## 3 Other Applications
### Hydrological Application  
#### 3.2.1 Cooperation with Dedicated Rain Sensors  
As an additional source of precipitation information, rainfall estimates from CMLs can supplement or cooperate with dedicated rain sensors. [PERSON] et al. [130] utilized CMLs for quality control of rain gauges and detected the occurrence error and quantification error separately. In contrast, [PERSON] et al. [131] proposed a method for adjusting rainfall estimates from CMLs using rain gauges, demonstrating that even distant rain gauges can reduce bias. Rain gauges are also most commonly used to reconstruct rainfall fields in conjunction with CMLs [85; 92; 93].  
Due to uncertainties such as \(Z\)-\(R\) relationships, ground clutter and microwave attenuation, Radar QPE requires near-surface rainfall information for improvement. [PERSON] et al. [132] first suggested that microwave links could be used to improve rainfall estimates from X-band radars. In their procedure, microwave link measurements were used on the one hand for radar attenuation correction and on the other hand to calibrate the \(Z\)-\(R\) relationship. Shortly after, [PERSON] et al. [133] proposed a backward iterative algorithm for the attenuation calibration of X-band radars via attenuation measurements of microwave links. The results show that the method is effective in recovering the reflectivity information, especially under heavy rainfall conditions. More studies focused on radar attenuation correction using CMLs can be found in [134; 135; 136; 137]. In addition, [PERSON] et al. [138] proposed a mean-field bias adjustment method for a radar QPE based on CMLs, with similar results to a rain gauge-based adjusted QPE. [PERSON] et al. [139] then fused radar and CML rainfall observations to reconstruct accurate rainfall maps. Furthermore, CMLs can be combined with radar to invert the vertical profile of rainfall, which is of interest in semi-arid regions for studying the Virga phenomenon (evaporation of precipitation before reaching the ground) and improving the accuracy of near-surface rainfall estimates [140].  
For meteorological satellites, [PERSON] et al. [50; 51] improved rain area detection and rainfall estimation by combining signals from satellites and CMLs. In addition, they attempted to construct a random forest model to map the satellite-retrieved cloud top attributes to the ground rainfall by taking the rainfall estimates from CMLs as the true values [141]. The results show that the algorithm enables rainfall inversion with high spatial and temporal resolution. In addition, in regions where dedicated rain sensors are scarce, CMLs may be the most promising option to validate rainfall products from satellites [142].  
There are also research groups that assimilate precipitation data from CMLs and multiple dedicated rain sensors to jointly reconstruct the rainfall field. [PERSON] et al. [30] used variational and Gauss-Newton methods to invert rainfall fields from observations of rain gauges, radar and CMLs in Switzerland, which resulted in improvements over the raw radar QPE. In addition, as described in Section 2.1, dedicated rain sensors can also contribute to the dry and wet period classification of CMLs.