Opportunities and challenges for Rainfall Nowcasting with Commercial Microwave Links in the Tropics

In general, quantitative precipitation estimates from weather radars are used as input into nowcasting models to produce high-resolution accurate and timely precipitation forecasts, up to several hours ahead. 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. In this study we investigate whether 2D rainfall fields created by interpolating path- averaged rainfall intensities from CMLs can be used as a standalone input into a conventional nowcasting algorithm, pySTEPS.

This work 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. We address challenges surrounding the nature of the input data, dealing with sparse or unequal CML coverage, and how to handle this in pySTEPS. Based on our findings we also highlight where other remotely sensed rainfall estimates, for example from geostationary satellites, can be used to complement CML based rainfall estimates to provide more accurate nowcasts.

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