Summary of Generative Nowcasting Of Marine Fog Visibility in the Grand Banks Area and Sable Island in Canada, by Eren Gultepe et al.
Generative Nowcasting of Marine Fog Visibility in the Grand Banks area and Sable Island in Canada
by Eren Gultepe, Sen Wang, Byron Blomquist, Harindra J.S. Fernando, O. Patrick Kreidl, David J. Delene, Ismail Gultepe
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Atmospheric and Oceanic Physics (physics.ao-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study employs generative deep learning techniques to predict marine fog visibility nowcasting using data from the FATIMA campaign. It leverages a combination of sensors, including Vaisala’s Forward Scatter Sensor and Weather Transmitter, as well as an ultrasonic anemometer mounted on the Research Vessel Atlantic Condor. The team preprocesses time-series data featuring fog visibility, wind speed, dew point depression, and relative humidity to create lagged time-step features. They then utilize conditional generative adversarial networks (cGAN) regression for nowcasting at 30- and 60-minute lead times, comparing results to those obtained with extreme gradient boosting (XGBoost). The study finds that cGAN outperforms XGBoost in predicting fog visibility at shorter lead times, while XGBoost performs better at longer lead times. Despite the challenges of nowcasting fog visibility at 30 minutes, the study suggests that generative analysis using observational meteorological parameters holds promise for improving our understanding of marine fog. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers used special computers to predict how thick the fog will be in the ocean. They gathered data from sensors on a boat and analyzed it to make better predictions. The team tried two different methods: one was good at predicting the thickness of the fog up close, while the other did better farther away. Despite some challenges, this study shows that using these special computers can help us understand the fog in the ocean. |
Keywords
* Artificial intelligence * Deep learning * Extreme gradient boosting * Regression * Time series * Xgboost
