Tropical rainfall monitoring with commercial microwave links in Sri Lanka

Aart Overeem,Hidde Leijnse,Linda Bogerd,Daniele Tricarico,Arjan Droste,Remko Uijlenhoet,Jan Priebe,Thomas C Van Leth

doi:10.1088/1748-9326/ac0fa6

Abstract

Commercial microwave links (CMLs) from cellular telecommunication networks can provide a valuable ‘opportunistic’ source of high-resolution space-time rainfall information, complementing traditional in-situ measurement devices (rain gauges, disdrometers) and remote sensors (weather radars, satellites). Their greatest potential lies in areas with low gauge densities and lack of weather radars, often in developing countries with a subtropical or tropical climate and generally large spatial rainfall variability. Here, the open-source R package RAINLINK is employed to retrieve CML rainfall maps covering the majority of Sri Lanka for a 3.5 month period based on CML data from on average 1140 link paths. These are compared locally to hourly and daily rain gauge data, as well as to rainfall maps from the dual-frequency precipitation radar on board the global precipitation measurement core observatory satellite. The potential of CMLs for real-time tropical rainfall monitoring is demonstrated.

Highlights

Accurate and timely surface precipitation measurements with high spatial and temporal resolution are of paramount importance for many applications such as water resources management, agriculture and weather prediction
An unprecedented Commercial microwave links (CMLs) dataset in terms of spatial and temporal coverage was used for rainfall mapping in the tropical country Sri Lanka
A spatial comparison with a high-quality satellite product (Grecu et al 2016) and an extensive local comparison with rain gauge data confirms the potential of microwave links for detailed tropical rainfall monitoring over land

Summary

Introduction

Accurate and timely surface precipitation measurements with high spatial and temporal resolution are of paramount importance for many applications such as water resources management, agriculture (irrigation scheduling and crop insurance) and weather prediction. The majority of the Earth’s land surface lacks rainfall data of sufficient quality. The total number of rain gauges from operational meteorological networks is estimated to be at most ∼0.25 million, while the number of those giving useful near-real time observations is estimated to be 8000–12 000 (Kidd et al 2017). Networks are especially sparse in developing countries (Lorenz and Kunstmann 2012). In these areas the coverage of ground-based weather radars is limited (Saltikoff et al 2019).

Results

Discussion

Conclusion