Validation of swot water surface elevations in estuaries and deltas
Bibliographic record
Abstract
The Surface Water and Ocean Topography (SWOT) mission launched in December 2022 and collected data every 1-day during the calibration/validation orbit from May-July of 2023 before switching to the science orbit for global data collection. While SWOT is designed to capture ocean and inland water processes, data application and processing in the coastal zone is less straightforward due to the complex interactions between tides, riverine inputs, and landscape features such as mudflats, vegetation, and topography. At the intersection of ocean and land, where SWOT HR and LR modes overlap, the coastal environment provides a unique opportunity to study the impact of onboard and ground processing and corrections on product accuracy and applicability in different ecosystems. We aim to evaluate the performance of SWOT HR L2 and LR L2/L3 products in measuring water surface elevations in wide open-water areas vs small tidal channels and water surface slopes in steep vs. shallow slopes. We are also exploring the use of SWOT in identifying tidal mud flats and estimating shallow water bathymetry during low-tide. The evaluation is conducted across four coastal sites: the St. Lawrence Estuary (Canada), the Komo Estuary (Gabon), the Mackenzie Delta (Canada), and the Birdfoot Delta (United States). The first two sites are within the SWOT Cal/Val 1-day repeat orbit, providing 3 months of daily acquisitions. Networks of in situ water level gauges are available at each site and are used to evaluate SWOT performance. In the St. Lawrence Estuary, among the 4 in-situ instruments within the LR swath, root mean square errors (RMSE) for L2 and L3 SSH products range from 8 to 20cm, with significant improvements in the L3 products. We’re expecting improved coverage of LR products over the estuary in SWOT Version D, increasing the number of in-situ instruments for comparison. Among the 24 in-situ instruments within the HR swath, RMSEs for the L2 Raster 100m products range from <1cm to 12cm. In the Komo Estuary, in-situ instruments at the mouth of the estuary showed a RMSE of 16cm compared with SWOT LR data. However, instruments in a small tidal channel showed a RMSE of 1m compared with SWOT HR data. To extract high quality data, we transitioned to using PIXC data products and applied additional filtering to remove layover effect from vegetation and improve site-specific classifications of land, open water, and inundated vegetation. We continue to investigate additional post-processing steps, including spatial segmentation to better capture shape and geomorphology of complex coastlines. Given these results, we are optimistic about the use of SWOT to study coastal hydrodynamics, especially in regions where in situ gauges are not available.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".