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 5 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 (Špaˇcková 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.

radome wetting. The importance of accurate estimation of A w increases in the case of short CMLs when its contribution to the observed attenuation is substantial (Pastorek et al., 2018).
The power-law relationship approximates the relation between attenuation caused by raindrops and rainfall intensity (Atlas and Ulbrich, 1977): (2) 55 where R is the rain rate in mm h -1 and parameters a and b are related to the microwave link characteristics (frequency, polarization) and drop size distribution (DSD) (Olsen et al., 1978). Value b is close to one for frequencies between 20 GHz and 40 GHz. While electromagnetic scattering for hydrometeors is generally complex (Eriksson, 2018), the specific attenuation of signal k in dB km -1 for liquid precipitation can be estimated from the drop size distribution: is the number of drops per unit volume in drop diameter interval.

Field campaign
The campaign took place in Dübendorf, Switzerland. Figure 1 presents the layout of the CoMMon field campaign with all sites 70 (white pins) where the disdrometers and rain gauges were deployed. The two antennas were located at sites 1 (Dübendorf) and 5 (Wangen) and the microwave link was 1.85 km long (red line). The area between the antennas consists mainly of an airport, sport fields, agricultural fields, a shopping mall and a highway. Five optical disdrometers were placed at sites 2, 3, 4 and 5. The disdrometers at site 2 were collocated to enable quality control and the quantification of observation uncertainties.
Antenna radomes and outdoor units were weather-protected by large custom-made PVC shields for approximately half of the  (Table 2).
Moreover, observations from two other weather stations located at the airport (Table 3)   Raindrop information was collected by the 1 st generation of the PARSIVEL optical disdrometer manufactured by OTT Hydromet and retrofitted by EPFL-LTE to allow for remote access and data transfer (Jaffrain et al., 2011). The horizontal laser beam had an area of 54 cm 2 . The measurement principle is based on the attenuation in received voltage and on the time required for the passage of a particle through the laser beam. From this, the terminal fall velocity and the equi-volumetric drop diameter 100 can be estimated. The maximum area covered by the drop is related to the maximum attenuation. The PARSIVEL rain rate (parameter 05 in Appendix C) retrieval is linked to the drop diameter. Drops larger than 1 mm are assumed to have an oblate shape with its axis ratio linearly decreasing to 0.7 for drops with a diameter of 5 mm (Battaglia et al., 2010;Löffler-Mang and Joss, 2000). Data were categorized into 32 non-equidistant velocity classes and 32 non-equidistant diameter classes (see Appendix A and B). The first two diameter classes were always empty since they were outside the measurement range of the 105 device. The sampling resolution was 30 s.
Providing additional rainfall data, the collocated tipping bucket rain gauges (3029-1, Précis Mécanique) were the same type of model and had been dynamically calibrated (Humphrey et al., 1997). Deployed 50 cm from the ground, it had a sampling area of 400 cm 2 . Its bucket content of 4 g corresponded to the resolution of 0.1 mm of rain. The logger had a time resolution 0.1 s and its time drift was less than 2 min per month. The data were saved in the internal memory and downloaded on-site.  An additional three weather stations operated by MeteoSwiss provided comprehensive weather data at sites located 6 to 10 km from the experimental site ( Fig. 1). Data were extracted using CLIMAP software provided by MeteoSwiss. In addition, there were two stations of the Automatic Weather Observation Systems (MIDAS IV, Vaisala) at the Dübendorf airport. The MIDAS IV system employed two sensors at both ends of the runway as these weather stations provide data primarily used for airport operations. The temporal resolution varied between 3 and 60 s depending on the weather parameter.      weather characteristics with columns organised as described in Appendix G.

Data quality and reliability
Major field visits were conducted on 6 July 2011, 19 October, 15 March 2012 and 21 March 2012 to maintain the instruments.
The rain gauges were dynamically calibrated in the laboratory.  around 300 mm of cumulative rainfall. Unfortunately, site 3 did not provide data from a collocated rain gauge for comparison.

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The filter presented in Jaffrain and Berne (2011) can be used to remove dubious measurements while preserving rain drops.
One of the tipping bucket rain gauges faced technical issues that constrained the data collection. The greatest data availability gap, due to low batteries, happened at site 4 (RG02) between 6 July 2011 and 16 March 2012. Figure 5 presents a comparison of cumulative rain collected by the rain gauges and disdrometers and shows a good temporal match of measured data. The outages of disdrometers between December and February, described above, caused the underes-150 timation of rain amounts during this period. RG02 corresponds to RG03 for the entire time period when it was in operation.
RG04 was blocked from the middle of June 2011 until the 6 July 2011. There was also unrealistic rainfall recorded by RG04 on 24 August 2011. In total, 139.7 mm of rainfall were measured within 2.5 h, probably an artefact due to vandalism. The collocated disdrometer (P20) also showed a dissimilar temporal evolution of rain rate to the other disdrometers. Both quality issues appeared at site 5 and were probably caused by the moving of the instrumentation during lawn mowing.

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The data from weather stations of MeteoSwiss and the airport are rather continuous and consistent.

Tool/HTML viewer
To facilitate the efficient exploration of the data plots, the html file makes it possible to readily plot pre-defined views of one selected day. The folders are related to the main data sources: the CML, the disdrometers, the rain gauges, the MeteoSwiss and airport weather stations. The intensity of red colour in the campaign calendar describes the daily cumulative rainfall depth which enables the user to choose the most interesting days and explore them further. Once the day is selected, the plots in each 165 folder are displayed.  There are eight pre-defined views in the drop-down menu in the viewer. The first view plots CML data accompanied by data from RG03 from site 2 which is located in the middle of the link path. Views two and three present rainfall intensities from the disdrometers, missing values and drop size distributions. The fourth view displays rain gauge data and its missing values.
Views five and six display the data and missing values from the airport weather stations and the last two views concern the data 170 and missing values from the MeteoSwiss weather stations. Note that November 2012 was extremely dry, therefore no rainfall was recorded.  The rate at which WAA decreased after an event showed substantial variation, ranging from a few minutes to several hours 190 depending on temperature, wind and humidity. In a follow-up study, Fencl et al. (2014) assessed the effectiveness of direct antenna shielding for mitigating WAA compared with post-processing techniques. They found that antenna shielding helps substantially reduce biases in rainfall estimates. However, shielding did not outperform model-based corrections as shielded antennas still experienced attenuation, even when the face of the radome was completely dry. Whether this is caused by the attenuation of side-lobes or are side effects of the environment built is presently unknown to the authors. weather conditions and whenever dew was present on the antennas. However, modelling these effects remains challenging.

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The CoMMon dataset could help gain new insight into these issues, for example, by further investigating WAA due to dew formation on antenna radomes (Fig. 8).

DSD retrieval and DSD related errors
Attenuation data of microwave links operating at different frequencies or polarizations could be, in theory, used for estimating path-averaged raindrop size distributions (e.g., Rincon and Lang, 2002   Another study by van Leth et al. (2020) based on a similar approach showed that reasonable performance on selected events and idealized conditions can be achieved. However, retrieved DSD parameters are not always reliable and large uncertainties remain due to quantization noise, baseline estimation and wet-antenna attenuation. These could possibly be reduced by using the data to update prior knowledge on empirical drop size distributions using Bayesian data analysis.

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Also, the high-quality CoMMon dataset could provide the evidence base to test different retrieval techniques and to assess their strengths and limitations. Last, but not least, detailed data on rainfall microphysics and microwave attenuation from operational devices can be extremely useful to radio engineers (Kvicera et al., 2009).

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There are still many unsolved issues regarding how to effectively retrieve precipitation from the microwave attenuation in praxis. Given the high temporal resolution, the CoMMon data might be most useful in improving our understanding of antenna wetting, baseline dynamics and the impact of variable DSDs.
First, wet antenna attenuation and dew formation on antennas are phenomena that need to be described more precisely to avoid the overestimation of rainfall. Several studies have suggested that correcting for wet antenna attenuation can significantly 220 enhance results (Leijnse et al., 2008;Pastorek et al., submitted). Most probably, corrections cannot be based on frequency and signal dynamics only, since the atmospheric state around the CML varies. In other words, temperature, relative humidity, radiation and wind probably have a more significant impact on the drying time of the antennas as well as the conditions prior to wetting. To what extent machine learning (Habi and Messer, 2018), trained on many CoMMon-type datasets, can provide an empirical solution remains to be seen.

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Second, the baseline of transmitted minus received power is often approximated as constant, even though substantial variations during dry weather have been reported (Wang et al., 2012) and there is little evidence that the baseline remains stationary during wet periods. Different dry-wet weather classification approaches were presented in Berne (2010), Overeem et al. (2011) or Polz et al. (2020). A benchmarking activity with many datasets from different sites and climatic regions is still lacking.

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Scattering theory suggests that a larger variation of drop size distribution challenges more precise retrieval for longer links (Leijnse et al., 2010). The disdrometer observations in the CoMMon dataset can also be used to build simulators making it possible to better understand the attenuation-rainfall relation and assess CML rainfall retrieval uncertainties related to variable DSDs (Berne and Uijlenhoet, 2007;Schleiss et al., 2012).
Variable DSDs also represent major uncertainties at CMLs with higher frequency bands. Although, to date, most CMLs use 235 frequencies from 5 GHz to 40 GHz, the spectrum is currently further extended to 80 GHz. Recently, Fencl et al. (2020) used PARSIVEL observations from the CoMMon dataset to simulate rainfall retrieval from an E-band CML which demonstrated that these may be promising tools for sensing light rainfall which is challenging for lower frequencies due to the quantization of the attenuation data (Berne and Schleiss, 2009). In a similar fashion, CMLs are "blind" regarding extremely high intensities as attenuation due to such high intensities drops below the receiver threshold of the hardware and causes outages of the CML 240 (cf. event on 5 August 2011). How this can be solved by "inputting" missing observations based on signals from nearby sensors (Mital et al., 2020), remains to be seen.
Melting snow causes large attenuation of EM waves at frequencies commonly used by CMLs (ITU-R, 2015). Upton et al. precipitation. Attenuation of EM waves by ice particles, however, remains challenging to simulate due to complex shapes of these hydrometeors. Moreover, ice particles containing liquid water interact with EM waves in substantially different manner (Eriksson, 2018).

Data accessibility
250 Data from CML, disdrometers and rain gauges, and nearby weather stations are available with data files stored in the Zenodo repository at https://doi.org/10.5281/zenodo.4524632 (Špačková et al., 2020). The citation should be used as follows: The dataset is available for reuse under a CC BY 4.0 license. License terms apply.

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The data from the CoMMon field campaign described in this paper is relevant for the remote sensing of rainfall, as well as for the design of outage-free terrestrial wireless communication systems. The unique dataset provides a comprehensive package of attenuation data from a 38 GHz dual-polarized microwave link with concurrent disdrometer and rain gauge measurements in (sub-)minute resolution. In addition, meteorological data from the weather stations of MeteoSwiss and Dübendorf airport were included. The main conclusions are:

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-The remote sensing of precipitation and related atmospheric phenomena, such as dew, remains a relevant problem. Using signals from commercial telecommunication microwave links to learn about these phenomena seems promising because they cover sparsely or completely ungauged regions and can be queried remotely and fast. The open CoMMon dataset makes a unique contribution by providing dual-polarized transmitted and received power levels, as well as ground-level observations of precipitation microphysics and local weather. It fosters the interconnection of datasets which can be used 270 to better understand scattering phenomena and benchmark retrieval methods.
-The dataset represents a duration of one year and contains data from i) a single 38-GHZ dual polarized CML with a length of 1.85 km; ii) collocated observations of five disdrometers; iii) three rain gauges; and iv) observations from five nearby weather stations. Specific highlights are, first, that the antenna radomes were protected by custom shielding for approximately half of the period of the campaign, thus making it possible to investigate the impact of antenna wetting 275 which is still considered a major disturbance for rainfall retrieval. Second, the data are provided in sub-minute resolutions making it possible to investigate the detailed dynamics of the involved processes. Third, the dataset contains periods with rain but also periods during which ice hydrometeors including snow and melting snow occured (see Appendix H).
-Although the experimental campaign faced expected difficulties regarding sensor malfunctioning, data outages, etc., these episodes are well documented and, thus, do not compromise the satisfactory quality of the dataset. The provided 280 static HTML viewer also makes it easy to explore the data by pre-configured views of daily time series. For example, by focusing on days with intense or little precipitation, typical dynamics of the observed processes can be screened effortlessly.
-The dataset contains unique evidence regarding several processes such as the wetting and drying of antenna radomes and outdoor units or the impact of temperature and wind. We encourage several applications, from investigating base-285 line separation to wetting phenomena, such as dew, which had much slower dynamics in comparison to rain-induced attenuation, to the retrieval of drop-size distributions from the joint analysis of horizontal and vertical polarizations.
-In the future, the CoMMon dataset can be used to further investigate challenging issues in the remote sensing of rainfall, such as the classification of dry/wet periods, space-time variability of DSDs or even the analysis of fade margins for better radio network design.