Nowcasting tropical rainfall events using Commercial Microwave Links

To mitigate the impact of severe storms, accurate and timely high-resolution precipitation forecasts are crucial. In the tropics, however, many low- and middle-income countries typically lack the near surface rainfall sensors to provide such a nowcast. Weather radars are largely unavailable, and rain gauge networks are often sparse or poorly maintained, and not available in (near) real-time. Satellite precipitation products do provide valuable precipitation information in these regions, but often come with the drawback of having a spatial or temporal resolution too low to nowcast convective storms at the kilometer scale.

A viable and ‘opportunistic’ source of near-surface high-resolution space-time rainfall estimates is based on the rain-induced signal attenuation experienced by commercial microwave links (CMLs) in cellular communication networks. This idea exploits the fact that the EM waves travelling between two antennas on different cell phone towers are scattered and absorbed by raindrops causing the strength of the signal to be partially attenuated. Using the open source algorithm RAINLINK we convert this specific attenuation to path-averaged rainfall intensities, essentially creating a network of virtual rain gauges which we then interpolate to create 2D rainfall maps, every 15 minutes.

In this work we investigate the potential to use these CML derived rainfall maps as input into a conventional nowcasting algorithm, pySTEPS. The analysis is based on a CML network from Sri Lanka. The data set spans 15 months across 2019 and 2020. For each of the four monsoon seasons represented in the data set we define extreme events of different duration, ranging from 1 to 24 hours. These events are used as input to create probabilistic nowcasts in pySTEPS for lead times up to three hours. The nowcasts are evaluated spatially against the QPE at multiple catchments, and using 21 hourly rain gauges as an independent point reference source. 

Based on our findings we point out the opportunities and limitations of using CML data for nowcasting tropical storms. Finally, we highlight where CMLs can complement other remotely sensed rainfall estimates, for example from geostationary satellites, to provide more accurate nowcasts and as such potentially have impact in an operational setting too.