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Prediction of tropical cyclone (TC) intensity is one of the ground challenges in weather forecasting, and rapid intensification (RI) is a key part of that prediction. Most of the current RI studies are based on a selected variable (feature) set, which is accumulated based on expert expertise in past studies of TC intensity changes and RI. Are there any more important variables in TC intensity predictions that were not identified in past studies? A systematic and comprehensive search for those variables from vast amounts of gridded data, satellite images, and other historically collected data could be helpful for answering the above question. Artificial intelligence (AI) has the capabilities to distill features in large array data, and it is helpful in identifying new features related to TC intensity changes in general and RI in particular. Here, we leverage the local linear embedding (LLE) dimension reduction techniques to the European Centre for Medium-Range Weather Forecasts ERA-Interim reanalysis data for identifying new variables related to RI. In addition to the well-known features in the SHIPS (statistical hurricane intensity prediction scheme) database, we identified other significant features, such as 400 and 450 hPa meridional wind, 1000 hPa potential vorticity, and vertical pressure speed, that could help the understanding and prediction of RI occurrences. Furthermore, our AI system outperforms our baseline model with SHIPS data only by 26.6% and 8.4% in kappa and PSS (Peirce’s skill score), respectively.

atmosphere

An Advanced Artificial Intelligence System for Identifying the Near-Core Impact Features to Tropical Cyclone Rapid Intensification from the ERA-Interim Data