Get data via: https://www.tamsat.org.uk/
RESTRICTED only accessable in CEN/MPI network or via CliSAP login What does that mean?
TAMSAT is the acronym for Tropical Application of Meteorology Using Satellite Data and Ground-Based Observations which is a rainfall estimation and quality assessment system providing high-resolution (~4 km) , 10-daily (or monthly) pan-African rainfall estimates.
The TAMSAT system is based on two cardinal data sets: 1) Rainfall estimates based on time-lapse analysis of the cloud-top temperature distribution and development observed every 30 minutes (every 15 minutes since July 2006) by thermal infrared (TIR) imagery from aboard the Meteosat satellites of the first and second generation [Grimes et al., 1999; Maidment et al., 2014, 2017, see References]. This retrieval is based on the assumption of a positive linear relationship between the life-time of convective clouds and the amount of rainfall at the surface; it works best (if not exclusively) for convective precipitation. 2) a thoroughly quality-assessed rain gauge data archive for the calibration of the TAMSAT rainfall estimation algorithm spanning years 1983-2012; in addition to that near-real-time rain gauge data available since 2011 are used for operational validation of the TAMSAT rainfall estimates.
Last update of data set at ICDC: January 25 2019.
Period and temporal resolution:
Missing months: 1983-01 & -11; 1984-11; 1985-02, -03, & -11;1986-08; 1988-11 & -12; 1989-01, -02, -05 & -09; 1990-01 & -02; 1992-02; 1993-05; 1996-10; 1999-01; 2006-09; main reason for these missing months usually is a missing 10-day period.
Coverage and spatial resolution:
The data set does not include any explicit uncertainty estimates.
A number of issues need to be kept in mind to understand potential uncertainties.
1) The TAMSAT algorithm works best for convective precipitation. Version 3.0 has reduced tendency to underestimate precipitation amount compared to version 2.0. Orographically induced precipitation from comparably warm clouds remains problematic in general and is under-estimated when rooting the retrieval on infrared observations solely, as is the case for this data set.
2) The (changing) spatial distribution of rain gauges impacts the calibration of the TAMSAT rainfall estimates - particularly when extending the estimates over the entire continent. This also impacts the near-real-time validation.
3) The (changing) temporal distribution of rain gauges does also impact both the calibration and extension as well as the validation.
4) The calibration requires to use regions of different extent and with different rain gauge densities because of the various climatic zones encountered across Africa; this may lead to spatial inconsistencies. Compared to version 2.0, version 3.0 has much less inconsistencies due to gaps in the calibration and validation data.
5) A relatively dense network of rain gauges in some areas does not guarantee a large enough number of data for the near-real-time validation; often only 20-25% of the stations report in due time.
More information about data quality can be found in the references - particularly the two from 2014. In addition the TAMSAT team regularly issues validation reports which can be found on www.met.reading.ac.uk/~tamsat
TAMSAT Research Group
When using this data set please cite the following two papers:
Maidment, R., et al., 2014, The 30 year TAMSAT African Rainfall Climatology And Time series (TARCAT) data set. J. Geophys. Res., 119, 10,619-10,644, doi:10.1002/2014JD021927
Tarnavsky, E., et al., 2014, Extension of the TAMSAT Satellite-Based Rainfall Monitoring over Africa and from 1983 to present. J. Appl. Meteorol. Climatol., 53, 2805-2822, doi:10.1175/JAMC-D-14-0016.1