## 2 Procedures in Deriving Rainfall Maps from Attenuation
### Determination of Baseline
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Authors and Year** & **County** & **Highlights** \\ \hline [PERSON] et al. (2007) [44] & The Netherlands & Based on the historical data, a simple global threshold was constructed and the portion above the threshold was treated as wet periods. \\ \hline [PERSON] et al. (2010) [27] & France & Based on the different local variability of dry/wet period attenuation, the standard deviation of attenuation was calculated using a rolling window to differentiate between dry and wet periods according to a preset threshold. On average, 92\% of wet periods, 86\% of dry periods and 93\% of total rainfall were identified. \\ \hline [PERSON] et al. (2011) [49] & The Netherlands & Radar data were used to distinguish between dry and wet periods; considering that the correlation of RSL sequences of nearby CMLs in wet periods rises, the nearby link approach (NLA) was proposed to distinguish between dry and wet periods. Both radar and NLA were able to invert rainfall accurately with similar results. \\ \hline [PERSON] et al. (2011) [52] & Switzerland & The Gaussian factor graph was used to distinguish between the dry and wet periods. Case studies showed that the proposed method has a high classification performance. \\ \hline [PERSON] et al. (2012) [28] & Germany & Due to the significant increase in the high-frequency components of the signal during the wet period, a spectral analysis method based on the short-time Fourier transform was proposed to classify dry and wet periods. The weighted mean error rate of the classification results was as low as 0.098. \\ \hline [PERSON] et al. (2012) [54] & Switzerland & A Markov switching model was used to differentiate between dry and wet periods, which was compared with rolling standard deviation, factor graphs and the global threshold method. The false-positive and false-negative rates were about 8\% and 15\%, respectively, in the case of a stationary baseline. In the case of an unstable baseline, the false-positive and false-negative rates were about 5\% and 23\%, respectively. \\ \hline [PERSON] et al. (2012) [53] & Israel & A Hidden Markov Model was used to determine the dry and wet periods. Rainfall inversions using this method showed good correlation and low bias compared with rain gauge results. \\ \hline [PERSON] et al. (2012) [63] & Israel & Based on the statistical characteristics of attenuation, a linear Fisher’s discriminant was used to distinguish between dry and wet periods and was able to identify 83\% of wet periods, with a false-positive rate of 12\%. \\ \hline [PERSON] et al. (2013) [55] & Israel & An algorithm based on a multifamily likelihood ratio test was used to separate dry and wet periods, and the true-positive rate could reach about 90\%. \\ \hline [PERSON] et al. (2013) [57] & Germany & Focused time-delay neural networks were used to distinguish between dry and wet periods. The average test error of the classification results was only 1.1095\%, with a correlation coefficient of 0.9647. \\ \hline [PERSON] et al. (2014) [56] & Israel & Based on the statistical characteristics of attenuation, the kernel Fisher’s discrimination was used to distinguish dry and wet periods. The results showed that the classification accuracy could reach 85.35\%. \\ \hline [PERSON] et al. (2019) [59] & China & A dry and wet period classification algorithm based on LSTM was proposed. The daily classification accuracy exceeded 60\%, with some results achieving up to 98\%. \\ \hline [PERSON] et al. (2020) [61] & Germany & A dry and wet period classification algorithm based on CNN was proposed. An average of 76\% wet periods and 97\% dry periods were detected in the validation results. \\ \hline [PERSON] et al. (2020) [60] & China & SVM was used to distinguish between dry and wet periods based on statistical features of attenuation. The classification accuracy exceeded 0.8 and the majority of the outcomes displayed true-positive and false-positive rates that exceeded 0.9 and were less than 0.2, respectively. \\ \hline [PERSON] et al. (2020) [50] & Kenya & Satellite data were used to identify rain areas along the CML path. The accuracy of rainfall inversion for CMLs supported by satellite data was high. \\ \hline \hline \end{tabular}
\end{table}