An Assessment of Satellite-Derived Rainfall Products Relative to Ground Observations over East Africa

Margaret Kimani,Joost Hoedjes,Zhongbo Su

doi:10.3390/rs9050430

Abstract

Accurate and consistent rainfall observations are vital for climatological studies in support of better agricultural and water management decision-making and planning. In East Africa, accurate rainfall estimation with an adequate spatial distribution is limited due to sparse rain gauge networks. Satellite rainfall products can potentially play a role in increasing the spatial coverage of rainfall estimates; however, their performance needs to be understood across space–time scales and factors relating to their errors. This study assesses the performance of seven satellite products: Tropical Applications of Meteorology using Satellite and ground-based observations (TAMSAT), African Rainfall Climatology And Time series (TARCAT), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Tropical Rainfall Measuring Mission (TRMM-3B43), Climate Prediction Centre (CPC) Morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), CPC Merged Analysis of Precipitation (CMAP), and Global Precipitation Climatology Project (GPCP), using locally developed gridded (0.05°) rainfall data for 15 years (1998–2012) over East Africa. The products’ assessments were done at monthly and yearly timescales and were remapped to the gridded rain gauge data spatial scale during the March to May (MAM) and October to December (OND) rainy seasons. A grid-based statistical comparison between the two datasets was used, but only pixel values located at the rainfall stations were considered for validation. Additionally, the impact of topography on the performance of the products was assessed by analyzing the pixels in areas of highest negative bias. All the products could substantially replicate rainfall patterns, but their differences are mainly based on retrieving high rainfall amounts, especially of localized orographic types. The products exhibited systematic errors, which decreased with an increase in temporal resolution from a monthly to yearly scale. Challenges in retrieving orographic rainfall, especially during the OND season, were identified as the main cause of high underestimations. Underestimation was observed when elevation was <2500 m and above this threshold; overestimation was evident in mountainous areas. CMORPH, CHIRPS, and TRMM showed consistently high performance during both seasons, and this was attributed to their ability to retrieve rainfall of different rainfall regimes.

Highlights

Accurate rainfall measurements are very important for many applications, such as hydrological modelling, agricultural practices, and climate studies
Validation was accomplished by considering point-to-point comparisons of the extracted satellite-derived rainfall estimates and corresponding gridded rain gauge data from the 284 rainfall stations distributed over the region
The products were converted to monthly spatial scales and resampled using nearest neighbor resampling to the spatial scale (0.05◦ ) of the rain gauge data

Summary

Introduction

Accurate rainfall measurements are very important for many applications, such as hydrological modelling, agricultural practices, and climate studies. Satellite-derived rainfall products may complement the sparse rain gauge data as they have an advantage of wide and consistent coverage [1,2,3]. Some products combine IR- and MW-based estimates, taking advantage of the high temporal resolution of IR platforms and the better accuracy in rainfall estimation of MW sensors. Most of these satellite-derived rainfall products have been validated globally and regionally [4,5] but there are still large discrepancies with ground-based observations at the sub-regional level, where these data are applied [6]. Maggioni et al [9], who studied the uncertainties of high-resolution satellite rainfall products, found that the systematic and random errors presented in these products were responsible for the inaccuracies in the retrieval of high rainfall rates

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