Validation of the CHIRPS satellite rainfall estimates over eastern Africa

Abstract

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.

Evaluation of remotely sensed rainfall products over Central Africa

“…More generally TARCAT is amongst the best products in terms of rain event detection. As emphasized by Dinku et al () for East Africa, this may be explained by the fact that TARCAT uses local calibration to select cold‐cloud duration thresholds. At the interannual time‐scale, CHIRPS and TMPA perform best, although when compared against fully independent stations (southern Congo‐Brazzaville) PERSIANN has comparable skills. TARCAT has the lowest correlations with observations.…”

“…Several studies focused on a single African subregion or country, e.g. Uganda (Maidment et al , ; Diem et al , ; 2019), the Zambezi Basin (Cohen Liechti et al , ), West Africa (Gosset et al , ; ), Angola (Pombo et al , ), East Africa (Dinku et al , ), or the Ethiopian Rift Valley (Tesfamariam et al , ), among others.…”

East Africa Rainfall Trends and Variability 1983–2015 Using Three Long-Term Satellite Products

“…Moreover, CHIRPS includes passive microwave estimates through the direct use of the monthly precipitation climatology CPHclim and in the CCD calibration phase [29]. It must also be mentioned that the next studies will benefit from the recently released version 3 of the TARCAT dataset, whose first results have been described in the literature [27,33]. Whilst maintaining its peculiarity concerning the exclusive use of a historical rain gauge dataset, several aspects regarding the calibration process were updated in this new version of TARCAT with the intent to fix known problems, i.e., the dry bias and the unrealistic spatial artefacts that originated from the use of rectangular calibration zones.…”

“…These aspects can introduce unwanted perturbations. In particular, the exploitation of simultaneous rain gauge measurements within the retrieval algorithms to be merged with the satellite estimates as in ARC2 and CHIRPS can insert inhomogeneities [33]. TARCAT makes use only of an historical rain gauge dataset for the calibration of IR brightness temperatures, and this mitigates the possible effects of the variations of rain gauge network density over time.…”

Spatial distribution and temporal trends of rainfall and erosivity in the Eastern Africa region

“…CHIRPS dataset was chosen because of the long‐term (1981 to present) and public domain data availability and the relatively high spatial and temporal resolutions, whereas the majority of the available global rainfall datasets have coarser resolutions (Table ). Moreover, Dinku () validated the CHIRPS dataset using reference rain gauge measurements from Ethiopia, Kenya, and Tanzania and reported that CHIRPS performed better than African Rainfall Climatology and Tropical Applications of Meteorology using Satellite data rainfall products over most of the Eastern Africa region.…”