Characterizing Regional Convective Rain Cell Features Based upon 13 Years of High-Resolution Radar Images

This work is part of the EU-funded FloodCitiSense project (http://floodcitisense.eu), the aim of which is to develop an urban pluvial flood forecasting and warning service for and by the citizens. The specific focus of this work is to characterize convective storm features over one of the project pilot cities – Birmingham (UK) – and in turn to provide useful ‘local’ knowledge that can contribute to the development of the pluvial flood forecasting service. The proposed work comprises two stages: 1) development of a regional convective rain cell database, and 2) characterization of storm cells’ features in relation to the pilot.

In the initial development of the database, individual convective rain cells and the traces between any two successive cells were extracted from the high-resolution UK Met Office (Nimrod) radar rainfall archive for the period of 2005-2017 over a 500 × 500 km² area centered at Birmingham city (Wang et al., 2018). These cells and traces were identified using a new object-based convective storm cell tracking algorithm developed by Muñoz et al. (2018). Incorporating an optical flow-based rain-field tracking and a multi-threshold object identification techniques, this new algorithm enables the accurate isolation and tracking of convective rain cells from high-resolution radar reflectivity data. In addition, the algorithm can handle the merging and splitting processes of rain cells, thus making the identified cells and traces more accurate and detailed than other storm tracking algorithms.

The extracted rain cells and traces have been stored using TimescaleDB (http://www.timescale.com/), which is an open-source database optimised for fast and complex queries. This database combines the advantage of being a full SQL (Structured Query Language) relational database, while scaling in ways previously reserved for NoSQL databases. Therefore, the properties of cells and traces can be efficiently queried using standard SQL commands.

In the second stage, the specific properties of the identified cells and traces and their relationship to the pilot city were extracted from the database for further analyses. These include characterizing statistical features of rain cells’ intensities, extents, lifespans and their inter-relationships, and mapping the locations of rain cells, traces and given properties over the pilot city. Preliminary results of rain cell and trace mapping enable us to locate the ‘hotspots’ that are prone to the initiation and maturing of convective cells. In addition, the mapping of the traces suggests the existence of certain storm ‘corridors’ in the southern part of Birmingham city. This provides valuable ‘local’ knowledge to the development of urban pluvial flood forecasting and warning services.

Muñoz, C., Wang, L.-P. and Willems, P.: Enhanced object-based tracking algorithm for convective rain storms and cells, Atmos. Res., 201, 144-158, 2018, doi:10.1016/j.atmosres.2017.10.027.

Wang, L.-P., Muñoz, C., Horng, T.-C., Manz, B., Ochoa-Rodriguez, S., Willems, P. adn Onof, C.: A convective rain cell database based upon high-resolution radar images: unraveling convection patterns, EGU 2018, Vienna.