Generative Nowcasting of Marine Fog Visibility in the Grand Banks area and Sable Island in Canada ...

• Main Authors: Gultepe, Eren, Wang, Sen, Blomquist, Byron, Fernando, Harindra J. S., Kreidl, O. Patrick, Delene, David J., Gultepe, Ismail
• Format: Text
• Language: unknown
• Published: arXiv 2024
• Subjects: Machine Learning cs.LG; Atmospheric and Oceanic Physics physics.ao-ph; FOS Computer and information sciences; FOS Physical sciences; Canada; North Atlantic
• Online Access: https://dx.doi.org/10.48550/arxiv.2402.06800; https://arxiv.org/abs/2402.06800

This study presents the application of generative deep learning techniques to evaluate marine fog visibility nowcasting using the FATIMA (Fog and turbulence interactions in the marine atmosphere) campaign observations collected during July 2022 in the North Atlantic in the Grand Banks area and vicinity of Sable Island (SI), northeast of Canada. The measurements were collected using the Vaisala Forward Scatter Sensor model FD70 and Weather Transmitter model WXT50, and Gill R3A ultrasonic anemometer mounted on the Research Vessel Atlantic Condor. To perform nowcasting, the time series of fog visibility (Vis), wind speed, dew point depression, and relative humidity with respect to water were preprocessed to have lagged time step features. Generative nowcasting of Vis time series for lead times of 30 and 60 minutes were performed using conditional generative adversarial networks (cGAN) regression at visibility thresholds of Vis < 1 km and < 10 km. Extreme gradient boosting (XGBoost) was used as a baseline ...