A year of attenuation data from a commercial dual-polarized duplex microwave link with concurrent disdrometer, rain gauge, and weather observations

Commercial microwave links (CMLs) in telecommunication networks can provide relevant information for remote sensing of precipitation and other environmental variables, such as path-averaged drop size distribution, evaporation, or humidity. The CoMMon field experiment (COmmercial Microwave links for urban rainfall MONitoring) mainly focused on the rainfall observations by monitoring a 38 GHz dual-polarized CML of 1.85 km path length at a high temporal resolution (4 s), as well as a co-located array of five disdrometers and three rain gauges over 1 year. The dataset is complemented with observations from five nearby weather stations. Raw and pre-processed data, which can be explored with a custom static HTML viewer, are available at https://doi.org/10.5281/zenodo.4923125 (Špačková et al., 2021). The data quality is generally satisfactory for further analysis, and potentially problematic measurements are flagged to help the analyst identify relevant periods for specific study purposes. Finally, we encourage potential applications and discuss open issues regarding future remote sensing with CMLs.

Precipitation Estimates from Commercial Microwave Links: Practical Approaches to Wet-antenna Correction

An inadequate correction for wet antenna attenuation (WAA) often causes a notable bias in quantitative precipitation estimates (QPEs) from commercial microwave links (CMLs) limiting the usability of these rainfall data in hydrological applications. This paper analyzes how WAA can be corrected without dedicated rainfall monitoring for a set of 16 CMLs. Using data collected over 53 rainfall events, the performance of six empirical WAA models was studied, both when calibrated to rainfall observations from a permanent municipal rain gauge network and when using model parameters from the literature. The transferability of WAA model parameters among CMLs of various characteristics has also been addressed. The results show that high-quality QPEs with a bias below 5% and RMSE of 1 mm/h in the median could be retrieved, even from sub-kilometer CMLs where WAA is relatively large compared to raindrop attenuation. Models in which WAA is proportional to rainfall intensity provide better WAA estimates than constant and time-dependent models. It is also shown that the parameters of models deriving WAA explicitly from rainfall intensity are independent of CML frequency and path length and, thus, transferable to other locations with CMLs of similar antenna properties.

Rainfall retrieval algorithm for commercial microwave links: stochastic calibration

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 antenna 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 optimization method Particle Swarm Optimisation, which is used for the numerical maximization of the objective function. An open dataset of approximately 2,800 sub-links of minimum and maximum received signal levels over 15-minute intervals covering the Netherlands (~35,500 km2) is employed, where 12 days are used for calibration and 3 months for validation. A gauge-adjusted radar rainfall dataset is utilized as 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.

On the Power of Microwave Communication Data to Monitor Rain for Agricultural Needs in Africa

Over the last two decades, prevalent technologies and Internet of Things (IoT) systems have been found to have potential for carrying out environmental monitoring. The data generated from these infrastructures are readily available and have the potential to provide massive spatial coverage. The costs involved in using these data are minimal since the records are already generated for the original uses of these systems. Commercial microwave links, which provide the underlying framework for data transfer between cellular network base stations, are one example of such a system and have been found useful for monitoring rainfall. Wireless infrastructure of this kind is deployed widely by communication providers across Africa and can thus be used as a rainfall monitoring device to complement the sparse proprietary resources that currently exist or to substitute for them where alternatives do not exist. Here we focus this approach’s potential to acquire valuable information required for agricultural needs across Africa using Kenya as an example.

Commercial microwave links as rainfall input data in urban hydrological modelling

<p>Many studies in literature have showed that hydrological models are highly sensitive to spatial variability of the rainfall field. Limited and inaccurate rainfall observations can negatively affect flood forecasting and the decision-making processes based on warning system. This problem becomes much more evident in urban catchments which usually covers huge areas and where the runoff process is faster, due to the highly impervious surfaces. Given this, it is a priority to develop always new operational instruments which can improve rainfall data availability and accurately quantify rainfall variability in space. To face this challenge, in the recent years, it has been investigated the use of commercial microwave links (CML) as opportunistic rainfall sensors which could be integrated with traditional rainfall observations in areas lacking sensors. The technique relies on the well-established relationship between CML's signal attenuation and rainfall intensity across the signal propagation path. Here, we assess the operational potential of a CML network, located in the northern area of Lambro river (Lombardia region, Italy). This urbanized region is of great hydrological interest, since it is often subjected to flash floods, hence it requires a robust and accurate warning system. We considered a set of about 80 CMLs distributed quite uniformly over the entire study area and we assessed if and how rainfall data collected by them can improve river discharge predictions. To this aim, we implemented a semi-distributed rainfall-runoff model, which reproduces the river flow at the outlet section in Lesmo (Monza e Brianza), and we fed the hydrological model with CML rainfall data. We tested the use of CML rainfall data as input to the hydrological model. In particular, we used path-averaged rainfall intensities, calculated from CML path attenuation, as point measurements with a weight inversely proportional to CML length. To check the suitability of CML data as input to our urban rainfall-runoff model, we compared the observed river discharge with the predicted one, obtained using different rainfall data layouts. Indeed, we tested CML data but also rain gauges measurements and a combination of CML and rain gauge observations.</p>

One year of attenuation data from a commercial dual-polarized duplex microwave link with concurrent disdrometer, rain gauge and weather observations

Commercial microwave links (CML) in telecommunication networks can provide relevant information for remote sensing of precipitation and other environmental variables, such as path-averaged drop size distribution, evaporation or humidity. To address this issue, the CoMMon field experiment (COmmercial Microwave links for urban rainfall MONitoring) monitored a 38-GHz dual-polarized CML of 1.85 km at a high temporal resolution (4 s), as well as a collocated array of five disdrometers and three rain gauges over one year. The dataset is complemented with observations from five nearby weather stations. Raw and pre-processed data, which can be explored effortlessly with a custom static HTML viewer, are available at https://doi.org/10.5281/zenodo.4524632 (Špačková et al., 2020). The data quality is generally satisfactory and potentially problematic measurements are flagged to help the analyst identify relevant periods for specific study purposes. Finally, we encourage potential applications and discuss open issues regarding future remote sensing with CMLs.

Quantitative Analysis of the Performance of Spatial Interpolation Methods for Rainfall Estimation Using Commercial Microwave Links

Using signal level measurements from commercial microwave links (CMLs) has proven to be a valuable tool for near-ground 2-D rain mapping. Such mapping is commonly based on spatial interpolation methods, where each CML is considered as a point measurement instrument located at its center. The validity of the resulted maps is tested against radar observations. However, since radar has limitations, accuracy of CML-based reconstructed rain maps remains unclear. Here we provide a quantitative comparison of the performance of CML-based spatial interpolation methods for rain mapping by conducting a systematic analysis: first by quantifying the performance of maps generated from semi-synthetic CML data, and thereafter turning to real-data analysis of the same rain events. A radar product of the GermanWeather Service, serves as ground truth for generating semi-synthetic data, in which several temporal aggregations of the radar rainfall fields are used to create different decorrelation distances. The study was done over an area of 225X245 km2 in southern Germany, with 808 CMLs. We compare the performance of two spatial interpolation methods - Inverse Distance Weighting and Ordinary Kriging - in two cases: where each CML is represented as a single point, and where three points are used. The points’ measurements values in the latter are determined using an iterative algorithm. The analysis of both cases is based on a 48 hour rain event. The results re-confirm the validity of CML-based rain retrieval, showing a slight systematic performance improvement when an iterative algorithm is applied so each CML is represented by more than a single point, independent of the interpolation method.