# Precipitation Products' Inter-Comparison over East and Southern Africa 1983-2017
## 4 Results
### Precipitation Product Intercomparisons at Daily Scale
#### 4.2.1 Dichotomous Statistics: Rain Detection
In the rest of EA, HK exceeds 0.4, with large regions characterized by values even higher than 0.6 and 0.8, denoting how the datasets have similar characteristics relative to precipitation detection even with underestimation by MSWEP relative to the other products. Figure 11: _Cont._  
Following the procedure previously described, Figure 12 presents the maps of POD, FAR, BIAS and HK for SA, whereas Table 4 reports the same statistical parameters averaged on the region together with the corresponding standard deviations. Considering the scores in Table 4, an agreement among the products even better than that found in EA emerges, characterized by a general improvement in the scores with lower variability (lower standard deviation). On average, TAMSAT3 and MSWEP give raise to very similar statistical scores when compared to CHIRPS. Looking at the score maps, the coast of Namibia and region of Cape Town, Southern Mozambique, and the east coast of Madagascar are the areas where POD, FAR, BIAS and HK deteriorate with respect to the rest of SA. The higher values of the statistical parameters are found in the northern belt of SA (Angola, Zambia and Northern Mozambique). The coast of Namibia, the region of Cape Town, and southern Mozambique are characterized by HK\(<\)0.2 for CHIRPS vs. TAMSAT3 and MSWEP vs. TAMSAT3, demonstrating a different capability of TAMSAT3 with respect to the other products in identifying the precipitating days (Figure 12d). As for the desert of Sudan in EA, the coast of Namibia is a desert area with a low number of precipitating days. The average daily precipitation does not exceed 0.5 mm day\({}^{-1}\); therefore, it is reasonable to expect the dichotomous approach be problematic, given 1 mm day\({}^{-1}\) as the threshold to discriminate between precipitating and non-precipitating days. The presence of a POD latitudinal gradient (Figure 12a) is particularly visible in the CHIRPS vs. TAMSAT3 and MSWEP vs. TAMSAT3 maps, with maximum values close to 1 in northern SA, on the west coast of Madagascar and in the mountain region surrounding Lesotho, decreasing below 0.3 when moving southward on the east coast of Madagascar, Southern Mozambique, Cape Town region and the coast of Namibia, where the index reaches its minimum around 0.1. In Figure 12b the worst FAR values correspond to the zones with low POD values in south-west RSA and improve towards the north. In Figure 12c the spatial distributions of the BIAS values confirm the TAMSAT3 tendency to underestimate the number of precipitating days compared to the other two satellite  
Figure 11: Maps of POD (**a**), FAR (**b**), BIAS (**c**), and HK (**d**) for EA. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **POD** & **FAR** & **BIAS** & **HK** \\ \hline CHIRPS vs. TAMSAT3 & 0.72 \(\pm\) 0.12 & 0.38 \(\pm\) 0.15 & 1.22 \(\pm\) 0.28 & 0.62 \(\pm\) 0.11 \\ CHIRPS vs. MSWEP & 0.60 \(\pm\) 0.20 & 0.35 \(\pm\) 0.16 & 0.94 \(\pm\) 0.29 & 0.52 \(\pm\) 0.15 \\ MSWEP vs. TAMSAT3 & 0.66 \(\pm\) 0.14 & 0.45 \(\pm\) 0.20 & 1.50 \(\pm\) 0.89 & 0.56 \(\pm\) 0.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: POD, FAR, BIAS and HK spatially averaged over EA with the corresponding standard deviation values. products on the east coast of Madagascar, southern Mozambique and the Namibian coast, whereas BIAS values very close to 1 come from the comparison of MSWEP to CHIRPS. On the contrary, TAMSAT3 overestimates the occurrence of precipitation in RSA.