*2.2. Satellite-Based Rainfall*

Due to the scarcity and poor spatial representativeness of rainfall data from ground observations [33,35], we rely on satellite-based rainfall estimates (SRE) from TRMM Multisatellite Precipitation Analysis 3B42 Real-Time, version 7 (hereafter spelled TMPA-RT). TMPA-RT data are freely available with a latency of 8 h over 50◦ N–50◦ S, at 0.25◦ × 0.25◦ and three-hourly spatiotemporal resolution, covering the period 2000 to present without gaps in space and time [48]. A recent paper on SRE over Central Africa [56] shows that TMPA has overall good skills in detecting and estimating daily rainfall as compared to ARC, CHIRPS, CMORPH, PERSIANN, TAPEER, TARCAT (see [56] for these acronyms' meanings). In their study, the Research Version of TMPA was used, a product that is outperformed by TMPA-RT over the WEAR with regard to rainfall detection skills and absolute errors [33]. Moreover, the short latency of TMPA-RT compared to the two-month latency of the Research Version, is of crucial importance in the context of early warning systems. Despite the relatively good performance of TMPA-RT in Central Africa, the WEAR is a challenging environment for SRE due to its complex topography, high rainfall variability, and presence of large lakes, with a resulting mean bias in daily rainfall estimates in the order of ~40% [33]. TMPA-RT three-hourly rainfall data have been downloaded from NASA Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/, last access: 14 April 2019) for the period 2000 to 2019 and accumulated to daily rainfall to maintain consistency with the temporal resolution of the landslide inventory.
