Using VIL density for identification of storm nuclei, tracking and nowcasting in the Barcelona Metropolitan Area

Urban resilience to extreme weather events is increasingly threatened by the intensification of short-duration rainfall, often leading to urban flooding. This study focuses on improving the prediction of heavy rainfall in the Metropolitan Area of Barcelona, located on the Catalan Mediterranean coast in the northeast of the Iberian Peninsula, using high-resolution radar products and rain gauge data. Despite the decrease in average of annual rainfall in the AMB over recent decades, the intensity rates of some storm events are among the highest of the existing series, with occasional convective events causing urban flooding and severe disruptions for the urban region. The latest climate change reports (IPCC 2022) point towards an increase in frequency and intensity of heavy rainfall events in the region.

An extensive dataset of rainfall days spanning from 2014 to 2022 is analysed, including volumetric radar products (VIL, Echo Top), surface rainfall measurements, and incident reports. A bottom-up approach is used to identify 45 intense convective days with significant impacts in the study region. A radar-based nowcasting approach is introduced, utilizing a two-dimensional radar product with three-dimensional atmospheric information to enhance early warnings in the urban region, with high spatial resolution. This approach focuses on the convective parts of storms through Vertical Integrated Liquid (VIL) density-based tracking and nowcasting with six-minute temporal updates to characterize storm centroids and their evolution. The density of VIL (DVIL), derived from radar composites, provides vertical storm structure information in a two-dimensional format, enabling faster data processing without losing volumetric capabilities.

The findings reveal spatial coherence between maximum DVIL intensities and maximum rainfall locations, with all events exceeding the 2.5 g/m³ DVIL threshold coinciding with high-intensity rainfall. Centroid trajectories show seasonal patterns, with some summer events originating from scattered sources and moving more slowly, while some autumn ones align along the coast and propagating inland. The time lag between initial DVIL detection and peak precipitation for the analysed days ranges from 30 minutes to over two hours, offering critical lead times for early warnings.

This study demonstrates the strengths and limitations of DVIL as a predictor of heavy rainfall in urban areas. The RaNDeVIL module shows promise for operational nowcasting, with necessary improvements to address complex interactions of the storm dynamics and more complex modelling to nowcast longer timescales. These advancements aim to enhance resilience to intense precipitation in the Metropolitan Area of Barcelona under changing climatic conditions.

 

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 101037193.