# Precipitation Products' Inter-Comparison over East and Southern Africa 1983-2017
## 4 Results
### Temporal Stability of the Rain Detection and Estimation Capabilities
As for the quantitative indices, the situation is very similar to that depicted for EA (Figure 18), with increasing CC values reaching 0.7 except for the case of MSWEP vs. TAMSAT3 (Figure 18c). The two products are more dissimilar in this region, as demonstrated by the higher MAE values. Figure 16: EA-averaged annual ME (mm day\({}^{-1}\)), MAE (mm day\({}^{-1}\)) and CC (right y-axis) for TAMSAT3 with respect to CHIRPS (**a**), MSWEP with respect to CHIRPS (**b**), and TAMSAT3 with respect to MSWEP (**c**). Figure 17: Same as in Figure 14 but for SA: TAMSAT3 with respect to CHIRPS (**a**), MSWEP with respect to CHIRPS (**b**) and TAMSAT3 with respect to MSWEP (**c**). Figure 18: Same as in Figure 15 but for SA: TAMSAT3 with respect to CHIRPS (**a**), MSWEP with respect to CHIRPS (**b**) and TAMSAT3 with respect to MSWEP (**c**). In this analysis, the year 2000 often stood out as a turning point in the annual time series of the examined statistical parameters, with a sort of stabilization of the parameters' variability from 2000 onward. Various factors could have contributed to this pre- and post-2000 change. Before 2000, the days without IR data from geostationary platforms (METEOSAT First Generation satellites) are more numerous with respect to recent decades when the METEOSAT Second Generation satellites entered their operational phase. For example, the TAMSAT3 time series has as many as 80 missing days per year before 2000. These data gaps are filled in using climatological data in TAMSAT3 and CHIRPS according to different methodologies (see Section 2.1.1 and [28] for TAMSAT3 and CHIRPS, respectively), and these procedures can introduce some discontinuities in the time series. In many countries of EA and SA the availability of rain gauges was subject to a drastic decrease, frequently occurring after 2000 (as confirmed on the web page of CHIRPS dignoatics, [[https://www.chc.ucsb.edu/data/chirps/diagnostics#stationsbycountry](https://www.chc.ucsb.edu/data/chirps/diagnostics#stationsbycountry)]([https://www.chc.ucsb.edu/data/chirps/diagnostics#stationsbycountry](https://www.chc.ucsb.edu/data/chirps/diagnostics#stationsbycountry)), last access 2 July 2021). A similar change around the year 2000 was recently identified for monthly CHIRPS estimates against GPCC data globally [54]. The results indicate that CHIRPS underestimates precipitation before 2000 with respect to GPCC, while this systematic underestimation is effectively removed after 2000. The authors also mention the CHIRPS calibration methodology as a key factor; the algorithm calibrates the CCD regressions using a TMPA 3B42 data set covering the time period 2000-2013. Therefore, it is possible that the CHIRPS CCD-precipitation relationships are more representative of recent decades than they are for 1983-1999. Finally, it is necessary to consider the differences in the input data for the generations of the MSWEP product during the two periods 1983-1999 and 2000-2017. With reference to Figure 4 in [31], where the relative weights assigned to the gauge-, satellite-, and reanalysis-based precipitation estimates are represented, it is clear that in the first period over EA and SA MSWEP is mainly based on rain gauges, with a minor contribution from reanalysis over the rain gauge lacking areas (e.g., Somalia). From 2000 onwards, the merging between satellite precipitation estimates based on passive microwave sensors and reanalysis becomes predominant in EA, except for the area surrounding Lake Victoria where rain gauges are still the main source of input data. On the contrary, in SA rain gauges continue to drive MSWEP estimates, with a minor contribution from the merging between satellite and reanalysis data.