Accurate and timely estimation of tropical cyclone (TC) wind radii is significant for characterizing wind structure, disaster prevention and mitigation, etc. The existing methods have not sufficiently considered and utilized multi-modal (i.e., multi-source heterogeneous) data for wind radii estimation. Meanwhile, complete modalities (i.e., all used modalities) can hardly be available simultaneously, especially in real-time monitoring scenarios, which restricts the applicability of multi-modal estimation models. It is challenging to maintain the accuracy of wind radii estimates when confronted with the issue of missing-modality. Therefore, to address these issues, this paper aims to achieve robust TC wind radii estimation under both conditions with complete modalities and missing-modality. We first present a multi-modal fusion network, MT-TCNet, for estimating TC wind radii under conditions with complete modalities. MT-TCNet benefits from multi-modal data including satellite infrared images, reanalysis wind fields, and the physical parameter maximum sustained wind speed (MSW). MSW, which reflects TC intensity, is incorporated to embed the implicit relationship between TC intensity and wind radii. It is capable of providing superior and robust wind radii estimates in scenarios without time constraints, and can be used to generate long-term historical results. Furthermore, this paper proposes MT-TCNet-Distill to alleviate the issue of missing-modality caused by delays in ERA5 reanalysis wind fields through generalized distillation and missing modality imputation. MT-TCNet-Distill broadens the applicability of MT-TCNet, which heavily relies on reanalysis data, enabling robust wind radii estimation in real-time scenarios. Comprehensive experiments demonstrate the superior performance of MT-TCNet and MT-TCNet-Distill compared to state-of-the-art methods.

Sea surface wind (SSW) plays a pivotal role in numerous research endeavors pertaining to meteorology and oceanography. SSW fields derived from remote sensing have been widely applied; however, regional and local studies require higher-spatial-resolution SSW fields to identify refined details. Most of the existing studies based on deep learning have constructed mappings from low-resolution inputs to high-resolution downscaled estimates. However, these methods have failed to capture the relationships between multiple variables as revealed by physical processes. Therefore, this paper proposes a spatial downscaling approach for satellite sea surface wind that employs soft-sharing multi-task learning. Sea surface temperature and water vapor are included as auxiliary variables for SSW, considering the close correlation revealed by physical principles and data availability. The spatial downscaling of auxiliary variables is designed as an auxiliary task and integrated into a multi-task learning network with generative adversarial network and dual regression structures. The proposed multi-task downscaling network achieves flexible parameter sharing and information exchange between tasks through a soft-sharing mechanism and bridge modules. Comprehensive experiments were conducted with WindSat SSW products at 0.25° from Remote Sensing Systems. The experimental results validate the outstanding downscaling capability of the proposed methodology with respect to precision in comparison with buoy measurements and reconstruction quality.