Rainfall retrieval algorithm for commercial microwave links: stochastic calibration

Wolff, Wagner; Overeem, Aart; Leijnse, H.; Uijlenhoet, R.

doi:10.5194/amt-15-485-2022

Cited by 4 publications

(1 citation statement)

References 48 publications

(81 reference statements)

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“…As shown in Gazit and Messer (2018b), the availability of CMLs in most areas on Earth is sufficient for reasonable accuracy of near-ground rain mapping. Indeed, despite technical challenges, appropriate tools were proposed, and operational software has been developed [e.g., Wolff et al (2022)]. Since most proposed mapping techniques are based on spatial interpolation, originally proposed for point rain field sampling by rain gauges, the study of the relative performance of 2D rain field retrieval from CMLs is of great interest.…”

Section: Summary and Open Issuesmentioning

confidence: 99%

Rain Field Retrieval by Ground-Level Sensors of Various Types

Messer¹,

Eshel²,

Habi³

et al. 2022

Front. Signal Process.

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Rain gauges (RGs) have been utilized as sensors for local rain monitoring dating back to ancient Greece. The use of a network of RGs for 2D rain mapping is based on spatial interpolation that, while presenting good results in limited experimental areas, has limited scalability because of the unrealistic need to install and maintain a large quantity of sensors. Alternatively, commercial microwave links (CMLs), widely spread around the globe, have proven effective as near-ground opportunistic rain sensors. In this study, we study 2D rain field mapping using CMLs and/or RGs from a practical and a theoretical point of view, aiming to understand their inherent performance differences. We study sensor networks of either CMLs or RGs, and also a mixed network of CMLs and RGs. We show that with proper preprocessing, the rain field retrieval performance of the CML network is better than that of RGs. However, depending on the characteristics of the rain field, this performance gain can be negligible, especially when the rain field is smooth (relative to the topology of the sensor network). In other words, for a given network, the advantage of rain retrieval using a network of CMLs is more significant when the rain field is spotty.

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Section: Summary and Open Issuesmentioning

confidence: 99%

Rain Field Retrieval by Ground-Level Sensors of Various Types

Messer¹,

Eshel²,

Habi³

et al. 2022

Front. Signal Process.

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Harnessing the Radio Frequency Power Level of Cellular Terminals for Weather Parameter Sensing

Sakkas,

Christofilakis,

Lolis

et al. 2024

Electronics

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In light of recent extreme weather events, it is imperative to explore innovative methodologies for promptly and accurately measuring various meteorological parameters. The high spatial and temporal variability in precipitation often surpasses the resolution capabilities of traditional rain gauge measurements and satellite estimation algorithms. Therefore, exploring alternative methods to capture this variability is crucial. Research on the correlation between signal attenuation and precipitation could offer valuable insights into these alternative approaches. This study investigates (a) the feasibility of the classification of precipitation rate using signal power measurements in cellular terminals and (b) the impact of atmospheric humidity as well as other meteorological parameters on the signal. Specifically, signal power data were collected remotely through a specialized Android application designed for this research. During the time of analysis, the power data were processed alongside meteorological parameters obtained from the meteorological station of the Physics Department at the University of Ioannina gathered over one semester. Having in mind the radio refractivity of the air as a fascinating concept affecting the way radio waves travel through the atmosphere, the processed results revealed a correlation with signal attenuation, while a correlation between the latter and absolute humidity was also observed. Moreover, a precipitation rate classification was attained with an overall accuracy exceeding 88%.

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Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives

Zhang,

Liu,

2023

Remote Sensing

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As rainfall exhibits high spatiotemporal variability, accurate and real-time rainfall monitoring is vitally important in fields such as hydrometeorological research, agriculture and disaster prevention and control. Nevertheless, the current dedicated rain sensors cannot fulfill the requirement for comprehensive precipitation observation, owing to their respective limitations. Within the last two decades, the utilization of commercial microwave links (CMLs) for rainfall estimation, as an opportunistic sensing method, has generated considerable attention. Relying on CML networks deployed and maintained by mobile network operators can provide near-surface precipitation information over large areas at a low cost. Although scholars have developed several algorithms for obtaining rainfall estimates from CML data, the rainfall estimation technique based on CMLs remains challenging due to the complex effect in the microwave radiation transmission process. In this paper, we provide a comprehensive review of the technical principles, developments and workflows for this technology, alongside its application in environmental monitoring and hydrological modeling. Furthermore, this paper outlines the current challenges and future research directions, which will hopefully draw the attention of researchers and provide valuable guidance.

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Rainfall retrieval algorithm for commercial microwave links: stochastic calibration

Cited by 4 publications

References 48 publications

Rain Field Retrieval by Ground-Level Sensors of Various Types

Rain Field Retrieval by Ground-Level Sensors of Various Types

Harnessing the Radio Frequency Power Level of Cellular Terminals for Weather Parameter Sensing

Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives

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