## 1 Introduction
### The Principle and Development of Rainfall Estimation by CMLs
Based on a multi-band dual-polarized microwave link of the Tropical Rainfall Measuring Mission (TRMM), [PERSON] et al. (2018) demonstrated the validity of the power-law relationship for rainfall estimation; subsequently, scientists further found that estimating rain rate using dual-frequency differential attenuation has a higher stability than single-frequency does ([PERSON] et al., 2018; [PERSON] and [PERSON], 2019; [PERSON] et al., 2020). The landmark breakthrough in this technology occurred around 2006. As we enter the 21 st century, the growing demand for wireless network terminals, such as cellphones,  
Figure 1: The basic principle of estimating rainfall using CMLs. CMLs typically connect two cell towers and carry information via microwave signals to enable data exchange between the two locations. When rainfall occurs on the CML path, the received signal is significantly attenuated due to scattering and absorption of the wave by raindrop particles. The path-averaged rain rate can be retrieved based on the power-law relationship between rain rate and rain-induced attenuation. has led to a dramatic increase in the number of CMLs with operating frequencies ranging from 20 to 40 GHz. The mobile network operators who installed and maintained these CMLs have established an adequate infrastructure for monitoring rainfall using CMLs. As a result, the large quantity of CMLs that are otherwise used for communications can be used as opportunistic sensors, and the signal impairments that are undesirable to telecommunication engineers instead become an additional source of rainfall information. In Israel, [PERSON] et al. [10] used CMLs to estimate rainfall for the first time and compared them with rain gauges and radar, demonstrating the potential of CMLs. Almost simultaneously, a rainfall inversion experiment in the Netherlands [26] also suggested that CMLs could be an effective complement to radar QPE. Later, experimental studies on the estimation of rainfall using CMLs have successively been carried out in France [27], Germany [28], Switzerland [29; 30], the Czech Republic [31], Italy [32], Brazil [33], China [34; 35], Sri Lanka [36], some countries in Africa [37; 38; 39], etc., as shown in Figure 3.  
Figure 2: Two-dimensional scatter plots obtained from real rain rates and rain rates inverted by the \(\gamma\)–\(R\) relation at frequencies (**a**) 6 GHz, (**b**) 10 GHz, (**c**) 15 GHz, (**d**) 18 GHz, (**e**) 22 GHz, (**f**) 26 GHz, (**g**) 38 GHz, (**h**) 50 GHz, (**i**) 60 GHz, (**j**) 73 GHz, (**k**) 83 GHz and (**l**) 92 GHz. It is worth noting that the real rain rates are obtained from an OTT Parsivel disdrometer deployed in Nanjing, whereas the CML-based rain rates are simulatively calculated using the T-matrix algorithm based on the \(\gamma\)–\(R\) relationship and the DSD data recorded by the disdrometer. The rainfall types are identified using the disdrometer data according to the classification criteria proposed in [25]. (Reprinted with permission from [PERSON] et al., 2023. Copyright 2023 IEEE). This paper provides a detailed overview of the techniques for rainfall estimation based on CMLs, in contrast to previous review articles [40; 41; 42; 43], with the aim of providing a comprehensive introduction to readers new to the field and offering researchers our view of current challenges and future research directions for the technique. The rest of this paper is organized as follows: Section 2 details the processes involved in obtaining rainfall estimates from the attenuation information of the CMLs. Other applications of CMLs as opportunistic sensors are discussed in Section 3. Section 4 summarizes the current challenges. In Section 5 we present possible future directions. Section 6 provides a conclusion and outlook.