Analysis of Generalization Performance of Tornado Detection Models: A Cross-Domain Evaluation from U.S. to Chinese Weather Radar Observations

Tornadoes pose severe threats, yet their low frequency in China creates a labeled data scarcity that hinders training robust detection models. Leveraging abundant U.S. data offers a solution, though cross-domain generalization remains challenging due to distinct climatic environments and heterogeneous radar systems. This study systematically evaluates the generalization capability of three representative models—TORP, TORP-XGB, and TDA-CNN—trained on the U.S. TorNet dataset and applied to Chinese CINRAD observations (2020–2024) via a zero-shot transfer strategy. The results indicate that while all models demonstrated robust performance in the source domain (with POD values of 0.75, 0.72, and 0.71 for TORP, TORP-XGB, and TDA-CNN, respectively), they experienced varying degrees of performance attenuation in the target domain (with POD values dropping to 0.56, 0.48, and 0.41, respectively). Notably, the TORP model exhibited superior robustness with minimal performance degradation. Further analysis primarily attributes this cross-domain degradation to three factors: disparities in radar systems, magnitude differences in tornado rotational features, and data quality issues. Crucially, sensitivity experiments confirm that linear feature enhancement substantially improves the detection rate and effectively mitigates the cross-domain performance gap, albeit at the cost of increased false alarms. These findings provide a reference for the cross-domain deployment of tornado identification models and future improvements in transfer learning strategies.