## 2 Materials and Methods
### IR Sensor-Based Satellite Products  
To provide global rain-rate estimates, IR sensors are usually used onboard GEO satellites [1], which allow for a high temporal resolution (every 30 min or even less) [12]. The estimations of rain rates (\(R\)) are given by empirical relationships between \(R\) and the cloud-top brightness temperature \(T_{b}\)[15; 33]. Being strongly related to the cloud heights, this type of estimation presents some limitations, such as a strong association to convective rain regimes, and conversely with non-convective rainfall, or the difficulty to distinguish precipitation patterns over complex terrain [34]. The Global Hydro Estimator (GHE) is derived from the Hydro-Estimator algorithm (HE; [17]), which is provided by the National Oceanic and Atmospheric Administration--National Environmental Satellite, Data and Information Service. GHE instantaneous rain-rate product has a spatial resolution of 4 \(\times\) 4 km\({}^{2}\) and a temporal resolution of 15 min (see Table 1), and uses estimations of cloud-top brightness temperature from IR sensors. GHE uses a fixed relationship between these cloud-top brightness temperatures and rain rates, calibrated with radar data [35]. GHE includes the information given by the National Centres for Environmental Prediction Global Forecast System (GFS) model to complete its algorithm [36]. GHE inputs can use regional model outputs, such as those for South America [37]. [PERSON] et al. [38] also considered the eventuality of associating GHE skills and PMW sensors. The Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Network (PERSIANN) Dynamic Infrared Rain Rate (PDIR) was developed by the Centre for Hydrometeorology and Remote Sensing (CHRS) at the University of California [39]. Initially, the PERSIANN algorithm [18] was based on the artificial neural network technique to estimate rainfall accumulation. Then, the PERSIANN--Cloud Classification System (PCCS) resumed the same PERSIANN features; however, the PCCS classifies cloud-patch features based on the texture, geometry, dynamic evolution, and cloud-top height of clouds from IR sensor-based satellite estimates [40] and does not use PMW data unlike PERSIANN [33]. This cloud classification is performed by using a variable threshold cloud segmentation algorithm and varies the \(T_{b}\)-\(R\) relationships. The PDIR algorithm is a near-real-time (NRT) precipitation product like the PCCS, with several modifications developed by [PERSON] et al. [33]:  
* A better identification of warmer stratiform clouds from higher temperature thresholds;
* An improvement in the watershed method used for the image segmentation;
* An improvement in the rainfall regime identification by adding monthly sets of cloud types to the existing cloud classification system;
* The use of PMW satellite data to adjust \(T_{b}\)-\(R\) curves;
* The integration of climatology data from WorldClim version 2 [41] over land and PERSIANN Climate Data Record (PCDR; [42]) over the ocean to create a dynamical \(T_{b}\)-\(R\) curve model. In this study, PDIR is an NRT precipitation product and provides 4 \(\times\) 4 km\({}^{2}\) gridded rain-rate estimations with an hourly temporal resolution. Microwave Sensor-Based and Blended Products  
Two types of microwave-based sensing techniques were used in this study:  
* PMW sensors measure the upwelling radiation from the Earth's surface which is affected by precipitation-sized particles [1]. This upwelling radiation is affected differently depending on the iced or liquid nature of the particles [43];
* AMW sensors are the most direct way to estimate precipitation amounts because they are based on the analysis of backscatter signals from rain-sized target particles [1; 44], such as ground-based weather radars [45]. However, PMW and AMW sensors are carried by LEO sensors only [12].