Rainfall Estimates for Africa from TAMSAT
- Coverage, spatial and temporal resolution
- Data quality
- Contact person
- Data citation
RESTRICTED:This link to the data set is only available for a restricted user group. The data set is only accessible in CEN/MPI net or accessible from external nets with a customer account. Please contact ICDC if you would like to access this data from outside the network.
- View TAMSAT data at LAS
- Access TAMSAT data via HTTP/OPeNDAP
- Data access via file system: /data/icdc/atmosphere/tamsat_african_rainfall
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:
- 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, 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.
- 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.
What is new compared to the previous version 3.0?
- Data gaps are filled via interpolation [i.e. now the time series are complete];
- Minimum cloud top temperature is -65°C instead of -60°C;
- Usage of additional rain gauges for the calibration during cold cloud duration.
Last update of data set at ICDC: August 9, 2023.
|Rainfall anomaly relative to 1983-2012||mm|
Period and temporal resolution:
- 1983-01 to 2023-07
- Monthly and 10-daily
Coverage and spatial resolution:
- Africa, over land
- Spatial resolution: 0.0375° x 0.0375°, cartesian grid
- Geographic longitude: -19.0125°E to 51.975°E
- Geographic latitude: -35.9625°N to 38.025°N
- Dimension: 1974 rows x 1894 columns
- Altitude: following terrain
The data set does not include any explicit uncertainty estimates. A number of issues need to be kept in mind to understand potential uncertainties:
- 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.
- 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.
- The (changing) temporal distribution of rain gauges does also impact both the calibration and extension as well as the validation.
- 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.
- 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 the TAMSAT web page.
- January and November 1983;
- November 1984;
- February, March, and November 1985;
- August 1986;
- November and December 1988;
- January, February, May and September 1989;
- January, and February 1990;
- February 1992;
- May 1993;
- October 1996;
- January 1999;
- September 2006;
The main reason for these missing months is usually a missing 10-day period. However, beginning with version 3.1 an additional data layer is added in which these data gaps are filled.
TAMSAT Research Group
Meteorology Department, University of Reading, UK
email: tamsat (at) reading.ac.uk
ICDC / CEN / Universität Hamburg
email: stefan.kern (at) uni-hamburg.de
- TAMSAT web page https://www.tamsat.org.uk/
- Technical report for version 3.0
- Maidment, R., et al., 2017, A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa Nature Scientific Data 4: 170063 DOI:10.1038/sdata.2017.63. https://doi.org/10.1038/sdata.2017.63
- Kimani, M.W., et al., 2017, An assessment of satellite-derived rainfall products relative to ground observations over East Africa, Remote Sensing, 9, 430, doi:10.3390/rs9050430. https://doi.org/10.3390/rs9050430
- 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 https://doi.org/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 https://doi.org/10.1175/JAMC-D-14-0016.1
- Grimes, D.I.F., et al., 1999, Optimal Areal Rainfall Estimation Using Rain gauges and Satellite Data. Journal of Hydrology, 222, 93-108. https://doi.org/10.1016/S0022-1694(99)00092-X
- Milford, J.R., and G. Dugdale, 1990, Estimation of Rainfall Using Geostationary Satellite Data. Applications of Remote Sensing in Agriculture, Butterworth, London Proceedings of the 48th Easter School in Agricultural Science, University of Nottingham. April 1989. https://doi.org/10.1016/B978-0-408-04767-8.50010-4
- Behrangi, A., and Y. Wen, 2017, On the spatial and temporal sampling errors of remotely sensed precipitation products, Remote Sensing, 9, 1127, doi:10.3390/rs9111127. https://doi.org/10.3390/rs9111127
Please cite the data as follows:
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
original data source: https://www.tamsat.org.uk/data/ [last accessed: August 4, 2023].
and the following acknowledgments:
Thanks to ICDC, CEN, University of Hamburg for data support.