Nowcasting Convective Weather: Evaluation, Development and Application of Techniques

Convective storms produce hazardous conditions that can lead to natural disasters such as flooding and landslides. Their societal and economic impacts are felt throughout the world, particularly in vulnerable tropical regions. Providing effective early warnings for such events requires accurate short-term prediction — a major challenge in meteorology, especially in the Tropics, where numerical weather prediction models have low skill. Nowcasting fills this capability gap by rapidly generating weather predictions with lead times on the scale of minutes to hours.

This thesis presents advances in convection nowcasting, including satellite-based solutions for the Tropics and the extension of traditional nowcasting techniques to flood prediction. First, traditional nowcasting tools, which are typically radar-based, are applied to the Maritime Continent – a tropical region that experiences regular convective activity – using satellite brightness temperature retrievals, a viable alternative to radar data, which is scarcely available in this region. Overall, these tools demonstrate skill in nowcasting propagating convection up to 4 hours in advance, but struggle to capture the initiation and growth of convection over mountainous regions during the afternoon period. Next, a novel satellite-based machine learning nowcasting tool, SII-NowNet, is introduced. SII-NowNet produces skilful nowcasts of convection initiation up to 2 hours in advance and convection intensification up to 3 hours in advance, over the Maritime Continent. Using Zambia as an example region, SII-NowNet shows that it can generalise well to a previously unseen tropical region without any re-training. Finally, traditional nowcasting techniques are applied to develop N-FOREWARNS, a surface water flood nowcasting tool that generates useful flood risk maps up to 3 hours in advance. N-FOREWARNS is both quantitatively verified and qualitatively assessed by expert users, demonstrating added value to existing operational capabilities.

Overall, the nowcasting developments presented in this thesis show the potential to strengthen early warning systems via improved nowcasting tools and thereby enhance resilience to hazardous weather in vulnerable communities.