Assessing the skill of gridded satellite and reanalysis precipitation products over in East and Southern Africa

Abstract

Validation of gridded precipitation products (GPP’s) increase confidence of the users and highlights possible improvements of the algorithms to handle complex rainfall forming processes. We evaluated the skill of three gridded precipitation products (GPP’s) in estimating the gauge observations and compared the precipitation trends derived from these products across the east and southern Africa region. Taylor diagrams and Kling-Gupta Efficiency (KGE) was used to assess the accuracy. A modified Mann-Kendal test and the Sen’s slope estimator were utilized to determine the significance and the magnitude of the trends respectively. The three GPP’s had varied performance over temporal and altitudinal ranges. The skill of the three GPP’s at monthly scale, was generally high but showed lower performance at elevations over 1500 m especially during the OND season. The three GPP’s performed equally well between 1001 – 1500 m elevation range. CHELSA-v2.1 was most accurate at 0-500m but had the lowest skill at 501 – 1000 m and above 1500 m elevations that caused over-estimation of the annual and seasonal precipitation trends over mountainous terrain and over large inland waterbodies. The quantified precipitation trends revealed high spatial-temporal variability. Generally, the skill and precipitation trends derived from CHIRPS-v2 and TC data showed substantial convergence except in Tanzania. Our results emphasize the importance of validation of climate datasets to avoid error propagation in different models and applications. Our results demonstrates that new or higher resolution precipitation data is not always the most accurate since update of the algorithms can introduce artifacts or biases.

Performance‐based evaluation of NMME and C3S models in forecasting the June–August Central African rainfall under the influence of the South Atlantic Ocean Dipole

“…In our findings, regions, where models demonstrate higher skill, are generally those with less complex terrain, such as the relatively homogeneous Gabon and Chad regions. Generally, models exhibit poorer skill in areas characterized by a wide range in elevation (as noted in studies by Muthoni et al (2023) and Stacey et al (2023)). To enhance model skill, it is essential to ensure that the high mountains and extensive coastlines of CA are wellrepresented in the models to capture the intricate precipitation effects.…”

Observations of enhanced rainfall variability in Kenya, East Africa

“…Others have suggested that CHIRPS might overestimate rainfall in the nearby Kisumu region along the Western shores of Lake Victoria 82 . In addition, 65 suggested that CHIRPS had a slight dry bias, although in general performing well. In other regions of the world, CHIRPS has been found to struggle to capture extreme rainfall 83 .…”