Rainfall Nowcasting with Commercial Microwave Links in the Tropics: Potential and Limitations

Accurate and timely precipitation forecasts are crucial for flood early warnings and mitigating other rainfall-induced natural hazards like landslides. For forecasts up to a few hours ahead, rainfall nowcasts are increasingly being used. Generally, these nowcasts statistically extrapolate real-time remotely sensed quantitative precipitation estimates, often based on weather radars. However, the global distribution of high-resolution (gauge-adjusted, ground-based) weather radar products is heavily skewed, largely favoring Europe, Northern America, and parts of East Asia. In many low- and middle-income countries, predominantly located in the tropics, weather radars are largely unavailable due to high installation and maintenance costs, and rain gauges are often scarce, poorly maintained, or not available in (near) real-time.

A viable and ‘opportunistic’ source of high-resolution space-time rainfall estimates is based on the rain-induced signal attenuation experienced by commercial microwave links (CMLs) in cellular communication networks. Typically, every 15 minutes the minimum and maximum received signal levels are stored in network management systems by mobile network operators for quality monitoring purposes. Based on these signal levels, path-averaged rainfall intensities can be estimated, and then interpolated, to produce high-resolution rainfall maps.

In this study, we explore the potential and limitations of using CML-derived rainfall maps for nowcasting in Sri Lanka. Based on 12 months of data from 2019 and 2020 from a Sri Lankan CML network that predominantly covers the northern half of the country, we create spatial rainfall fields at 15-minute intervals. Using the rainfall nowcasting algorithm pySTEPS, probabilistic nowcasts are created for lead times up to three hours. The nowcasts are evaluated using hourly rain gauges across 12 catchments of varying sizes and with varying CML densities. The nowcasts are further compared qualitatively to snapshots from the combined radar-radiometer product based on overpasses of the Global Precipitation Mission core satellite.

This novel application of CMLs, essentially providing a ‘weather radar’ in the tropics, highlights the potential impact for operational early warning services based on opportunistic rainfall sensors in regions that lack dedicated rainfall sensors.