface wind and current fields remote sensing with rapid global coverage and wide observation swath. Existing current inversion methods for DopScat are based on maximum likelihood estimation (MLE), and its inversion accuracy cannot meet the requirements of many offshore operations. To improve the accuracy of ocean surface current measurement for DopScat, a method using prior probability distributions extracted from historical current fields is presented. First, according to the temporal correlation of ocean surface current, the minimum root mean square differences of current speed and direction are used to select the historical ocean current data correlated to those of the observation area. Next, a fitting method based on maximum likelihood is employed to fit the selected current speed and direction data to determine their prior probability distributions. Then, the obtained distributions are used to construct the cost functions of the proposed maximum a posteriori (MAP) based current inversion method. Finally, taking the current result provided by the MLE based current inversion method as initial guess, the cost functions of the proposed MAP-based method are optimized to obtain the final current field. Validation experiments were conducted using simulated DopScat data based on the current generated by the Ocean Surface Current Analyses Real-time (OSCAR) model, the results show that the biases of the estimated current speed and direction are better than 0.05 m/s and 15°, respectively. Compared with that of the MLE-based method, the biases are reduced by 0.16 m/s and 9°, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".