Time-Member Progressive Inference for Long-Term Tropical Cyclone Track Forecasting

Tropical cyclone (TC) track forecasting is fraught with uncertainty due to the dynamic complexity of atmospheric environments. While ensemble forecasting provides potential predictive results by combining multiple members’ predictions, it often neglects the temporal evolution of initial condition errors within each member. On the other hand, existing deep learning-based time series forecasting (TF) methods, although capable of capturing temporal dependencies with complex network structures, rarely explicitly consider the complementary information from different ensemble members. These limitations restrict the ability to fully exploit the potential of both temporal and multimember information, resulting in suboptimal TC track forecasting. To address these challenges, we propose a Time-Member Progressive Inference (TMPI) method that uniquely integrates temporal and multimember information for long-term TC track forecasting. Unlike existing TF methods that rely on complex architectures, TMPI employs a simple yet effective linear modeling framework to capture the intrinsic temporal patterns of each member’s historical track data, mitigating initial condition error propagation over lead time. To further enhance forecasting accuracy, the TMPI model incorporates a multimember inference branch that focuses on learning the correlations and biases among ensemble members. By integrating complementary information from various members, this branch provides a more comprehensive perspective for TC track forecasting. Experiments on Northwest Pacific historical TC track data demonstrate that TMPI reduces medium to long-term ($>$24 h) prediction errors by an average of 6% in 2022 and 9% in 2023, compared to the ensemble mean of the Global Ensemble Forecast System (GEFS).