<![CDATA[Tropical cyclones have an extremely short detection-to-peak-impact lead time, as exemplified by Tropical Cyclone Seroja, which caused catastrophic damage in East Nusa Tenggara in April 2021, while conventional methods such as the Dvorak technique, the Advanced Dvorak Technique, and Numerical Weather Prediction remain limited in update speed and objectivity. This study applies the Temporal Convolutional Network (TCN) for nowcasting the track and intensity of Tropical Cyclone Seroja as a faster, computationally lighter alternative capable of capturing nonlinear cyclone dynamics. The study uses historical best-track data from the Bureau of Meteorology (BOM) Australia covering 1973–2026 (longitude, latitude, central pressure, wind speed), with the model trained via a sliding-window approach for predictions at 6–24-hour horizons and tested on Cyclone Seroja, evaluated using RMSE, MAPE, and R². Results show TCN performs best in track prediction (longitude and latitude RMSE of 1.01–2.97 and 0.40–1.88, consistently high R² up to 24 hours), whereas intensity performance declines sharply (central-pressure RMSE of 4.01–10.40 hPa, wind-speed RMSE of 2.87–7.22 m/s, wind-speed MAPE up to 23.77%, negative R²), associated with the interaction between Cyclone Seroja and Tropical Cyclone Odette through the Fujiwhara effect. TCN has potential as an alternative to conventional methods for tropical cyclone early-warning systems in Indonesia, particularly for track prediction, with further development needed to improve intensity-prediction accuracy.]]>
