Improving Ensemble Forecasts of Abnormally Deflecting Tropical Cyclones with Fused Atmosphere-Ocean-Terrain Data

Abstract

Deep learning-based tropical cyclone (TC) forecasting methods have demonstrated significant potential and application advantages, as they feature much lower computational cost and faster operation speed than numerical weather prediction models. However, existing deep learning methods still have key limitations: they can only process a single type of sequential trajectory data or homogeneous meteorological variables, and fail to achieve accurate forecasting of abnormal deflected TCs. To address these challenges, we present two groundbreaking contributions. First, we have constructed a multimodal and multi-source dataset named AOT-TCs for TC forecasting in the Northwest Pacific basin. As the first dataset of its kind, it innovatively integrates heterogeneous variables from the atmosphere, ocean, and land, thus obtaining a comprehensive and information-rich meteorological dataset. Second, based on the AOT-TCs dataset, we propose a forecasting model that can handle both normal and abnormally deflected TCs. This is the first TC forecasting model to adopt an explicit atmosphere-ocean-terrain coupling architecture, enabling it to effectively capture complex interactions across physical domains. Extensive experiments on all TC cases in the Northwest Pacific from 2017 to 2024 show that our model achieves state-of-the-art performance in TC forecasting: it not only significantly improves the forecasting accuracy of normal TCs but also breaks through the technical bottleneck in forecasting abnormally deflected TCs.

Figures (5)

• Figure 1: Abnormal deflection of TC GEIMI during landfall in Taiwan in 2024. The Central Mountain Range (peaking at nearly 4,000 m) interacted with GEIMI's low-level circulation within the 600–700 hPa vertical layer, inducing a venturi effect that guided its low-level center southward. Satellite imagery revealed complete eye dissolution after GEIMI's collision with the mountain range.
• Figure 2: The architecture of AOT-TCNet, comprising Encoders and TMA-MoE. The environment data $D$ and observed trajectories $X$ are encoded and passed to the fusion module. The experts can predict different mode trajectory distributions for the given observation. The gating network estimates probabilities $\pi$ for the experts. The model samples or selects a expert from $\pi$ and predicts a trajectory $Y$ conditioned on the features $c$.
• Figure 3: Forecast results for selected TCs in 2024. Historical trajectories are shown in red, ground truth future paths in blue, and model-predicted trajectories in green.
• Figure 4: Ensemble forecasting results for abnormally deflected TCs in 2024. Historical trajectories are shown in red, actual future tracks in blue, predicted paths in green, and shaded regions denote the projected trajectory trends.
• Figure 5: Results showcase trajectory predictions using AOT-TCNet for the TC YAGI. The red dots are input, the green dots are prediction, and the blue dots are GT.