The tropical cyclone formation process is one of the most complex natural
phenomena which is governed by various atmospheric, oceanographic, and
geographic factors that varies with time and space. Despite several years of
research, accurately predicting tropical cyclone formation remains a
challenging task. While the existing numerical models have inherent
limitations, the machine learning models fail to capture the spatial and
temporal dimensions of the causal factors behind TC formation. In this study, a
deep learning model has been proposed that can forecast the formation of a
tropical cyclone with a lead time of up to 60 hours with high accuracy. The
model uses the high-resolution reanalysis data ERA5 (ECMWF reanalysis 5th
generation), and best track data IBTrACS (International Best Track Archive for
Climate Stewardship) to forecast tropical cyclone formation in six ocean basins
of the world. For 60 hours lead time the models achieve an accuracy in the
range of 86.9% - 92.9% across the six ocean basins. The model takes about 5-15
minutes of training time depending on the ocean basin, and the amount of data
used and can predict within seconds, thereby making it suitable for real-life
usage