Predicting Surface Oil Transport in California Using a High-Resolution Regional Ocean Modeling System (ROMS) and the National Oceanic and Atmospheric Administration's (NOAA's) Trajectory Analysis Planner (TAP)
Bibliographic record
Abstract
The Bureau of Ocean Energy Management (BOEM) and Bureau of Safety and Environmental Enforcement (BSEE) Pacific Region conduct oil spill risk analyses to determine potential impacts to environmental resources. Oil spill trajectory modeling is conducted to predict the movement and fate of spilled oil, if a spill occurred, from existing offshore oil and gas operations in southern California. To improve BOEM and BSEE Pacific Region's ability to conduct oil spill risk analyses for southern California, BOEM partnered with the University of California, Los Angeles (UCLA) to run a multi-year hind cast (re-analysis) of winds, waves, and currents along the coast of California. UCLA created a high-resolution (1 km) ROMS hind cast for the 10 year period 2004–2013 from Morro Bay, California to the border with Mexico. The project was conducted in three phases: (1) Surface winds were calculated at high horizontal and temporal resolution and validated using existing datasets; (2) A wave model was forced by the wind model results and validated through in situ measurements; and (3) The ocean model was run at high resolution and includes temperature, salinity, and currents; it assimilated in situ data and was forced by the hind cast atmospheric model results. BOEM is subsequently partnering with NOAA, to utilize the surface currents and winds from the ROMS hind cast analysis with NOAA's General NOAA Operational Modeling Environment (GNOME) to produce multiple trajectories for NOAA's TAP. Using realistic oil spill scenarios over a range of different regional oceanographic regimes (such as upwelling, relaxation, and eddy-driven flow), TAP will calculate the probabilities of oil contacting parcels of water and shoreline were any oil to spill from southern California oil platforms. This will enable analysts to understand where an oil spill may travel, how long it could take to get there, and the likelihood of spilled oil contacting their resource area. An online TAP viewer with the GNOME-generated data from this study will be publicly available along with the ROMS hind cast data for oil spill response planning along with other oceanographic modeling needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".