Camp Springs
Global Precipitation Nowcasting of Integrated Multi-satellitE Retrievals for GPM: A U-Net Convolutional LSTM Architecture
Rahimi, Reyhaneh, Ebtehaj, Ardeshir, Behrangi, Ali, Tan, Jackson
This paper presents a deep learning architecture for nowcasting of precipitation almost globally every 30 min with a 4-hour lead time. The architecture fuses a U-Net and a convolutional long short-term memory (LSTM) neural network and is trained using data from the Integrated MultisatellitE Retrievals for GPM (IMERG) and a few key precipitation drivers from the Global Forecast System (GFS). The impacts of different training loss functions, including the mean-squared error (regression) and the focal-loss (classification), on the quality of precipitation nowcasts are studied. The results indicate that the regression network performs well in capturing light precipitation (below 1.6 mm/hr), but the classification network can outperform the regression network for nowcasting of precipitation extremes (>8 mm/hr), in terms of the critical success index (CSI).. Using the Wasserstein distance, it is shown that the predicted precipitation by the classification network has a closer class probability distribution to the IMERG than the regression network. It is uncovered that the inclusion of the physical variables can improve precipitation nowcasting, especially at longer lead times in both networks. Taking IMERG as a relative reference, a multi-scale analysis in terms of fractions skill score (FSS), shows that the nowcasting machine remains skillful (FSS > 0.5) at the resolution of 10 km compared to 50 km for GFS. For precipitation rates greater than 4~mm/hr, only the classification network remains FSS-skillful on scales greater than 50 km within a 2-hour lead time.
Operator Guidance Informed by AI-Augmented Simulations
Edwards, Samuel J., Levine, Michael
Operational guidance is provided in the form of selection of speeds and headings, and is generally based on accessing ship motions response predictions from a pre-computed database or lookup table for a given condition. Operational guidance is an important consideration in the survival of a ship and has been the focus of many International Maritime Organization (IMO) publications, IMO (1995), IMO (2007), IMO (2020). Recommendations for ship-specific operational guidance has been developed and discussed in the interim guidelines of the Second Generation Intact Stability by IMO, IMO (2020). While these guidelines are certainly useful in design and at sea, they are not comprehensive. The ocean environment is random and complex.