identification of robust predictors of tropical cyclones using causal effect network over the north indian ocean

Tropical cyclone (TC) activity varies substantially yearly, and tropical cyclone-related damage also changes. Longer-term prediction of tropical cyclones plays an important role in reducing the wear and human loss caused by TCs. In this study, we have used a Causal-network-based algorithm to find the main development regions and precursors responsible for TC genesis and intensification. However, all the extreme events are interconnected through various global links. Therefore, analysis of the teleconnection and correlation of Tropical Cyclones with El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and North Atlantic Oscillation (NAO) during the satellite era (1980-2020) over the North Indian Ocean (NIO) basins using this Causal Effect Network (CEN) based algorithms is checked. The most appropriate metric for cyclone energy is Accumulated Cyclone Energy (ACE); its correlation with the various factors are investigated. We examined the variation in TCs activity during all three phases (positive, negative, and neutral phases).The results show an increasing trend in ACE over the NIO region during that specific period. The duration of most intense cyclones is increased, but their frequency decreases in this period. A shift in ACE starts after 1997 and still rises significantly. Analysis of Sea Surface Temperature (SST), Vertical Wind Shear (VWS) between 850 and 250 hPa, mid-tropospheric (800 hPa) Relative Humidity (RH), low level (850 hPa) Relative Vorticity (RV), and Tropical Cyclone Heat Potential (TCHP) is done, and it shows positive changes and variability of ACE. These results may help get better knowledge about the atmospheric or oceanic teleconnections between the events, and improved tropical cyclone prediction can help reduce the loss caused by the TCs.