Reference : Evaluation of remotely sensed rainfall products over Central Africa
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography
Evaluation of remotely sensed rainfall products over Central Africa
Camberlin, Pierre mailto [> >]
Barraud, Geoffrey [> >]
Bigot, Sylvain [> >]
Dewitte, Olivier [> >]
Makanzu Imwangana, Fils [> >]
Maki Mateso, Jean-Claude [> >]
Martiny, Nadège [> >]
Monsieurs, Elise [Université de Liège - ULiège > Département de géographie > Unité de géographie physique et quaternaire (UGPQ) >]
Moron, Vincent [> >]
Pellarin, Thierry [> >]
Philippon, Nathalie [> >]
Muhindo, Sahani mailto [> >]
Samba, Gaston [> >]
Quarterly Journal of the Royal Meteorological Society
Yes (verified by ORBi)
United States
[en] An intercomparison of seven gridded rainfall products incorporating satellite data
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 TMPAperforming 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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