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Nowcasting convective cores using deep learning
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DOI：10.1002/qj.70284.png)
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Abstract
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Extreme precipitation events in the Tropics are often linked to convective storms. Storm nowcasting systems for early warning have been recognized as a necessary adaptation strategy to increase public resilience in a rapidly evolving climate. Ongoing advances in deep-learning techniques now allow better exploitation of satellite data, including where ground-based radar networks and computational resources are limited. This study presents an easily reproducible model (Network for Nowcasting Convective Cores, NetNCC) for nowcasting convective cores in near real time at a fine spatial resolution (
). NetNCC uses Meteosat Second Generation thermal infrared brightness temperature images at three successive time steps to predict the probability of convective cores for lead times between 1 and 6 hr. A spatially enhanced loss function (fractions skill score) captures the atmospheric conditions within the neighbourhood of each pixel. Skilful probabilistic predictions are achieved at fine spatial resolutions (
) for short lead times of
and at coarser resolution (180–270 km) for lead times
. Nowcast accuracy shows a clear diurnal cycle, with higher accuracy during the daytime when convection frequency is at a maximum in eastern and southern Africa. NetNCC performs better for short-lived convective cores that develop over eastern Africa than for cores embedded in typically long-lived West African convection. Compared with persistence, NetNCC nowcasts are considerably better particularly for fine spatial scales. Further input information on atmospheric and land-surface states alongside thermal infrared imagery may be required to improve longer (
4 hr) lead-time predictions. Nevertheless, our results indicate considerable potential of NetNCC for nowcasting convective activity, and hence intense rainfall, in at-risk regions and illustrate the utility to forecasters for operational applications.
Keywords:
Africa
convective cloud cores
deep learning
nowcasting
U-Nets
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