Tropical Cyclone Intensity Prediction Using BP-RNN From GPS-Derived Precipitable Water Vapor and Surface Meteorological Data

Abstract Tropical cyclones frequently threaten tropical coastal areas, making accurate prediction vital. While cyclone track forecasting has improved, predicting cyclone intensity remains challenging. This study uses precipitable water vapor (PWV) and other surface meteorological data to predict wind intensity during tropical cyclone Seroja in southern Indonesia. Data from two GPS stations, CKUP and CRTE, near the cyclone’s path, were analyzed. We employed neural network (NN) algorithms to model nonlinear relationships between variables, utilizing backpropagation to minimize error. The NN was fed with time series data across various hour window sizes (0h, 6h, 9h, and 12h), under the assumption that current parameters influence future conditions. Independent variables included PWV, ZTD, partial pressure of water vapor, temperature, and air pressure, with additional attributes implemented in multiple scenarios. Two years of data (2019-2020) were used to train the model, and wind velocities were estimated during cyclone Seroja. At CKUP, scenario 1 with a 9h window size achieved a probability of detection (POD) of 89% and a critical success index (CSI) of 84%. At CRTE, scenario 4 with a 6h window size achieved a POD of 73% and a CSI of 55%. The root mean square error for predicted wind speed was 1.32 m/s at CKUP and 2.08 m/s at CRTE. This study demonstrates the potential of integrating GPS and meteorological data to enhance cyclone intensity prediction, especially in cyclone-prone regions like Indonesia, offering a valuable contribution to local and global disaster preparedness.