Application of the Spectral Nudging on Global Tides Towards a Global Total Water Level Prediction System
Bibliographic record
Abstract
Abstract With the long-term goal of developing an ensemble forecast system for coastal flooding, we are developing a dynamically-based, numerical model of the global ocean. The model is based on the NEMO framework and has been used to predict global tides and surges in previous studies. This study focuses on the optimization of the joint prediction of both tides and surges, the two main components of total water level that cause coastal flooding. To improve the predictions of the tide we use a modified form of “spectral nudging”. We show this leads to significant improvements in the prediction of the M2 tide in the open ocean, and also in the shallow regions closer to shore where the model is not nudged. The median value of the vector difference of the tidal amplitude based on sea level observations and a data-assimilative model, and the predictions of our ocean model, is reduced from 11.2 cm to 2.66 cm by the nudging. The improvement deteriorates significantly however if additional tidal constituents are included in the model (most notably S2). This is explained in terms of spectral leakage between tidal bands associated with the nudging methodology and a straightforward solution is proposed.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".