# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 6 Validation
### Data Preprocessing and Statistical Metrics  
The validation of SPP or satellite sensor products includes using ground precipitation observations as a reference and mathematical methods (e.g., triple-collocation method and generalized three-cornered hat method) that do not depend on ground data as the benchmark. Most of the SPP validation studies have used the former method. The validation process of the SPE is illustrated in Figure 5. However, obtaining 'true' precipitation data that match the time and space scales of satellite data or possess the same spatial coverage is often challenging. To address this issue, it is common practice to compare SPEs with reference precipitation data, such as ground observations or satellite radar measurements [160], which are considered to have smaller estimation errors. Recognizing the increasing significance of a ground validation network for global precipitation measurements [161], collecting reference precipitation data (from gauges, disdrometers, or radars) serves as the initial step, which is followed by performing spatial and temporal matching tasks. Ultimately, a comparison between the SPE and reference data can be conducted. Global validation studies are constrained by temporal and spatial inconsistencies in the reference data [162]. Consequently, numerous studies have been conducted over the past three decades to validate SPEs across different continents and spatiotemporal scales, such as river or lake basins or entire countries [156; 157; 163; 164; 165; 166; 167; 168; 169; 170; 171]. Some of these efforts have focused on analyzing the relationship between SPE performance and season/latitude [172; 155], while others have assessed the impact of elevation on SPE performance [173; 174; 175; 176; 177]. Additionally, research has explored how precipitation rates influence SPE accuracy and investigated the relationships between precipitation types and SPE performance [178; 171; 179; 180; 171; 172]. Certain studies have also examined extreme precipitation events and tropical cyclones [182; 183; 184; 185], compared SPE performance in arid versus wet regions [186; 187], or conducted global validations instead of validating a single SPE [188; 143; 189; 53]. After determining the study region and scale, the next step involves comparing SPEs with reference data; this comparison process necessitates spatial and temporal matching. Figure 5: Schematic diagram showing the SPE validation process. Point-grid comparisons directly match the pixels and gauge(s) within each dataset [147]. Grid-grid comparisons integrate multiple pixels and gauges within an area into a larger grid scale, followed by matching the resulting grids [190]. These matching processes always involve calculating either arithmetic mean values (e.g., [175]) or weighted values (e.g., using the Thiessen polygon method) of the gauges in each pixel (e.g., [191]). When utilizing radar measurements as references, it is essential to adjust the grids to a consistent scale through spatial matching methods (e.g., interpolation method) [192; 193]. The temporal matching process entails aggregating precipitation data to identical timescales, such as from hourly to daily to monthly scales (Figure 5). Obtaining both quantitative and qualitative accuracy is the ultimate objective of these processes. The most commonly employed approach involves constructing scatter plots to examine the overall performance of statistical prediction equations using validation indices, such as the coefficient of determination (R2), accuracy (A) (commonly referred to as bias), precision (P), and uncertainty (U) (usually denoted by the root mean square error [RMSE]) [194]. Additionally, categorical metrics including probability of detection (POD), false alarm rate (FAR), and Heidke Skill Score (HSS) [147] are utilized. Furthermore, Taylor diagrams [195], spatiotemporal patterns, and their corresponding D values are frequently adopted for illustrating both the quantitative and qualitative accuracies of SPEs [196].