Assimilation of synthetic and real SWOT observations for the North Atlantic Ocean and Canadian east coast using the regional ice ocean prediction system
Bibliographic record
Abstract
The Surface Water Ocean Topography (SWOT) mission significantly improves on the capabilities of current nadir altimeters by enabling two-dimensional mapping. Assimilating this advanced data into high-resolution models poses challenges. To address this, Observing System Simulation Experiments (OSSEs) were conducted to evaluate the effects of both simulated and actual SWOT data on the Regional Ice Ocean Prediction System (RIOPS). This study examines the OSSEs’ design, focusing on the simulated observations and assimilation systems used. The validity of the OSSE designs is confirmed by ensuring the deviations between the assimilation system and the Nature Run (NR) align with discrepancies observed between actual oceanic data and OSSE simulations. The study measures the impact of assimilating SWOT and two nadir altimeters by calculating root mean square forecast error for sea surface height (SSH), temperature, and velocities, along with performing wave-number spectra and coherence analyses of SSH errors. The inclusion of SWOT data is found to reduce RMS SSH errors by 16% and RMS velocity errors by 6% in OSSEs. The SSH error spectrum shows that the most notable improvements are for scales associated with the largest errors in the range of 200-400 km, with a 33% reduction compared to traditional data assimilation. Additionally, spectral coherence analysis shows that the limit of constrained scales is reduced from 280 km for conventional observations to 195 km when SWOT is assimilated as well. This study also represents our first attempt at assimilating early-release SWOT data. A set of Observing System (data denial) experiments using early-release SWOT measurements shows similar (but smaller) responses to OSSE experiments in a two nadir-altimeter context. In a six-altimeter constellation setup, a positive impact of SWOT is also noted, but of significantly diminished amplitude. These findings robustly advocate for the integration of SWOT observations into RIOPS and similar ocean analysis and forecasting frameworks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".