Automatic Weather Station Data Tool (ADT) Installation and Training at ANACIM

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cg.contributor.affiliationInternational Research Institute for Climate and Societyen_US
cg.contributor.donorWorld Banken_US
cg.coverage.countrySenegalen_US
cg.coverage.iso3166-alpha2SNen_US
cg.coverage.regionAfricaen_US
cg.coverage.regionWestern Africaen_US
cg.creator.identifierJames Hansen: 0000-0002-8599-7895en_US
dc.contributor.authorFaniriantsoa, Rijaen_US
dc.contributor.authorHansen, Jamesen_US
dc.date.accessioned2023-01-05T19:38:45Zen_US
dc.date.available2023-01-05T19:38:45Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/126633en_US
dc.titleAutomatic Weather Station Data Tool (ADT) Installation and Training at ANACIMen_US
dcterms.abstractThe recent expansion of meteorological observation networks has focused on the use of Automatic Weather Stations (AWS). Automatic Weather Stations offer a number of advantages including automated reporting at a very fine temporal resolution (15 minutes on average). The challenge many National Meteorological Services (NMS) have been facing with the exploitation of AWS data is that different initiatives and donors have been providing different types of AWS from different vendors, leading to different AWS systems and networks. The data collected by these different AWS systems are in different formats and may sit on different computers. Although there are applications that come with each AWS network to access and visualize AWS data, access to the data is still done manually and station by station. This complicates data access, processing, and use. In addition, data from the different AWS networks is in different formats, which makes it even more difficult to analyze all the data without additional tools or applications that can convert the data into a common format and combine the data from the different networks. As a result, accessing, processing, and using these data has been a major impediment to the use of data from these varieties of AWS.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.audienceScientistsen_US
dcterms.bibliographicCitationFaniriantsoa R, Hansen J. 2022. Automatic Weather Station Data Tool (ADT) Installation and Training at ANACIM. AICCRA Workshop Report. Accelerating Impacts of CGIAR Climate Research for Africa (AICCRA).en_US
dcterms.extent12 p.en_US
dcterms.issued2022-12en_US
dcterms.languageenen_US
dcterms.licenseCC-BY-NC-4.0en_US
dcterms.publisherAccelerating Impacts of CGIAR Climate Research for Africaen_US
dcterms.subjectagricultureen_US
dcterms.subjectclimate-smart agricultureen_US
dcterms.subjectclimate changeen_US
dcterms.subjectweatheren_US
dcterms.typeReporten_US

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