Long and temporally consistent rainfall time series are essential in climate analyses and applications. Rainfall data from station observations are inadequate over many parts of the world due to sparse or non‐existent observation networks, or limited reporting of gauge observations. As a result, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. However, many satellite‐based rainfall products with long time series suffer from coarse spatial and temporal resolutions and inhomogeneities caused by variations in satellite inputs. There are some satellite rainfall products with reasonably consistent time series, but they are often limited to specific geographic areas. The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are recently produced satellite‐based rainfall products with relatively high spatial and temporal resolutions and quasi‐global coverage. In this study, CHIRP and CHIRPS were evaluated over East Africa at daily, dekadal (10‐day) and monthly time‐scales. The evaluation was done by comparing the satellite products with rain‐gauge data from about 1,200 stations. The CHIRP and CHIRPS products were also compared with two similar operational satellite rainfall products: the African Rainfall Climatology version 2 (ARC2) and the Tropical Applications of Meteorology using Satellite data (TAMSAT). The results show that both CHIRP and CHIRPS products are significantly better than ARC2 with higher skill and low or no bias. These products were also found to be slightly better than the latest version of the TAMSAT product at dekadal and monthly time‐scales, while TAMSAT performed better at the daily time‐scale. The performance of the different satellite products exhibits high spatial variability with weak performances over coastal and mountainous regions.

An extensive evaluation of 10 different satellite rainfall products was performed using station network over a complex topography, where elevation varies from below sea level to 4620 m. Evaluation was for two groups of products. The first group had low spatial (2.5°) and temporal (monthly) resolution and included the Global Precipitation Climatology Project (GPCP), the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA‐CPC) merged analysis (CMAP), and the Tropical Rainfall Measuring Mission (TRMM‐3B43). The second group comprised products with relatively high spatial (0.1° to 1°) and temporal (3‐hourly to 10‐daily) resolution. These included the NOAA‐CPC African rainfall estimation algorithm, GPCP one‐degree‐daily (1DD), TRMM‐3B42, Tropical Applications of Meteorology using SATellite and other data (TAMSAT) estimates, and the CPC morphing technique (CMORPH). These products were aggregated to a 10‐day total and remapped to spatial resolutions of 1°, 0.5° and 0.25°. TRMM‐3B43 and CMAP from the first group and CMORPH, TAMSAT and TRMM‐3B42 from the second group performed reasonably well.

High‐resolution satellite rainfall products, at daily accumulation and 0.25° spatial resolution, are evaluated using station networks located over two different parts of Africa. The first site is located over Ethiopia with a very complex terrain. The second site, located over Zimbabwe, has a less rugged topography. The evaluated satellite rainfall products are the NOAA‐CPC African rainfall estimation algorithm (RFE), TRMM‐3B42, the CPC morphing technique (CMORPH), PERSIANN, and the Naval Research Laboratory's blended product. These products perform reasonably well over both regions in detecting the occurrence of rainfall. However, performances are poor in estimating the amount of rainfall in each pixel. The correlation coefficients are low and random errors high. The performance was better over Zimbabwe as compared with Ethiopia. Comparing the different products, CMORPH and TRMM‐3B42 showed a better performance over Ethiopia, while RFE, CMORPH, and TRMM‐3B42 preformed relatively better over Zimbabwe.

Climate data are used in a number of applications including climate risk management and adaptation to climate change. However, the availability of climate data, particularly throughout rural Africa, is very limited. Available weather stations are unevenly distributed and mainly located along main roads in cities and towns. This imposes severe limitations to the availability of climate information and services for the rural community where, arguably, these services are needed most. Weather station data also suffer from gaps in the time series. Satellite proxies, particularly satellite rainfall estimate, have been used as alternatives because of their availability even over remote parts of the world. However, satellite rainfall estimates also suffer from a number of critical shortcomings that include heterogeneous time series, short time period of observation, and poor accuracy particularly at higher temporal and spatial resolutions. An attempt is made here to alleviate these problems by combining station measurements with the complete spatial coverage of satellite rainfall estimates. Rain gauge observations are merged with a locally calibrated version of the TAMSAT satellite rainfall estimates to produce over 30‐years (1983‐todate) of rainfall estimates over Ethiopia at a spatial resolution of 10 km and a ten‐daily time scale. This involves quality control of rain gauge data, generating locally calibrated version of the TAMSAT rainfall estimates, and combining these with rain gauge observations from national station network. The infrared‐only satellite rainfall estimates produced using a relatively simple TAMSAT algorithm performed as good as or even better than other satellite rainfall products that use passive microwave inputs and more sophisticated algorithms. There is no substantial difference between the gridded‐gauge and combined gauge‐satellite products over the test area in Ethiopia having a dense station network; however, the combined product exhibits better quality over parts of the country where stations are sparsely distributed.

Different satellite rainfall products are used in different applications over different parts of the world. These products are particularly important over many parts of Africa, where they are used to augment the very sparse rain-gauge network. However, the quality of the different satellite products varies from one product to another and from one climatic region to another. The climate over eastern Africa varies from wet coastal and mountainous regions to dry arid regions. Significant variations could be observed within short distances. The different climates will pose different challenges to satellite rainfall retrieval over this region. This study explores the effect of mountainous and arid climates on four different satellite rainfall-estimation (RFE) algorithms. The mountainous climate is located over the Ethiopian highlands, while the arid region covers parts of Ethiopia, Djibouti and Somalia. One infrared-only product, African rainfall climatology (ARC), one passive-microwave-only product (the Climate Prediction Center morphing technique, CMORPH) and two products (the RFE algorithm and the tropical rainfall measuring mission (TRMM-3B42)), which combine both infrared and passive-microwave estimates, are used for this investigation. All the products exhibit moderate underestimation of rainfall over the highlands of Ethiopia, while the overestimation over the dry region is found to be very high. The underestimation over the mountainous region is ascribed to the warm orographic rain process, while the overestimation over the dry region may be because of sub-cloud evaporation. Local calibration of satellite algorithms and merging of satellite estimates with all locally available rain-gauge observations are some of the approaches that could be employed to alleviate these problems.

Seven different satellite rainfall estimates are evaluated at daily and 10-daily time scales and a spatial resolution of 0.25° latitude/longitude. The reference data come from a relatively dense station network of about 600 rain gauges over Colombia. This region of South America has a very complex terrain with mountain ranges that form the northern tip of the Andes Mountains, valleys between the mountain ranges, and a vast plain that is part of the Amazon. The climate is very diverse with an extremely wet Pacific coast, a dry region in the north, and different rainfall regimes between the two extremes. The evaluated satellite rainfall products are the Tropical Rainfall Measuring Mission 3B42 and 3B42RT products, the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), the Naval Research Laboratory’s blended product (NRLB), and two versions of the Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMaP_MVK and GSMaP_MVK+). The validation and intercomparison of these products is done for the whole as well as different parts of the country. Validation results are reasonably good for daily rainfall over such complex terrain. The best results were obtained for the eastern plain, and the performance of the products was relatively poor over the Pacific coast. In comparing the different satellite products, it was seen that PERSIANN and GSMaP-MVK exhibited poor performance, with significant overestimation by PERSSIAN and serious underestimation by GSMaP-MVK. CMORPH and GSMaP-MVK+ exhibited the best performance among the products evaluated here.