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Object‐based deep learning for probabilistic convective‐core nowcasting from satellite data
- Truvace record
- TRV-2026-2565raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
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Abstract Flash flooding from intense rainfall causes major damage and loss of life across Africa, particularly in the Sahel, where rainfall is dominated by mesoscale convective systems. Convective cores, which represent regions of intense convective activity, evolve rapidly, limiting short‐term predictability, especially in data‐sparse regions where numerical weather prediction models face substantial uncertainty in convective initiation, evolution, and organisation. This study presents an object‐based deep‐lear…
The gain
In a model evaluation for the western Sahel, a transformer called NCAST made skilful probabilistic forecasts of intense storm cores one, three and six hours ahead, beating persistence and an operational conditional-climatology benchmark.
Sources
1 on file- Quarterly Journal of the Royal Meteorological Societydoi:10.1002/qj.70303
Cite this record
v1Truvace Impact Record TRV-2026-2565, v1: “Object‐based deep learning for probabilistic convective‐core nowcasting from satellite data.” Truvace, 2026-11-12. /record/TRV-2026-2565 (accessed on the day you copy it). sha256 67ebdd1503f5038a…
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