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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5441289/ https://www.ncbi.nlm.nih.gov/pubmed/28534868 http://dx.doi.org/10.1038/sdata.2017.63 |
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author | 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 |
author_facet | 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 |
author_sort | Maidment, Ross I. |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-5441289 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-54412892017-06-07 A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa 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 Sci Data Data Descriptor 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. Nature Publishing Group 2017-05-23 /pmc/articles/PMC5441289/ /pubmed/28534868 http://dx.doi.org/10.1038/sdata.2017.63 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article. |
spellingShingle | Data Descriptor 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 A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa |
title | A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa |
title_full | A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa |
title_fullStr | A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa |
title_full_unstemmed | A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa |
title_short | A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa |
title_sort | new, long-term daily satellite-based rainfall dataset for operational monitoring in africa |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5441289/ https://www.ncbi.nlm.nih.gov/pubmed/28534868 http://dx.doi.org/10.1038/sdata.2017.63 |
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