# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 1 Introduction  
Precipitation is a crucial component of the hydrological cycle [1, 2] and plays a pivotal role in maintaining the energy balance [3]. Accurately quantifying precipitation poses a significant challenge due to its non-normal distribution and substantial spatiotemporal variabilities [4, 5, 6, 7]. Typically, precipitation can be observed through rain gauges or retrieved using ground-based radars and satellite sensors (Figure 1). Station measurements of precipitation have been recorded since the late 1890s. The distribution of these stations around the globe is uneven, with a tendency for sparser coverage of high-elevation regions and oceans. Moreover, inconsistencies arise in station measurements due to instrument replacements and variations in data preprocessing [8, 9]. The availability of precipitation-measuring ground radar dates back to the 1950s [10], and these instruments have been utilized as alternative means for obtaining accurate precipitation data since the 1970s [10, 11]. However, the limited coverage of such instruments around the globe is primarily attributed to their exorbitant operational and maintenance costs. Furthermore, radar data suffer from unavailability or are compromised due to range effects and beam blockage [12, 13, 14]. In contrast to ground-based observations, satellitesoffer a \"top-down\" approach for estimating precipitation with unparalleled spatiotemporal coverage and the ability to bridge data gaps in remote and unsampled regions [15; 16]. Figure 1: A brief history of precipitation-observing techniques, experiments, and products. Satellite data have been utilized for precipitation estimation for over six decades, with satellite precipitation estimation (SPE) primarily relying on brightness temperature (TB) obtained from low-Earth orbit (LEO) and geostationary (GEO) infrared (IR) sensors as well as passive microwave (PMW) sensors. Although IR data possess high spatiotemporal resolution, the relationship between cloud-top temperature and precipitation is indirect [17]. PMW data offer a direct inference of precipitation compared to IR data [18]. However, the former are currently accessible from LEO satellites with a spatial resolution typically below 5 \(\times\) 5 km. Launched in 1997, the Tropical Rainfall Measurement Mission (TRMM) carried a precipitation radar (PR) [19] that remained operational until April 2015. The launch of the TRMM marked a significant milestone in the advancement of precipitation retrieval using active microwave (AMW) sensors. Subsequently, in 2014, the Global Precipitation Measurement (GPM) Core Satellite was deployed with a dual-frequency radar (DPR), greatly enhancing the development of SPE [20; 21]. Despite being the most accurate, PRs and DPRs are currently employed as calibration references for obtaining global precipitation data. For instance, they provide vertical hydrometeor profiles utilized by the Goddard Profiling Algorithm (GPROF) to establish the foundation for PMW-based rain rate retrievals. IR, PMW, and AMW each possess distinct advantages and disadvantages. As a result, in the early 1990s, multi-sensor precipitation estimation (MPE) emerged with the objective of harnessing the benefits of multi-sensor methods while mitigating their limitations [22; 23]. MPE was defined by [PERSON] et al. [24] as a methodology that integrates IR- with PMW-based precipitation estimates through mathematical manipulations, resulting in a refined final product. Since its initial success, MPE has emerged as a widely accepted method for SPE, particularly since the 2000s. MPE has emerged as a prevalent approach for acquiring high-resolution and high-accuracy global satellite precipitation products (SPPs).