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Workshop: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning: Blending New and Existing Knowledge Systems

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

Eren Gultepe · Sen Wang · Byron Blomquist · Harindra Fernando · Patrick Kreidl · David Delene · Ismail Gultepe


This study presents the application of generative deep learning techniques to evaluate marine fog visibility conditions by nowcasting visibility 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 Vaisala Weather Transmitter model WXT50, mounted on the Research Vessel (R/V) Atlantic Condor. To perform nowcasting, the time series of fog visibility (Vis), wind speed (Uh), dew point depression (Ta-Td), and relative humidity (RHw) 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 method for comparison against cGAN. At the 30 min lead time, Vis was best predicted with cGAN at Vis < 1 km (RMSE = 0.151 km) and with XGBoost at Vis < 10 km (RMSE = 2.821 km). At the 60 min lead time, Vis was best predicted with XGBoost at Vis < 1 km (RMSE = 0.167 km) and Vis < 10 km (RMSE = 3.508 km), but cGAN error was not too far off. At both lead times for Vis < 1 km, the cGAN model tracked the variation in Vis very well, which suggests that there is potential for future generative analysis of marine fog formation using observational meteorological parameters.

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