SII-NowNet: A machine learning tool for nowcasting convection initiation and intensification in the Tropics

Nowcasting developing convection is a crucial component of early warning systems in the Tropics. While machine learning has proven effective for radar-based nowcasting, the lack of radar coverage across much of the Tropics creates a significant capability gap. This study presents Simple Initiation and Intensification Nowcasting neural Network (SII-NowNet), a machine learning tool that uses satellite brightness temperatures to produce probabilistic nowcasts of intensifying and initiating convection in the Tropics. SII-NowNet is first demonstrated over Sumatra, Indonesia—a densely populated tropical island with frequent convective activity. For nowcasts of intensifying convection, SII-NowNet outperforms an optical flow model for lead times of 1–6 hours but begins to over-predict events beyond 3 hours, indicating its limit of capability. For nowcasts of initiating convection, SII-NowNet’s limit of capability is reached at 2 hours, beyond which it over-predicts events and is outperformed by climatology. SII-NowNet is trained on 8,661 samples (12 months of data), but sensitivity testing shows that the number of samples can be reduced to three weeks for intensification and three months for initiation, before its outperformed by climatology. This has practical implications for the implementation and further development of SII-NowNet in resource-constrained settings. To exemplify generalisability in other Tropical regions, SII-NowNet is tested over New Guinea, Zambia, Congo and West Africa. Without retraining or region-specific tuning, SII-NowNet achieves skill scores comparable to those over Sumatra. Overall, SII-NowNet’s promising results, combined with ease of applicability across the Tropics, make it a valuable tool for future operational nowcasting.