Generative Nowcasting of Marine Fog Visibility in the Grand Banks area
  and Sable Island in Canada
Eren Gultepe Sen Wang Byron Blomquist Harindra J. S. Fernando O. Patrick Kreidl David J. Delene Ismail Gultepe
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 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 the cGAN RMSE was similar to XGBoost. Despite nowcasting Vis at
30 min being quite difficult, the ability of the cGAN model to track the
variation in Vis at 1 km suggests that there is potential for generative
analysis of marine fog visibility using observational meteorological
parameters.