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Commercial microwave link networks for rainfall observation: Assessment of the current status and future challenges

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Accurate observation of the high spatio‐temporal variability of rainfall is crucial for hydrometeorological applications. However, the existing observations from rain gauges and weather radars have individual shortcomings that can introduce considerable errors and uncertainties. A fairly new technique to get additional rainfall information is the usage of the country‐wide commercial microwave link (CML) networks for rainfall estimation by exploiting the measurements of rain‐induced attenuation along these CMLs. This technique has seen an increasing number of applications during the last years. Different methods have been developed to process the noisy raw data and to derive rainfall fields. It has been shown that CMLs can provide important line‐integrated rainfall information that complements pointwise rain gauge and spatial radar observations. There exist several limitations, though. Robustly dealing with the erratic fluctuations of the CML raw data is a challenge, in particular with the growing number of CMLs. How to correctly compensate for the biases from the effect of wet antenna attenuation for different CMLs also remains a crucial research question. Progress is additionally hampered by the lack of method intercomparisons, which in turn is hampered by restricted data sharing. Hence, collaboration is key for further advancements, also with regard to extended interaction with the CML network operators, which is a prerequisite to achieve increased data availability. In regions where rain gauges and weather radars are available, CMLs are a welcome complement. But in developing countries, which are characterized by weak technical infrastructure and which often suffer from water stress, additional rainfall information is a necessity. CMLs could play a crucial role in this respect. This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Extremes Science of Water > Methods
Plots of the relation between rain rate R and specific attenuation k for different frequencies in the microwave range and, for comparison, the relation between radar reflectivity Z and rain rate R. Each point represents a drop size distribution (DSD) of aggregated drop counts over 1 min, recorded by a This Laser Disdrometer from April till end of September of the years 2011, 2012, and 2013 in southern Germany. The DSD data has been filtered according to Friedrich, Kalina, Masters, and Lopez (). Calculations of k have been performed with the T‐matrix method (Mishchenko, Travis, & Mackowski, ) for oblate spheroid shaped drops at 10°C using the Python package pytmatrix (Leinonen, ). The colored dashed lines represent a fit of the kR relation. The dotted black lines follows the ITU recommendation (ITU, ). Striking is the very low DSD dependence of the kR relation compared to the ZR relation, in particular for the shown frequencies below 40 GHz. However, it has to be noted, that for low frequencies, for example, for 10 GHz, DSD dependence increases. Due to the equal scaling of all scatter plots, this is not easily visible from the figures
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CML network of the three largest cell phone providers in France. Data of CML transmitter and receiver location and orientation is provided by the French National Frequencies Agency (ANFR). From this data, CML paths were derived and made available
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Illustration of the basic operating principle of CML rainfall estimation. CMLs (with the drum‐shaped antennas) are typically used to interconnect cell phone towers. Due to scattering and absorption the transmitted microwave (MW) radiation is attenuated by raindrops. This leads to an attenuated signal level at the receiver, from which the rain rate along the path can be estimated
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Shown are the numbers of cell phone subscribers in Africa for the last years (solid bars) and the predicted evolution till 2020 (hatched bars). Based on data from GSMA ()
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Predicted worldwide evolution of the usage of microwave links, fiber optics and copper wires in the cell phone backhaul. Based on data from Ericsson ()
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(left) Distribution of CML lengths and frequencies with their sensitivity to path‐average rain rates. Clearly visible are the separated frequency bands which are used by CMLs. The sensitivity due to path‐averaged rainfall (which is not taking into account possible variations along the CML path) increases with length and with higher frequencies. Hence, for most CMLs they are both chosen, so that the CML exhibits between 0.25 and 1.0 dB attenuation for a path‐averaged rainfall rate of 1 mm/hr. (right) This figure shows the rain rate that causes 1 dB attenuation versus the rain rates that causes 60 dB attenuation at a specific CML. The values of the x‐ and y‐axis can be interpreted as estimates of the minimum‐ and maximum detectable path averaged rain rate. It has to be noted, though, that the detection limit varies for each individual CML due to different quantization and different grades of noisiness of the TRSL records (e.g., as shown in Figures 5 and 6). The assumed dynamic range of 60 dB is a typical average value and might be different depending on the maximum available transmitter power
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(top) Example of a CML which exhibits “bad” characteristics for rainfall estimation due to strong erratic fluctuations in its TRSL time series. These fluctuations most likely stem from multi‐path propagation caused by reflections of the lake over which the CML path leads. (bottom) For comparison, data from a nearby CML with a fairly stable signal level during dry periods, which clearly reveals the rain events marked by the shaded period. The data shown is the raw data which is sampled instantaneously every minute. The “bad” CML has a length of 10.5 km and uses a frequency of 19.2 GHz with vertical polarization. The “good” CML has a length of 10.3 km and uses a frequency of 18.2 GHz with vertical polarization
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Example of a CML time series of the transmitted minus received signal level (TRSL) showing fluctuations on different temporal scales. (top) The weekly median clearly reveals a yearly pattern in the data. Note that the weekly median is used to filter influences from all the strong attenuation events, stemming from rainfall, which are visible in the raw data. (bottom) Example for a diurnal pattern of TRSL data, probably stemming from specific propagation characteristics during clear nights when a stable atmospheric boundary layer forms. Note that there was no rain present during the 3 days in July which are shown here. The data shown is the raw data which is sampled instantaneously every minute from a CML with a length of 19.6 km using a frequency of 16.4 GHz and vertical polarization
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Illustration of a typical workflow, starting from acquiring several CML raw data time series to having CML‐based rainfall fields. For better visibility of the details of the time series, the plots only show a very short time period of several hours. The black lines in the plot of the CML rainfall field represent the CML paths. Shown is the hourly rainfall sum compared to the hourly rain gauge adjusted weather radar product (RADOLAN‐RW) of the German meteorological service (Chwala, Smiatek, & Kunstmann, )
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