Enhanced oceanic fog nowcasting through satellite-based recurrent neural networks
Bibliographic record
Abstract
The presence of fog in offshore regions poses significant hazards to navigation and aviation, making fog nowcasting indispensable for various industries, including oil and gas. This study presented a novel approach utilizing Recurrent Neural Networks (RNN) within a deep learning framework to address this need. Leveraging geostationary GOES-16 satellite data from the summers of 2018 and 2019, fog maps were generated as input. The model incorporated Convolutional Long Short-Term Memory (ConvLSTM) layers and was trained with a unique loss function combining Minimum Squared Error (MSE) and structural DISSIMilarity (DSSIM) metrics. Validation results demonstrated an approximate 60% accuracy for both two-hour and three-hour nowcasting. Furthermore, evaluation against in-situ data from an offshore platform revealed a Probability of Detection (PoD) of 0.75 and False Alarm Rate (FAR) of 0.14 for two-hour nowcasting, PoD of 0.75 and FAR of 0.20 for three-hour nowcasting, and PoD of 0.70 and FAR of 0.20 for six-hour nowcasting. These findings suggested the operational viability of the proposed method for short-term fog forecasting in offshore environments.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".