# Precipitation Products' Inter-Comparison over East and Southern Africa 1983-2017
## 5 Discussion  
The most problematic regions for precipitation detection and estimate are located in arid, mountainous and coastal areas. Arid regions are acknowledged as problematic for satellite estimates because of precipitation overestimation with respect to rain gauge measurements due to sub-cloud evaporation [8]. From the dichotomous statistics, differences in the precipitation detection emerged in Northern Sudan, Somalia and the Horn of Africa, and coastal Namibia. The problem is connected with the low daily precipitation values in these areas, usually no more than 1 mm day\({}^{-1}\) on average, which is near the threshold value used for precipitation detection. This means that even low estimation errors can determine differences in detection among the products. Moreover, not all mentioned arid areas provided an equal response in this analysis. EA BIAS (Figure 1c) indicates MSWEP as the product with the lowest number of precipitating days, whereas TAMSAT3 overestimates the other two products. On the contrary, TAMSAT3 underestimates precipitation occurrences with respect to both products in the coastal Namib Desert. In this case, the proximity to the coast and presence of non-convective precipitating systems further complicate satellite retrieval [16]. Even coastal and mountain areas are considered among the most demanding contexts for satellite precipitation estimation. In particular, IR-based products can underestimate precipitation over coastal and mountain regions because most of the rain comes from clouds with top temperatures higher than the threshold used by the satellite algorithms [8]. TAMSAT3 tends to underestimate daily precipitation compared to CHIRPS and MSWEP on the eastern coast of Madagascar and Mozambique (Figure 1b), as confirmed by [PERSON] et al. and [PERSON] et al. at decadal and monthly scales [16; 17], and misses precipitation on the coasts of Kenya and southern Somalia with respect to CHIRPS, which exhibits higher precipitation amounts (Figure 13). The coastal region near Cape Town is another emblematic area for the satellite product differences, where TAMSAT3 performance is not aligned with the other two products in terms of detection (HK < 0.3; Figure 12) and to a lesser extent estimation (ME \(\in\) [-0.6, 0] mm day\({}^{-1}\), Figure 14). In the Ethiopian Highlands region, the differences are more limited to precipitation estimation, with MSWEP systematically underestimating precipitation compared to the other two products, which show more similar behavior (Figure 13). Altogether, the agreement among the three datasets in describing precipitation over EA and SA is considerable, in spite of the specific differences summarized previously. This demonstrates once more the applicability and reliability of IR-based algorithms such as CHIRPS and TAMSAT3 for the evaluation of precipitation long-term variability in Africa with respect to a multi-source precipitation product such as MSWEP, which also exploits passive microwave-based precipitation estimates and reanalyses. Moreover, from the study of the statistical indices as a function of time it is clear that the agreement between the products has generally improved over time, showing more stable capabilities to identify precipitating days and estimate daily precipitation, in particular starting in the late 1990s. Thus, as highlighted by [PERSON] et al. (2013), who compared MSWEP v. 2.1 at the monthly scale to various precipitation products including CHIRPS, the results of this analysis do not identify any explicit advantage in using MSWEP rather than CHIRPS and TAMSAT3 in EA and SA, so it is not possible nor appropriate to establish a ranking. Nevertheless, greater similarity between CHIRPS and TAMSAT3 was observed in EA, with MSWEP deviating more, whereas CHIRPS and MSWEP exhibited a higher degree of agreement in SA, with TAMSAT3 showing a tendency to underestimate precipitation.