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

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...

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Veröffentlicht in:Scientific data 2017-05, Vol.4 (1), p.170063-170063, Article 170063
Hauptverfasser: Maidment, Ross I, Grimes, David, Black, Emily, Tarnavsky, Elena, Young, Matthew, Greatrex, Helen, Allan, Richard P, Stein, Thorwald, Nkonde, Edson, Senkunda, Samuel, Alcántara, Edgar Misael Uribe
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container_issue 1
container_start_page 170063
container_title Scientific data
container_volume 4
creator Maidment, Ross I
Grimes, David
Black, Emily
Tarnavsky, Elena
Young, Matthew
Greatrex, Helen
Allan, Richard P
Stein, Thorwald
Nkonde, Edson
Senkunda, Samuel
Alcántara, Edgar Misael Uribe
description 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. Design Type(s) observation design • data integration objective Measurement Type(s) hydrological precipitation process Technology Type(s) meterological observation Factor Type(s) Sample Characteristic(s) Mozambique • Niger • Nigeria • Uganda • Zambia Machine-accessible metadata file describing the reported data (ISA-Tab format)
doi_str_mv 10.1038/sdata.2017.63
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subjects 704/106
704/242
704/4111
Data Descriptor
Humanities and Social Sciences
multidisciplinary
Science
title A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa
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