A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa

Ross I. Maidment, David Grimes, Emily Black, Elena Tarnavsky, Matthew Young, Helen Greatrex, Richard P. Allan, Thorwald Stein, Edson Nkonde, Samuel Senkunda, Edgar Misael Uribe Alcántara

Research output: Contribution to journalArticlepeer-review

183 Scopus citations


Rainfall information is essential for many applications in developing countries, and yet, continually updated information at fine temporal and spatial scales is lacking. In Africa, rainfall monitoring is particularly important given the close relationship between climate and livelihoods. To address this information gap, this paper describes two versions (v2.0 and v3.0) of the TAMSAT daily rainfall dataset based on high-resolution thermal-infrared observations, available from 1983 to the present. The datasets are based on the disaggregation of 10-day (v2.0) and 5-day (v3.0) total TAMSAT rainfall estimates to a daily time-step using daily cold cloud duration. This approach provides temporally consistent historic and near-real time daily rainfall information for all of Africa. The estimates have been evaluated using ground-based observations from five countries with contrasting rainfall climates (Mozambique, Niger, Nigeria, Uganda, and Zambia) and compared to other satellite-based rainfall estimates. The results indicate that both versions of the TAMSAT daily estimates reliably detects rainy days, but have less skill in capturing rainfall amount - results that are comparable to the other datasets.

Original languageEnglish (US)
Article number170063
JournalScientific Data
StatePublished - May 23 2017

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences


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