Details Document
LVBC Online IWRM Library

The 30 year TAMSAT African Rainfall Climatology And Time series (TARCAT) data set

8-7-14

paper

Maidment, Ross I. & David Grimes, Richard P. Allan, Elena Tarnavsky, Marc Stringer, Tim Hewison,
Rob Roebeling, and Emily Black

Otherwise the copyright prevents this. In that case check the website using the hyperlink below (if provided).

Journal of Geophysical Research: Atmospheres
RESEARCH ARTICLE 10.1002/2014JD021927

AGU Publications

data collection databases meteo remote sensing

African societies are dependent on rainfall for agricultural and other water-dependent activities, yet rainfall is extremely variable in both space and time and reoccurring water shocks, such as drought, can have considerable social and economic impacts. To help improve our knowledge of the rainfall climate, we have constructed a 30 year (1983–2012), temporally consistent rainfall data set for Africa known as TARCAT (Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series) using archived Meteosat thermal infrared imagery, calibrated against rain gauge records collated from numerous African agencies. TARCAT has been produced at 10 day (dekad) scale at a spatial resolution of 0.0375°. An intercomparison of TARCAT from 1983 to 2010 with six long-term precipitation data sets indicates that TARCAT replicates the spatial and seasonal rainfall patterns and interannual variability well, with correlation coefficients of 0.85 and 0.70 with the Climate Research Unit and Global Precipitation Climatology Centre gridded-gauge analyses respectively in the interannual variability of the Africa-wide mean monthly rainfall. The design of the algorithm for drought monitoring leads to TARCAT underestimating the Africa-wide mean annual rainfall on average by 0.37mmd1 (21%) compared to other data sets. As the TARCAT rainfall estimates are historically calibrated across large climatically homogeneous regions, the data can provide users with robust estimates of climate related risk, even in regions where gauge records are inconsistent in time.