Predicting tropical cyclone formation and its landfall’s characteristics

Abstract

Disaster risk reduction is integral to social and economic development as per the 2030 Agenda of Sustainable Development. Among all the natural disasters, storms and floods have significant contributions in terms of their frequency, the number of affected people, and economic loss. In the tropical and subtropical regions of the world, tropical cyclones are the primary cause of storms and floods in the coastal areas. In the last 50 years, about 1942 disasters happened due to tropical cyclones that killed about 0.8 million people and caused a financial loss of US$1407.6 billion. Any tropical cyclone related prediction task is challenging as the atmospheric and oceanographic causal factors are multidimensional in nature and have a complex non-linear relationship among them. Tropical cyclone-related research mainly focuses on predicting a cyclone formation, track, intensity, storm surge, and associated rainfall. The existing operational models are primarily numerical and statistical in nature. The numerical methods are computationally involved and time-consuming. The statistical techniques are too simple to capture complex non-linear relationships between a large number of causal factors with spatial and temporal dimensions. Recently various deep learning studies appeared that successfully answer various tropical cyclone-related prediction problems. This research work tries to answer various prediction problems related to a cyclone’s different development phases. Starting from the formation (genesis) of a cyclone, the first work proposes a deep-learning model that detects the formation of a cyclone well advance in time in six ocean basins across the world. If a cyclone dies over the sea, one can simply ignore it, as it will not cause significant damage. But if it crosses the ocean and moves over to the land (known as Landfall), it causes a colossal disaster. Therefore, in our second work, we propose a deep learning classification model that answers the fundamental question of whether a cyclone will make a Landfall or not. The extent of disaster caused by a cyclone is determined by the location, intensity, and time of its Landfall. We propose a model that predicts the intensity, location, and time of Landfall for a cyclone across six ocean basins of the world. In the first work, the proposed deep learning model forecasts the tropical cyclone formation in six ocean basins of the world. It achieves a 5-fold accuracy in the range of 91.7% − 97.7%, 96.4% − 99.3%, 95.4% − 99.1%, and 86.9.7% − 92.9% at lead times of 24h, 36h, 48h, and 60h respectively using only 12h of data. The second proposed model tries to answer the fundamental question whether a cylcone will make the landfall or not? It achieves an accuracy in the range of 96.4% − 99.2% and 93.0% − 98.7% using 12h or 24h of data (during initial 72h of cyclones progress) respectively across all six ocean basins of the world. The third work focuses on predicting the landfall’s characteristics in the form of its location, time and intensity across six ocean basins of the world. The first model in this direction achieves a 5-fold cross-validation MAE of 4.24(±0.40)knots, 4.5(±0.58)h, and 51.7(±1.20)KM for landfall’s intensity, time, and location, respectively, using any 21h of data during the course of a cyclone in north Indian ocean basin. Our model outperforms the landfall’s location accuracy reported by IMD on its website. The second model achieved the 5-fold cross-validation MAE in the range of 66.18(±2.87)KM- 158.92(±12.62)KM and 4.71(±0.54)h-8.20(±2.96)h for location and time prediction respectively across all six ocean basins of the world.