Commercial microwave link networks for rainfall observation: Assessment of the current status and future challenges

Abstract

Accurate observation of the high spatio‐temporal variability of rainfall is crucial for hydrometeorological applications. However, the existing observations from rain gauges and weather radars have individual shortcomings that can introduce considerable errors and uncertainties. A fairly new technique to get additional rainfall information is the usage of the country‐wide commercial microwave link (CML) networks for rainfall estimation by exploiting the measurements of rain‐induced attenuation along these CMLs. This technique has seen an increasing number of applications during the last years. Different methods have been developed to process the noisy raw data and to derive rainfall fields. It has been shown that CMLs can provide important line‐integrated rainfall information that complements pointwise rain gauge and spatial radar observations. There exist several limitations, though. Robustly dealing with the erratic fluctuations of the CML raw data is a challenge, in particular with the growing number of CMLs. How to correctly compensate for the biases from the effect of wet antenna attenuation for different CMLs also remains a crucial research question. Progress is additionally hampered by the lack of method intercomparisons, which in turn is hampered by restricted data sharing. Hence, collaboration is key for further advancements, also with regard to extended interaction with the CML network operators, which is a prerequisite to achieve increased data availability. In regions where rain gauges and weather radars are available, CMLs are a welcome complement. But in developing countries, which are characterized by weak technical infrastructure and which often suffer from water stress, additional rainfall information is a necessity. CMLs could play a crucial role in this respect. This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Extremes Science of Water > Methods

Atmospheric observations with E-band microwave links – challenges and opportunities

“…The dry-wet classification has been reported as an important step in CML pre-processing as it minimizes unwanted changes in attenuation level by setting the baseline separately for each event from the relatively short period before the event (Chwala and Kunstmann, 2019;Overeem et al, 2011). Here, dry-wet weather classification was not used for baseline identification.…”

A General Overview of the Risk-Reduction Strategies for Floods and Droughts

“…The deployment of large-scale and economically efficient sensory networks is expected, and the quantity and quality of the monitoring can be increased and improved accordingly [113,114]. New techniques, such as the application of commercial microwave links for rainfall observations, the analysis of pictures from cameras, or the crowdsourcing of personal weather stations, are rapidly developing to extend the horizon of monitoring for sustainability [115][116][117][118]. Other than the monitoring task itself, gauging stations according to their locations (e.g., upstream of a watershed) can be used to for flood forecasts at a downstream area [119,120].…”

Rainfall monitoring with opportunistic sensors

“…Because it is possible to log power levels in real time, this technique allows for real-time rainfall monitoring (Chwala et al, 2016;Andersson et al, 2017;Chwala et al, 2018). Messer & Sendik (2015), Uijlenhoet et al (2018), and Chwala & Kunstmann (2019) provide reviews on the details and background of this technique.…”

“…We refer to Messer & Sendik (2015); Uijlenhoet et al (2018) and Chwala & Kunstmann (2019) for a comprehensive overview of research on microwave links for rainfall monitoring. Section 4.4.2 details the method to retrieve rainfall estimates from the dataset that was chosen in this study.…”

“…Leijnse et al, 2008;Zinevich et al, 2010;Overeem et al, 2011;Chwala et al, 2012) and produce rainfall maps (Overeem et al, 2013a(Overeem et al, , 2016b with real-time applicability (Chwala et al, 2016;Andersson et al, 2017;Chwala et al, 2018). Comprehensive overviews of literature on this technique were provided by Messer & Sendik (2015), Uijlenhoet et al (2018), and Chwala & Kunstmann (2019). Several tools have been developed, documented and made (freely) available for users to construct rainfall observations with CML data: 'Rcmlrain' (https://github.com/ fenclmar/Rcmlrain), 'RAINLINK' (https://github.com/overeem11/RAINLINK), 'pycomlink' (https://github.com/pycomlink/pycomlink), and 'pySNMPdaq' (https:// github.com/cchwala/pySNMPdaq).…”