Enhanced oceanic fog nowcasting through satellite-based recurrent neural networks

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.

Keywords

• Remote sensing
• GOES-16
• deep learning
• fog
• Atlantic ocean