# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 7 Conclusions and Outlook  
The precipitation community has greatly benefited from the availability of multiple long-term satellite precipitation records derived from both infrared (IR) and passive microwave (PMW) sensors. Remarkable advancements have been achieved in the development of multi-sensor precipitation estimation (MPE) over the past two to three decades. Currently, MPE stands as the predominant method for generating global satellite precipitation products (SPPs). Satellites and sensors, particularly those within the Global Precipitation Measurement (GPM) constellation, offer invaluable data support. The key to obtaining accurate MPEs lies in leveraging PMW- and IR-based observations and/or precipitation estimates through a combination of methods. These estimates are crucial for advancing MPE techniques, while other sources such as radar measurements, ground observations, and additional precipitation datasets play complementary roles. Two broad categories of techniques have been developed for integrating PMW precipitation and/or IR observations. The first category, known as the cloud-motion-based approaches (employed in CMORPH, GSMaP, and GPM IMERG), involves propagating and interpolating instantaneous PMW estimates using the cloud motion vectors derived from IR images. The second category primarily encompasses approaches based on PMM, AR, regression, and NN methods. These approaches inherently assume that PMW estimates represent accurate measurements of instantaneous precipitation rates (as seen in TMPA, PERSIANN, and GPCP). Additionally, a weighted-average-based method is utilized in the GPCP and CMAP products. Numerous international projects, programs, and research studies have investigated MPE uncertainties with a primary focus on SPP performance evaluation while gradually establishing validation methodologies. Generally speaking, MPE uncertainties arise due to data quality issues (primarily related to PMW and IR estimates) as well as the MPE algorithms themselves. Despite significant advancements in MPE techniques over time [16], several challenges still remain at the forefront [58; 220]. Future attention should be directed toward addressing these issues. (1) Improved PMW sensors are crucial for the advancement of MPE, and the current GPM constellation has greatly contributed to this field over the past few decades. It has been observed that the performance of almost all SPEs in MPE heavily relies on the accuracy of PMW estimates [55; 199]. However, due to the limited spatial and temporal resolution of LEO sensors, the present PMW data are collected at a spatial resolution of approximately 0.1\({}^{\circ}\) every three hours, leading to deficiencies in recovering precipitation at the sub-mesoscale. (2) The improved consistency among multi-sensor data and the optimal integration of multi-sensor retrievals are essential. Inconsistencies arise from the distinct spatiotemporal samplings and error structures inherent in the different instruments and frequency channels [30]. Coupled with diverse estimation algorithms, these characteristics contribute to uncertainties in precipitation retrievals [43; 221]. Despite the existence of various proposed and developed algorithms, determining the optimal integration of multi-sensor estimates has remained a focal point in MPE studies, which continues to be an ongoing endeavor. (3) Emphasis should be placed on estimations of extremely low and high precipitation, hail, and snowfall, as well as precipitation over water bodies, mountainous regions, and coastal areas. The challenge in estimating orographic precipitation primarily stems from difficulties in detecting the influence of topography on precipitation [96; 217]. Numerous studies have confirmed the inadequate performance of statistical postprocessing (SPP) methods at high latitudes (above 40\({}^{\circ}\) latitude), particularly during the cold season, and over regions with complex terrain such as the Tibetan Plateau, coastlines, and water bodies [155; 217].