4.6 Article

Evaluation of remotely sensed rainfall products over Central Africa

Journal

QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
Volume 145, Issue 722, Pages 2115-2138

Publisher

WILEY
DOI: 10.1002/qj.3547

Keywords

Precipitation; Remote sensing; Congo Basin; DRC; Central African Republic; Cameroon; Gabon

Funding

  1. BELSPO [SR/00/305]
  2. F.R.S.-FNRS PhD scholarship
  3. French Fund for the Global Environment (FFEM) [CZZ1636.01D]
  4. French Centre National d'Etudes Spatiales (CNES)

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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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