## 2 Procedures in Deriving Rainfall Maps from Attenuation
### Classification of Dry and Wet Periods  
As mentioned above, phenomena other than or triggered by rainfall may also cause signal impairment. Therefore, when we obtain attenuation data for CMLs, typically the transmitted signal level (TSL) minus the received signal level (RSL), the first critical issue is to determine at each timestamp whether the CMLs are affected by rainfall. More simply, the CML data need to be classified into dry (rain free) and wet (rainy) periods. In order to  
Figure 3: World map with the main countries that have researched rainfall measurement techniques based on CMLs. Notably, Antarctica and the Arctic have been omitted from the figure. In addition, the number of research is based on the references in this paper, which may not fully encompass all published studies but can reflect the approximate distribution. accomplish the classification of dry and wet periods, scholars have developed a variety of methods, which can be broadly divided into three categories. The first category is based on the analysis of attenuation sequences to determine whether rainfall exists on the path of CMLs. The simplest way to distinguish between dry and wet periods is based on a global threshold for attenuation, with periods above the threshold being treated as wet [44]. [PERSON] et al. [23] found that RSL sequences of dual-frequency microwave links are highly correlated during wet periods and suggested setting correlation thresholds to distinguish between dry and wet periods. Further, [PERSON] et al. [45] extended the spatial correlation of rainfall from individual CMLs to all CMLs within a certain range and proposed the so-called nearby link approach (NLA). They considered a time period as wet if the RSLs of at least half of the CMLs in the range decreased simultaneously during that time period. In addition, [PERSON] et al. [27] found significant differences in the local variability of signal attenuation between dry and wet periods and proposed the use of a rolling window to calculate the standard deviation of the attenuation sequence to identify dry and wet periods based on a predetermined threshold. Recently, [PERSON] et al. [46] proposed an improved version of this algorithm, arguing that the threshold can depend on the fluctuating trend of individual CMLs rather than only on climatology. Based on the short-time Fourier transform of the RSL sequence, [PERSON] et al. [28] found that the normalized amplitude difference between low and high frequencies of the power spectrum can be used to determine the dry and wet periods. The second category of classification methods relies on dedicated rainfall observations in the vicinity of CMLs. As near-surface rainfall measurement instruments, rain gauges are widely used to achieve dry/wet period classification for CMLs [47; 48]. In the literature [49], considering the elevation angle of the radar, if the path-averaged rain rate of the radar QPE is greater than 0.1 mm/h, the current and subsequent time steps are regarded as wet periods. [PERSON] et al. [50; 51] utilized Meteosat Second Generation satellites to check for the presence of rain areas in the path of CMLs. The last category is data-driven approaches based on machine learning. Thanks to the massive data, they can derive hidden feature information to explore patterns of signal fluctuations and apply them to dry and wet period classification. [PERSON] et al. [52] demonstrated the applicability of a factor graph-based quasi-periodic signal model for dry and wet period classification. The literature [53; 54] reported cases where Markov models were employed to determine dry and wet periods. In addition, dry/wet classification based on the multifamily likelihood ratio test [55] and the kernel Fisher discriminant analysis [56] have been attempted. Subsequently, time-delay neural networks [57], long short-term memory neural networks (LSTM) [58; 59], support vector machines (SVM) [60] and convolutional neural networks (CNN) [61] are also used for dry and wet period identification for CMLs.