article · Quarterly Journal of the Royal Meteorological Society
An evaluation of seven satellite-derived gridded rainfall products across Central Africa compares their performance against three ground-based observational datasets. The assessed tools, including ARC, CHIRPS, CMORPH, PERSIANN, TAPEER, TARCAT, and TMPA, generally capture regional mean rainfall regimes and spatial patterns of annual precipitation, though discrepancies exist along the east-to-west gradient. Performance varies significantly by timescale and location. For daily rainfall amounts, TMPA demonstrates the highest accuracy, followed by CMORPH, whilst TARCAT performs well over central Democratic Republic of the Congo. At interannual timescales, CHIRPS and TMPA show the strongest agreement, alongside PERSIANN when verified against fully independent stations. CMORPH exhibits a systematic positive bias, and capturing dry season variability remains difficult. Users must exercise caution when applying these datasets in data-sparse regions, particularly for evaluating rainfall trends.
Accurate rainfall data are essential for managing water resources, agriculture, and climate risks across Central Africa. Because ground monitoring networks are sparse across large areas, satellite-derived rainfall datasets offer a crucial alternative. Understanding the specific strengths, biases, and regional limitations of these datasets helps hydrologists, forecasters, and planners select the most dependable tools for local and regional climate assessments.
The abstract does not indicate a direct commercialisation pathway. However, the findings provide applied benchmarking for meteorological services, agricultural platforms, and water resource developers seeking reliable precipitation inputs. Sitting at an applied evaluation stage, the evidence directs technical practitioners and geospatial analytics providers to the most suitable off-the-shelf satellite rainfall products for daily or multi-year monitoring across Central Africa.
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Abstract An intercomparison of seven gridded rainfall products incorporating satellite data (ARC, CHIRPS, CMORPH, PERSIANN, TAPEER, TARCAT, TMPA) is carried out over Central Africa, by evaluating them against three observed datasets: (a) the WaTFor database, consisting of 293 (monthly records) and 154 (daily records) rain‐gauge stations collected from global datasets, national meteorological services and monitoring projects, (b) the WorldClim v2 gridded database, and (c) a set of stations expanded from the FAOCLIM network, these two latter sets describing climate normals. All products fairly well reproduce the mean rainfall regimes and the spatial patterns of mean annual rainfall, although with some discrepancies in the east–west gradient. A systematic positive bias is found in the CMORPH product. Despite its lower spatial resolution, TAPEER shows reasonable skills. When considering daily rainfall amounts, TMPA shows best skills, followed by CMORPH, but over the central part of the Democratic Republic of the Congo, TARCAT is amongst the best products. Skills ranking is however different at the interannual time‐scale, with CHIRPS and TMPA performing best, though PERSIANN has comparable skills when only fully independent stations are used as reference. A preliminary study of Southern Hemisphere dry season variability, from the example of Kinshasa, shows that it is a difficult variable to capture with satellite‐based rainfall products. Users should still be careful when using any product in the most data‐sparse regions, especially for trend assessment.
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DOI: 10.1002/qj.3547
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