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TAMSAT Rainfall Estimates for Africa

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Data access via file system: /data/icdc/atmosphere/tamsat_african_rainfall

 

Description

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.

We refer to the TAMSAT web page and the references for more information. We offer version 3 of this data set.

Last update of data set at ICDC: January 25 2019.

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Parameters

Name Units
Rainfall estimate mm
   
Rainfall anomaly relative to 1983-2012 mm
   

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Coverage, spatial and temporal resolution

Period and temporal resolution:

  • 1983-01 to 2018-12
  • Monthly and 10-Daily

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:

  • 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

Format:

  • netCDF

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Data quality

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

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Contact

TAMSAT Research Group
Meteorology Department, University of Reading, UK
email: tamsat (at) reading.ac.uk

Stefan Kern
ICDC / CEN / University of Hamburg
email: stefan.kern (at) uni-hamburg.de

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References

  • 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.
  • 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.
  • 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
  • Grimes, D.I.F., et al., 1999, Optimal Areal Rainfall Estimation Using Rain gauges and Satellite Data. Journal of Hydrology, 222, 93-108.
  • 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.
  • 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.

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Data citation

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

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