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...
Gespeichert in:
Veröffentlicht in: | Scientific data 2017-05, Vol.4 (1), p.170063-170063, Article 170063 |
---|---|
Hauptverfasser: | , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 170063 |
---|---|
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 |
format | Article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5441289</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1901762906</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4353-90895dc35b20993d159b6e7717f4acca5d344d8d29323a302decec382a2334c3</originalsourceid><addsrcrecordid>eNptkc2LFDEQxYMo7rLu0asEvHiwxySVdHcuwrD4BQte9iiEmiQ9ZulOxiTjsv-9GWddRvGUgvzqVdV7hLzkbMUZjO-Kw4orwfiw6uEJORdMiU7KHp6e1GfkspRbxhgHydTAnpMzMSqQYz-ek29rGv3dWzqnuO2qzwt1GOZ7WrD6eQ7Vdxss3tGMIU44z_QwsfhKp5Rp2vmMNaSIM11SDDXlELc0RLqecrD4gjxrPcVfPrwX5Objh5urz931109frtbXnZWgoNNs1MpZUBvBtAbHld70fhj4MEm0FpUDKd3ohAYBCEw4b72FUaAAkBYuyPuj7G6_WbyzPtaMs9nlsGC-NwmD-fsnhu9mm34aJSUXo24Cbx4Ecvqx96WaJRTb7sfo074YrpvDvdCsb-jrf9DbtM_NgCMFfIBeNqo7UjanUrKfHpfhzBySM7-TM4fkTA-Nf3V6wSP9J6cGrI5A2R0s9vlk7H8VfwE32aSF</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1901317364</pqid></control><display><type>article</type><title>A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa</title><source>Nature Free</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central</source><source>PubMed Central Open Access</source><source>Springer Nature OA Free Journals</source><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</creator><creatorcontrib>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</creatorcontrib><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)</description><identifier>ISSN: 2052-4463</identifier><identifier>EISSN: 2052-4463</identifier><identifier>DOI: 10.1038/sdata.2017.63</identifier><identifier>PMID: 28534868</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>704/106 ; 704/242 ; 704/4111 ; Data Descriptor ; Humanities and Social Sciences ; multidisciplinary ; Science</subject><ispartof>Scientific data, 2017-05, Vol.4 (1), p.170063-170063, Article 170063</ispartof><rights>The Author(s) 2017</rights><rights>Copyright Nature Publishing Group May 2017</rights><rights>Copyright © 2017, The Author(s) 2017 The Author(s)</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4353-90895dc35b20993d159b6e7717f4acca5d344d8d29323a302decec382a2334c3</citedby><cites>FETCH-LOGICAL-c4353-90895dc35b20993d159b6e7717f4acca5d344d8d29323a302decec382a2334c3</cites><orcidid>0000-0002-9215-5397</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5441289/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5441289/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27903,27904,41099,42168,51554,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28534868$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Maidment, Ross I</creatorcontrib><creatorcontrib>Grimes, David</creatorcontrib><creatorcontrib>Black, Emily</creatorcontrib><creatorcontrib>Tarnavsky, Elena</creatorcontrib><creatorcontrib>Young, Matthew</creatorcontrib><creatorcontrib>Greatrex, Helen</creatorcontrib><creatorcontrib>Allan, Richard P</creatorcontrib><creatorcontrib>Stein, Thorwald</creatorcontrib><creatorcontrib>Nkonde, Edson</creatorcontrib><creatorcontrib>Senkunda, Samuel</creatorcontrib><creatorcontrib>Alcántara, Edgar Misael Uribe</creatorcontrib><title>A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa</title><title>Scientific data</title><addtitle>Sci Data</addtitle><addtitle>Sci Data</addtitle><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)</description><subject>704/106</subject><subject>704/242</subject><subject>704/4111</subject><subject>Data Descriptor</subject><subject>Humanities and Social Sciences</subject><subject>multidisciplinary</subject><subject>Science</subject><issn>2052-4463</issn><issn>2052-4463</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>BENPR</sourceid><recordid>eNptkc2LFDEQxYMo7rLu0asEvHiwxySVdHcuwrD4BQte9iiEmiQ9ZulOxiTjsv-9GWddRvGUgvzqVdV7hLzkbMUZjO-Kw4orwfiw6uEJORdMiU7KHp6e1GfkspRbxhgHydTAnpMzMSqQYz-ek29rGv3dWzqnuO2qzwt1GOZ7WrD6eQ7Vdxss3tGMIU44z_QwsfhKp5Rp2vmMNaSIM11SDDXlELc0RLqecrD4gjxrPcVfPrwX5Objh5urz931109frtbXnZWgoNNs1MpZUBvBtAbHld70fhj4MEm0FpUDKd3ohAYBCEw4b72FUaAAkBYuyPuj7G6_WbyzPtaMs9nlsGC-NwmD-fsnhu9mm34aJSUXo24Cbx4Ecvqx96WaJRTb7sfo074YrpvDvdCsb-jrf9DbtM_NgCMFfIBeNqo7UjanUrKfHpfhzBySM7-TM4fkTA-Nf3V6wSP9J6cGrI5A2R0s9vlk7H8VfwE32aSF</recordid><startdate>20170523</startdate><enddate>20170523</enddate><creator>Maidment, Ross I</creator><creator>Grimes, David</creator><creator>Black, Emily</creator><creator>Tarnavsky, Elena</creator><creator>Young, Matthew</creator><creator>Greatrex, Helen</creator><creator>Allan, Richard P</creator><creator>Stein, Thorwald</creator><creator>Nkonde, Edson</creator><creator>Senkunda, Samuel</creator><creator>Alcántara, Edgar Misael Uribe</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><scope>C6C</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-9215-5397</orcidid></search><sort><creationdate>20170523</creationdate><title>A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa</title><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></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4353-90895dc35b20993d159b6e7717f4acca5d344d8d29323a302decec382a2334c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>704/106</topic><topic>704/242</topic><topic>704/4111</topic><topic>Data Descriptor</topic><topic>Humanities and Social Sciences</topic><topic>multidisciplinary</topic><topic>Science</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Maidment, Ross I</creatorcontrib><creatorcontrib>Grimes, David</creatorcontrib><creatorcontrib>Black, Emily</creatorcontrib><creatorcontrib>Tarnavsky, Elena</creatorcontrib><creatorcontrib>Young, Matthew</creatorcontrib><creatorcontrib>Greatrex, Helen</creatorcontrib><creatorcontrib>Allan, Richard P</creatorcontrib><creatorcontrib>Stein, Thorwald</creatorcontrib><creatorcontrib>Nkonde, Edson</creatorcontrib><creatorcontrib>Senkunda, Samuel</creatorcontrib><creatorcontrib>Alcántara, Edgar Misael Uribe</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Scientific data</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Maidment, Ross I</au><au>Grimes, David</au><au>Black, Emily</au><au>Tarnavsky, Elena</au><au>Young, Matthew</au><au>Greatrex, Helen</au><au>Allan, Richard P</au><au>Stein, Thorwald</au><au>Nkonde, Edson</au><au>Senkunda, Samuel</au><au>Alcántara, Edgar Misael Uribe</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa</atitle><jtitle>Scientific data</jtitle><stitle>Sci Data</stitle><addtitle>Sci Data</addtitle><date>2017-05-23</date><risdate>2017</risdate><volume>4</volume><issue>1</issue><spage>170063</spage><epage>170063</epage><pages>170063-170063</pages><artnum>170063</artnum><issn>2052-4463</issn><eissn>2052-4463</eissn><abstract>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)</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>28534868</pmid><doi>10.1038/sdata.2017.63</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-9215-5397</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2052-4463 |
ispartof | Scientific data, 2017-05, Vol.4 (1), p.170063-170063, Article 170063 |
issn | 2052-4463 2052-4463 |
language | eng |
recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5441289 |
source | Nature Free; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; PubMed Central Open Access; Springer Nature OA Free Journals |
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 |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T04%3A28%3A30IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20new,%20long-term%20daily%20satellite-based%20rainfall%20dataset%20for%20operational%20monitoring%20in%20Africa&rft.jtitle=Scientific%20data&rft.au=Maidment,%20Ross%20I&rft.date=2017-05-23&rft.volume=4&rft.issue=1&rft.spage=170063&rft.epage=170063&rft.pages=170063-170063&rft.artnum=170063&rft.issn=2052-4463&rft.eissn=2052-4463&rft_id=info:doi/10.1038/sdata.2017.63&rft_dat=%3Cproquest_pubme%3E1901762906%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1901317364&rft_id=info:pmid/28534868&rfr_iscdi=true |