Tuned Fusion Deep Learning Approach for Unsupervised Cyclone Passive Microwave Rainfall Imagery Prediction
In one sentence. Tuned fusion deep learning model forecasts tropical cyclone passive microwave rainfall images from satellite data, with no quantitative results reported in the abstract.
Abstract
In many cases, tropical cyclones (TCs) can bring about widespread, intense rainfall because of the volume of water vapour they contain. High-resolution, passive microwave (PMW) rainfall assessment of tropical cyclones is essential for disaster forecasting tropical cyclones. Still, it remains a tricky issue because of the low temporal resolution of sensing devices. This research makes an effort to address this issue by directly forecasting PMW rainfall pictures of tropical cyclones. We developed a tuned fusion learning approach to forecast unannotated PMW image classes. The study outcomes demonstrate the system’s ability to retrieve essential features from PMW images successfully. The global applicability of this deep learning technique is promising. It offers a fresh angle on satellite-based typhoon rainfall forecasting, which can yield valuable insights for real-time visualization of typhoon precipitation around the world in operational activities.
Labels
- Topic
- Nowcasting, Observations & remote sensing, Extremes & hazards
- Type
- New model or method
- Method
- Other
- Time scale
- 0–6 h
- Domain
- Global
- Data
- Satellite
- Status
- Research
- Identifiers
- OpenAlex:W7217394415
- Added
- 2026-10-22
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