# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 6 Validation
### Uncertainties and Recommendations for SPPs  
Despite recent advancements, SPPs are not exempt from uncertainties [197; 198; 199; 200]. The uncertainties of MPE products primarily stem from the data sources and combining algorithms [201; 202]. Evidently, the uncertainties in IR-based and PMW-based estimations propagate to MPEs [203]. For instance, [PERSON] et al. [204] observed significant uncertainties in IR-based precipitation estimates that carried over into the final merged product. [PERSON] et al. [205] argued that the improvement relative to the TMPA IR component was more substantial than that relative to the PMW retrieval due to the Kalman smoother and PMW morphing steps in IMERG reducing the reliance on IR compared to TMPA. Studying and quantifying the uncertainties in both IR-based and PMW-based precipitation estimations pose a challenge [111; 81]. This becomes even more intricate when examining MPE uncertainties. Despite the similar input data, there are significant variations in the methods used for estimating precipitation in MPE algorithms through interpolation and merging. The selection of a combining method also introduces considerable uncertainties. Each available combining method has its own advantages and disadvantages (Table 3). Generally, the cloud-motion-based approach heavily relies on PMW data, where PMW estimates are propagated along cloud motions from the observation time to the target analysis time [26; 110]. One notable challenge is that these algorithms implicitly assume the linear evolution of precipitation between PMW images over time. Another limitation of this approach is that the cloud tops detected with IR imagery may move at different speeds than the underlying precipitation features. Although this discrepancy can be adjusted using ground radar data, it may not adequately account for precipitation within the moving interval. Most other methods inherently assume that PMW estimates accurately represent instantaneous precipitation rates. The key to this approach lies in the temporally changing and regionally dependent empirical relationship between IR Tb and PMW precipitation intensity. However, a direct physical connection between precipitation and cloud-top temperature is absent, leading to significant errors in IR precipitation estimates and consequently affecting the final merged PMW-IR estimates [25; 29]. Furthermore, uncertainties arise in the distribution of precipitation due to the subjective assumptions made for the rain/no-rain threshold. Generally, the performance of these methods heavily relies on the quality of the utilized PMW estimates, while spatial patterns are closely associated with IR data. In order to successfully obtain merged estimates using a weighted-average-based method, accurately defining the bias and error structuresof individual data sources is crucial. Particularly, it is essential to investigate the relationship among errors in various individual data sources. To enhance accuracy, MPE algorithms can incorporate ground-based observations [25; 111] and integrate different correction procedures. However, these algorithmic variations and the corresponding correction techniques may introduce diverse uncertainties [206]. Furthermore, continuous updates to the algorithms contribute to their evolving nature. Additionally, the spatiotemporal aggregation of precipitation estimates introduces uncertainty, such as inconsistency even after applying normalization processes [97; 207]. For instance, the inconsistencies among sensors carried on various satellites (e.g., SSM/I and SSMIS sensors; GOES Imager and ABI sensors) can lead to temporal aggregation uncertainties [29], while the disparities among heterogeneous sensor types (e.g., SSMIS, ATMS, AMSR-E and GMI of PMW sensors; ABI SEVIRI and AHI of GEO-IR sensors) may result in spatial aggregation uncertainties [200; 208]. These inconsistencies primarily arise from differences in data quality, sampling scales, sensor frequencies, and blending processes.