Abstract
Abstract. During the last decade, rainfall monitoring using signal-level data from commercial microwave links (CMLs) in cellular communication networks has been proposed as a complementary way to estimate rainfall for large areas. Path-averaged rainfall is retrieved between the transmitting and receiving cellular antennas of a CML. One rainfall estimation algorithm for CMLs is RAINLINK, which has been employed in different regions (e.g., Brazil, Italy, the Netherlands, and Pakistan) with satisfactory results. However, the RAINLINK parameters have been calibrated for a unique optimum solution, which is inconsistent with the fact that multiple similar or equivalent solutions may exist due to uncertainties in algorithm structure, input data, and parameters. Here, we show how CML rainfall estimates can be improved by calibrating all parameters of the algorithm systematically and simultaneously with the stochastic particle swarm optimization method, which is used for the numerical maximization of the objective function. An open dataset of approximately 2800 sub-links of minimum and maximum received signal levels over 15 min intervals covering the Netherlands (∼ 35 500 km2) is employed: 12 d are used for calibration and 3 months for validation. A gauge-adjusted radar rainfall dataset is utilized as a reference. Verification of path-average daily rainfall shows a reasonable improvement for the stochastically calibrated parameters with respect to RAINLINK's default parameter settings. Results further improve when averaged over the Netherlands. Moreover, the method provides a better underpinning of the chosen parameter values and is therefore of general interest for calibration of RAINLINK's parameters for other climates and cellular communication networks.
Highlights
Accurate rainfall observations with high temporal and spatial resolution are crucial for, e.g., agriculture, meteorology, flood warnings, and freshwater resource management
With the new optimization method, we provide a better underpinning of parameter values for this commercial microwave links (CMLs) rainfall retrieval algorithm
It is important to highlight that the max(Pmin) is only computed if at least a minimum number of hours of data are available; otherwise it is not computed and no rainfall intensities are retrieved
Summary
Accurate rainfall observations with high temporal and spatial resolution are crucial for, e.g., agriculture, meteorology, flood warnings, and freshwater resource management. For many places on the Earth’s land surface, accurate rainfall information is lacking, especially from groundbased measurements at sub-daily and daily timescales (Sun et al, 2018). The largest worldwide rain gauge database, maintained by the Global Precipitation Climatology Centre (GPCC), had 45 000 rain gauges in 1961– 2000, down to 10 000 after 2016. This decrease was caused by a delay in data delivery and by post-processing at GPCC (Schneider et al, 2021). Decreasing in the past due to quality control, the GPCC database has been increasing in recent years as a result of delivery of updates as well as supplements with additional stations and long time series of data (Schneider et al, 2021)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
