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Sensors
Volume 22
Issue 9
10.3390/s22093218
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Article

Comparison of CML Rainfall Data against Rain Gauges and Disdrometers in a Mountainous Environment

by
Roberto Nebuloni
1,*,
Greta Cazzaniga
2,
Michele D’Amico
3,
Cristina Deidda
2 and
Carlo De Michele
2
1
IEIIT, Consiglio Nazionale delle Ricerche, 20133 Milano, Italy
2
DICA, Politecnico di Milano, 20133 Milano, Italy
3
DEIB, Politecnico di Milano, 20133 Milano, Italy
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(9), 3218; https://doi.org/10.3390/s22093218
Submission received: 15 March 2022 / Revised: 12 April 2022 / Accepted: 15 April 2022 / Published: 22 April 2022
(This article belongs to the Special Issue Rain Sensors)
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Abstract

Despite the several sources of inaccuracy, commercial microwave links (CML) have been recently exploited to estimate the average rainfall intensity along the radio path from signal attenuation. Validating these measurements against “ground truth” from conventional rainfall sensors, as rain gauges, is a challenging issue due to the different spatial sampling involved. Here, we assess the performance of a network of CML as opportunistic rainfall sensors in a challenging mountainous environment located in Northern Italy. The benchmark dataset was provided by an operational network of rain gauges and by three disdrometers. Moreover, disdrometer data were used to establish an accurate relationship between path attenuation and rainfall intensity. A new method was developed for assessing CML: time series of rainfall occurrence and rainfall depth, representative of CML radio path, were derived from the nearby rain gauges and disdrometers and compared with the same quantities gathered from the CML. It turns out that, over the very short integration times considered (10 min), CML perform well in detecting rainfall, whereas quantitative rainfall estimates may have large discrepancies.
Keywords: commercial microwave links; disdrometers; rain gauges; rainfall sensors; rainfall; microwave propagation; rain attenuation commercial microwave links; disdrometers; rain gauges; rainfall sensors; rainfall; microwave propagation; rain attenuation

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MDPI and ACS Style

Nebuloni, R.; Cazzaniga, G.; D’Amico, M.; Deidda, C.; De Michele, C. Comparison of CML Rainfall Data against Rain Gauges and Disdrometers in a Mountainous Environment. Sensors 2022, 22, 3218. https://doi.org/10.3390/s22093218

AMA Style

Nebuloni R, Cazzaniga G, D’Amico M, Deidda C, De Michele C. Comparison of CML Rainfall Data against Rain Gauges and Disdrometers in a Mountainous Environment. Sensors. 2022; 22(9):3218. https://doi.org/10.3390/s22093218

Chicago/Turabian Style

Nebuloni, Roberto, Greta Cazzaniga, Michele D’Amico, Cristina Deidda, and Carlo De Michele. 2022. "Comparison of CML Rainfall Data against Rain Gauges and Disdrometers in a Mountainous Environment" Sensors 22, no. 9: 3218. https://doi.org/10.3390/s22093218

APA Style

Nebuloni, R., Cazzaniga, G., D’Amico, M., Deidda, C., & De Michele, C. (2022). Comparison of CML Rainfall Data against Rain Gauges and Disdrometers in a Mountainous Environment. Sensors, 22(9), 3218. https://doi.org/10.3390/s22093218

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