Assessing the Impact of Different Ocean Analysis Schemes on Oceanic and Underwater Acoustic Predictions
Bibliographic record
Abstract
Abstract Assimilating oceanic observations into prediction systems is an advantageous approach for real‐time ocean environment characterization. However, its benefits to underwater acoustic predictions are not trivial due to the nonlinearity and sensitivity of underwater acoustic propagation to small‐scale oceanic features. In order to assess the potential of oceanic data assimilation, integrated ocean‐acoustic Observing System Simulation Experiments are conducted. Synthetic altimetry and in situ data were assimilated through a variational oceanographic data assimilation system. The predicted sound speed fields are then ingested in a range‐dependent acoustic model for transmission loss (TL) predictions. The predicted TLs are analyzed for the purpose of (i) evaluating the contributions of different sources to the uncertainties of oceanic and acoustic forecasts and (ii) comparing the impact of different oceanic analysis schemes on the TL prediction accuracy. Using ensemble member clustering techniques, the contributions of boundary conditions, ocean parameterizations, and geoacoustic characterization to acoustic prediction uncertainties are addressed. Subsequently, the impact of three‐dimensional variational (3DVAR), 4DVAR, and hybrid ensemble‐3DVAR data assimilation on acoustic TL prediction at two signal frequencies (75 and 2,500 Hz) and different ranges (30 and 60 km) are compared. 3DVAR significantly improves the predicted TL accuracy compared to the control run. Promisingly, 4DVAR and hybrid data assimilation further improve the TL forecasts, the hybrid scheme achieving the highest skill scores for all cases, while being the most computationally intensive scheme. The optimal scheme choice thus depends on requirements on the accuracy and computational constraints. These findings foster developments of coupled data assimilation for operational underwater acoustic propagation.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".