# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 6 Validation
### Uncertainties and Recommendations for SPPs
Consequently, it is not possible to designate any single algorithm or SPP as superior to the others (Table 4). The abovementioned validation practices yield consistent conclusions: MPEs face difficulties in accurately estimating light precipitation rates, snowfall, and orographic rain. SPEs exhibit substantial uncertainties in regions at moderate to high latitudes, especially during cold seasons and over areas characterized by complex terrain. For instance, [PERSON] et al. [215] and [PERSON] and [PERSON] [188] investigated the errors of HRPPs (GSMaP-MVK, CMORPH, 3B42, and PERSIANN) worldwide and reported that while SPPs perform well for precipitation rates exceeding 40 mm/d, they tend to underestimate a significant portion of light precipitation (<10 mm/d) up to approximately 40%. In addition, significant uncertainties (ranging from 100% to 140%) were observed over high latitudes (poleward of 40\({}^{\circ}\) latitude), particularly during the cold season, as well as in regions characterized by complex terrain (such as the Tibetan Plateau), coastlines, and water bodies. [PERSON] et al. [189] reported that the overall bias of 3B42 falls within the range of \(-\)16.89% to 0.27% (RMSE: 5.28-8.52 mm/d), while indicating that, globally, precipitation is overestimated by 12.57-41.28% with respect to 3B42 RT (RMSE: 5.07-7.53 mm/d). [PERSON] et al. [168] demonstrated that HRPPs' overall biases on a daily scale in China are within \(\pm\)10%, including IMERG, 3B42, CMORPH, GSMaP-gauge and PERSIANN-CDR. The results of IMERG validation studies, as summarized by [PERSON] et al. [58], confirm that the bias of IMERG ranges from \(-\)58.77% to 13.5% (9.7-47.5 mm/h) for extreme events and from \(-\)28.7% to 41.4% on the daily scale in hydrological applications. These previous studies indicate that the latest IMERG product exhibits significant improvement over the TRMM dataset, highlighting its potential for various present and future applications. Previous studies primarily focused on PMW sensor estimates instead of SPPs. For instance, [PERSON] et al. [216] indicated that PMW imagers are superior to sounders and morph (PMW-IR using the morphing technique). Among the other imagers, TMI shows the best performance with a bias of \(-\)8.8%, while SSMIS shows the worst performance with a bias of 39.8%. GMI is skillful in detecting heavy rains but overestimates light to moderate rains. All sounders along with morph have large biases, typically >120%. The advantages of PMW imagers over sounders were mentioned by [PERSON] et al. [217]. They also revealed the inefficiency of GMI in winter/spring and over arid/semi-arid regions. [PERSON] et al. [218] reported the best performance of MHS among other sensors including GMI, SSMIS, ATMS, AMSR-2 and SAPHIR. Land cover also matters. Generally, large uncertainties are observed over low-lying, urban, water, and coastal areas. In addition, [PERSON] et al. [219] analyzed the performance of PMW sensor estimates for quantifying extreme rates. The results showed that MHS is the best sensor in this regard, while SAPHIR shows the worst performance.