## 3 Results  
Since first introduced in 2006, research groups from different disciplines have started to study this technology, and dozens of peer-reviewed papers have been published. The references include many selected publications of studies which are CML measurement-based. In general, these papers can be divided into four groups:  
1. Papers in which the capabilities of CML technology for environmental monitoring have been demonstrated (see Table 2). Naturally, foremost potential is attributed to the near-ground rainfall  
Figure 3: A possible workflow for rain estimation from passive measurements, where appropriate power-law (PL) is adjusted to maximum attenuation (after [PERSON] [15]). Figure 2: A possible workflow for a commercial microwave link (CML) based environmental monitoring, where the input is the transmitted/received signal level measurements (TSL/RSL) (after [PERSON]). monitoring capability. Several papers demonstrated the CML as a rainfall sensor, and many CMLs as a sensors network, capable of 2D rainfall mapping. Later, other papers have demonstrated the use of CMLs for monitoring other-than-rain phenomena, including humidity, fog, dew, snow and sleet, and even wind and air pollution (indirectly). 2. The next step was to study the accuracy of CMLs as virtual rainfall sensors. Since cellular networks have been designed to operate optimally for efficient telecommunication service and not for measuring rain (or other atmospheric variables), its opportunistic use for rain monitoring is challenging, since the network must be taken as is. Table 3 presents a summary of the major contributions to an errors and uncertainties analysis. The analysis aims at quantifying the different sources' errors and their effect on the resulting rain estimates. Generally speaking, the uncertainties can be put into two groups: one which is related to physical, atmospheric effects, e.g., wet antenna, which cause attenuation that may read as higher rain-intensity value in Equation (1) if not properly handled. The second group consists of errors caused by the opportunistic use of existing technology not aimed at atmospheric monitoring. This may include signal quantization and non-linear pre-processing (applied on the signal for efficient network management), as well as errors resulting from the non-optimal, given spatial spread of links and frequencies in the CML network, when being used for atmospheric monitoring. 3. In Table 4, a list of papers suggesting algorithms for rainfall monitoring is presented. As the main opportunity in CML technology is in near ground, bottom-up rain mapping, most algorithms are focused on this. The straightforward approach is to treat each CML as a local point measurement and to interpolate local measurements to a grid, using standard spatial interpolation techniques (e.g., inverse distance weighting IDW, Kriging, etc.). On the basis of this approach, open software tools were developed [43; 44]. More advanced algorithms have been developed by signal processing experts, on which the tempo-spatial resolution of the rainfall maps, their accuracy and their coverage have been improved by exploiting the spatial spread of the CML measurements. Different authors used different approaches, such as: an iterative approach in which variability of rain along the links is exploited [19]; a compressed sensing approach [45; 46]; a model based, parametric approach; a tomographic approach [47]; and dynamic mapping [48; 49]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Atomospheric Phenomenon** & **Reference** \\ \hline Rainfall sensing & [8; 16; 17; 18] \\ Rainfall mapping & [8; 19] \\ Humidity sensing & [20] \\ Fog sensing & [21; 22; 23] \\ Precipitation classification & [24] \\ Dew detection & [25] \\ Wind estimation & [26] \\ Air pollution detection & [27] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Demonstrations of capabilities.