On the potential of commercial microwave link networks for high spatial resolution rainfall monitoring in urban areas

Zheng, Xin; Messer, H.; Wang, Qian; Xu, Tao; Qin, Youwei; Yang, Tao

doi:10.1016/j.atmosres.2022.106289

Cited by 7 publications

(10 citation statements)

References 42 publications

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“…The number of links was generated (approximately 130) for a link density of 0.53 km −2 in an area of 16 km 2 × 16 km 2 . In Zheng et al (2022), the study of Eshel et al (2020) was extended to the case where the spatial interpolation employs both CMLs and RGs. Here, the study is based on an actual CML network (see Figure 2A) where a CML is sometimes replaced by an RG located in its center.…”

Section: D Near-ground Rain Retrieval-practical Considerationsmentioning

confidence: 99%

“…The first refers to a location along a CML path (usually its center), which was chosen to represent a path-integrated measurement, whereas the latter is a point sampler of the rain field at its very location. In Zheng et al (2022), nonisotropic GRCs (1) characterized by 6 parameters are assumed. The parameters defining the GRC, including the size and the orientation of the rain cell, have been chosen randomly and then "sampled" by the network according to (2), along with the addition of noise and quantization to simulate actual measurements.…”

Section: D Near-ground Rain Retrieval-practical Considerationsmentioning

confidence: 99%

“…The resulting maps were later compared with the ground truth, to assess their performance. In Zheng et al (2022), multiple points along the link represented the CML measurements and GMZ was utilized prior to the interpolation, where 11 VRGs were equally spaced along each CML path. In scenarios in which CMLs were replaced by RGs, the location of RGs was set randomly along the paths of discarded CMLs.…”

Section: D Near-ground Rain Retrieval-practical Considerationsmentioning

confidence: 99%

“…These two questions were partially studied in our research group under different models, assumptions, and conditions. In particular, Question #1 was studied by simulations using synthetic scenarios with CMLs and RGs, as well as with semireal data and an actual CML network (Eshel et al, 2020;Eshel et al, 2021;Zheng et al, 2022). Question #2 was studied using theoretical performance bounds, assuming parametric (Gat and Messer, 2019) or nonparametric (Sagiv and Messer, 2022) models for the rain field.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: D Near-ground Rain Retrieval-practical Considerationsmentioning

confidence: 99%

Section: D Near-ground Rain Retrieval-practical Considerationsmentioning

confidence: 99%

Section: D Near-ground Rain Retrieval-practical Considerationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rain Field Retrieval by Ground-Level Sensors of Various Types

Messer¹,

Eshel²,

Habi³

et al. 2022

Front. Signal Process.

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“…Follow this principle, each CMLs in a communication network can be considered as a rainfall sensor. Such rainfall information can be used not only for the 2‐D rain field reconstruction (Goldshtein et al., 2009, Messer et al., 2022; Zheng et al., 2022), but also for radar adjustment (Cummings et al., 2009), rainfall nowcasting (Imhoff et al., 2020), and hydrological models (Cazzaniga et al., 2022; Pastorek et al., 2023; Smiatek et al., 2017; Stransky et al., 2018), etc.…”

Section: Introductionmentioning

confidence: 99%

A Statistical Approach to Retrieve Rainfall Intensity by Attenuation Measurements From Commercial Microwave Links

Zheng,

Messer,

Qin

et al. 2023

Earth's Future

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Commercial microwave link (CML) has become one of the most widely used opportunistic sensors for rainfall monitoring. The high density and coverage of CMLs enable us to monitor near‐ground rainfall in high spatial resolution. The empirical power‐law (PL) model proposed in 1978 is the leading approach in relating CML attenuation with rain rate. However, while giving good rain rate estimates for CMLs longer than 1 km, in short CMLs, large errors are observed when the PL approach is applied. In this paper, we studied a statistical approach to convert CML attenuation measurement to rainfall intensity. The proposed approach calculates the exceedance probability, p, of rain‐induced attenuation and derives the corresponding rainfall intensity based on the rain rate cumulative distribution curve. Data from two cities in two countries were used to validate the approach. The results of the proposed approach were compared with that of the PL model. Results show that the models derived using our approach outperform PL for short CMLs, while showing similar performance for long ones. Since in the next generation mobile networks short CMLs will become more and more dominant, our work provides a way to derive retrieval models for the future generation CML networks.

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An Online Training Procedure for Rain Detection Models Applied to Satellite Microwave Links

Gianoglio,

Colli,

Zani

et al. 2023

IEEE Geosci. Remote Sensing Lett.

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In recent years, opportunistic rainfall sensing exploiting Commercial Microwave Links (CML) has become popular for real-time monitoring of rainfall events. Among such systems, Satellite-to-earth Microwave Links (SML) are gaining great interest. An important information to extract from the collected data is whether or not an SML observation corresponds to a rain condition. This letter proposes an online training procedure to keep the algorithms that classify this condition up to date, overcoming the problem of data seasonality. Specifically, we propose to retrain the algorithms after one day of observations by applying a forgetting mechanism, where the oldest data are removed from the training dataset. Data were collected by the Smart Rainfall System (SRS) SML-type sensors located in the city of Genoa (Italy). We compare three classification algorithms: one based on an anomaly detection method and two based on Machine Learning (ML) algorithms. The outcomes of this analysis pave the way for implementing artificial intelligence models on embedded systems with limited resources such as the electronic units of SML sensors, hence reducing the data flow from the peripheral sensor network to the central data acquisition and integration server.

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On the potential of commercial microwave link networks for high spatial resolution rainfall monitoring in urban areas

Cited by 7 publications

References 42 publications

Rain Field Retrieval by Ground-Level Sensors of Various Types

Rain Field Retrieval by Ground-Level Sensors of Various Types

A Statistical Approach to Retrieve Rainfall Intensity by Attenuation Measurements From Commercial Microwave Links

An Online Training Procedure for Rain Detection Models Applied to Satellite Microwave Links

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