Tropical cyclones (TCs) are complex weather systems with strong winds and heavy rainfall, causing substantial loss of life and property. Therefore, accurate TC forecasting is crucial for the effective prevention of disasters caused by TCs. TC forecasting can be regarded as a spatio-temporal prediction problem. It has been proven that using multi-modal data can effectively introduce atmospheric information to achieve better prediction results and higher interpretability. But it also introduces inevitably introduces noise into the prediction process. The diffusion model's unique noise modeling capability can reduce prediction noise when using multi-modal datasets. However, adapting it to TC forecasting has two main challenges: how to extract valuable information from multi-modal data, and how to utilize them to guide the generation process. For the first challenge, while recent methods can predict multiple TC attributes using multi-modal data, they often overlook the interdependence of multiple attributes and the semantic gap between modalities. Considering the interdependence of attributes, we propose two condition generators that capture the commonalities and characteristics of TC attributes, extracting spatio-temporal and environmental features and incorporating expert knowledge. To reduce the semantic gap between multi-modal data, we introduce the PGSA-LSTM module to map primary and auxiliary modalities. For the second challenge, we propose a novel Bi-condition diffusion model that sequentially processes conditions from the characteristics to commonalities of attributes, thereby expanding the guidance information that the diffusion model can accept. Our results surpass state-of-the-art deep learning models and outperform the numerical weather prediction model used by the China Central Meteorological Observatory. TC-Diffuser shows high generalizability across global ocean areas, strong robustness in handling missing data, and higher computational efficiency.

Tropical Cyclone (TC) estimation aims to accurately estimate various TC attributes in real time. However, distribution shifts arising from the complex and dynamic nature of TC environmental fields, such as varying geographical conditions and seasonal changes, present significant challenges to reliable estimation. Most existing methods rely on multi-modal fusion for feature extraction but overlook the intrinsic distribution of feature representations, leading to poor generalization under out-of-distribution (OOD) scenarios. To address this, we propose an effective Identity Distribution-Oriented Physical Invariant Learning framework (IDOL), which imposes identity-oriented constraints to regulate the feature space under the guidance of prior physical knowledge, thereby dealing distribution variability with physical invariance. Specifically, the proposed IDOL employs the wind field model and dark correlation knowledge of TC to model task-shared and task-specific identity tokens. These tokens capture task dependencies and intrinsic physical invariances of TC, enabling robust estimation of TC wind speed, pressure, inner-core, and outer-core size under distribution shifts. Extensive experiments conducted on multiple datasets and tasks demonstrate the outperformance of the proposed IDOL, verifying that imposing identity-oriented constraints based on prior physical knowledge can effectively mitigates diverse distribution shifts in TC estimation.