Global Tropical Cyclone Intensity Forecasting with Multi-modal
  Multi-scale Causal Autoregressive Model
Xinyu Wang Kang Chen Lei Liu Tao Han Bin Li Lei Bai
Accurate forecasting of Tropical cyclone (TC) intensity is crucial for
formulating disaster risk reduction strategies. Current methods predominantly
rely on limited spatiotemporal information from ERA5 data and neglect the
causal relationships between these physical variables, failing to fully capture
the spatial and temporal patterns required for intensity forecasting. To
address this issue, we propose a Multi-modal multi-Scale Causal AutoRegressive
model (MSCAR), which is the first model that combines causal relationships with
large-scale multi-modal data for global TC intensity autoregressive
forecasting. Furthermore, given the current absence of a TC dataset that offers
a wide range of spatial variables, we present the Satellite and ERA5-based
Tropical Cyclone Dataset (SETCD), which stands as the longest and most
comprehensive global dataset related to TCs. Experiments on the dataset show
that MSCAR outperforms the state-of-the-art methods, achieving maximum
reductions in global and regional forecast errors of 9.52% and 6.74%,
respectively. The code and dataset are publicly available at
https://anonymous.4open.science/r/MSCAR.