# 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  
Dichotomous statistics was applied to verify the products' respective ability to discriminate between precipitating and non-precipitating days. Starting from the daily time series of TAMSAT3, CHIRPS and MSWEP, contingency tables and the parameters POD, FAR, BIAS and HK (Table 1) were calculated for the two examined regions, by considering one of the datasets as reference. Comparisons between pairs of datasets are presented where the first product represents the reference data set against which the second one is evaluated. According to this scheme, the analyzed couples are: CHIRPS vs. TAMSAT3, with CHIRPS as reference dataset; CHIRPS vs. MSWEP, again with CHIRPS as a reference; and MSWEP vs. TAMSAT3, with MSWEP in the role of reference dataset. Figure 11 shows the spatial distributions of POD, FAR, BIAS, and HK on EA, whereas Table 3 summarizes the spatially averaged values with the corresponding standard deviations. On average, similar results emerge from the intercomparisons among the various products, assuring substantial agreement among the evaluated products in terms of precipitation detection. Note the best POD value (0.72 \(\pm\) 0.12) complemented by the best HK (0.62 \(\pm\) 0.11) from TAMSAT3 with respect to CHIRPS, and the worst results, from TAMSAT3 with respect to MSWEP, relative to FAR (0.45 \(\pm\) 0.20) and BIAS (1.5 \(\pm\) 0.89). Moreover, the analysis of the spatial distribution of the four indices makes possible to identify specific areas where the differences or the agreement among the products are more marked. MSWEP exhibits low POD values (\(\leq\)0.4) in Somalia, Eritrea, and Sudan compared to CHIRPS (Figure 11a). TAMSAT3 shows similar POD spatial patterns with respect to CHIRPS and MSWEP, with values \(\leq\) 0.4 in central Kenya, Ruanda, and Sudan (lowest values from the MSWEP-TAMSAT3 comparison). In the northern region of Sudan, the low precipitation amounts impact the ability to distinguish precipitating from non-precipitating days, especially in the CHIRPS vs. MSWEP comparison, where POD reaches its minimum. On the other hand, POD > 0.7 are found over the Ethiopian Highlands, South Sudan, Uganda and the entire region surrounding Lake Victoria, together with the lowest FAR values, demonstrating how the high amounts of precipitation typical of these areas \"facilitate\" the task of precipitation detection. The region with the worst FAR values (\(\sim\) 1) is northern Sudan, in particular for the MSWEP vs. TAMSAT3 comparison. The tendency of TAMSAT3 to identify more precipitating events in this area is further confirmed by the BIAS, with a wide region exhibiting values \(\geq\) 2 in the MSWEP vs. TAMSAT3 comparison and to a lesser extent for CHIRPS vs. TAMSAT3. Further regions with high FAR and BIAS values are Somalia and Eritrea (FAR > 0.8, BIAS > 1.4) for the MSWEP vs. TAMSAT3 case. In general, a better agreement between CHIRPS and TAMSAT3 emerges from the BIAS maps, with a weak tendency for TAMSAT3 to overestimate the number of precipitating days compared to CHIRPS in a large portion of EA. MSWEP underestimates the occurrence of precipitation compared to the other two products, especially in Somalia, Eritrea, and Sudan, and its behavior deviates more markedly from TAMSAT3 than from CHIRPS. Again, the HK maps confirm the previously described results, with values lower than 0.2 limited to the northern region of Sudan for the CHIRPS vs. MSWEP and MSWEP vs. TAMSAT3 comparisons. This is the area covered by cluster 7 (Figure 6g), with an average daily precipitation of < 0.3 mm day\({}^{-1}\), so it is possible that the threshold value of 1 mm day\({}^{-1}\) chosen as the discriminant for the precipitating and non-precipitating days is not appropriate.