Automatic Weather Station Data Tool (ADT) Installation and Training at ANACIM
The 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 Meteor...
| Autores principales: | , |
|---|---|
| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
Accelerating Impacts of CGIAR Climate Research for Africa
2022
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/126633 |
| _version_ | 1855530887629766656 |
|---|---|
| author | Faniriantsoa, Rija Hansen, James |
| author_browse | Faniriantsoa, Rija Hansen, James |
| author_facet | Faniriantsoa, Rija Hansen, James |
| author_sort | Faniriantsoa, Rija |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The 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. |
| format | Informe técnico |
| id | CGSpace126633 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Accelerating Impacts of CGIAR Climate Research for Africa |
| publisherStr | Accelerating Impacts of CGIAR Climate Research for Africa |
| record_format | dspace |
| spelling | CGSpace1266332025-11-11T16:27:35Z Automatic Weather Station Data Tool (ADT) Installation and Training at ANACIM Faniriantsoa, Rija Hansen, James agriculture climate-smart agriculture climate change weather The 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. 2022-12 2023-01-05T19:38:45Z 2023-01-05T19:38:45Z Report https://hdl.handle.net/10568/126633 en Open Access application/pdf Accelerating Impacts of CGIAR Climate Research for Africa Faniriantsoa 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). |
| spellingShingle | agriculture climate-smart agriculture climate change weather Faniriantsoa, Rija Hansen, James Automatic Weather Station Data Tool (ADT) Installation and Training at ANACIM |
| title | Automatic Weather Station Data Tool (ADT) Installation and Training at ANACIM |
| title_full | Automatic Weather Station Data Tool (ADT) Installation and Training at ANACIM |
| title_fullStr | Automatic Weather Station Data Tool (ADT) Installation and Training at ANACIM |
| title_full_unstemmed | Automatic Weather Station Data Tool (ADT) Installation and Training at ANACIM |
| title_short | Automatic Weather Station Data Tool (ADT) Installation and Training at ANACIM |
| title_sort | automatic weather station data tool adt installation and training at anacim |
| topic | agriculture climate-smart agriculture climate change weather |
| url | https://hdl.handle.net/10568/126633 |
| work_keys_str_mv | AT faniriantsoarija automaticweatherstationdatatooladtinstallationandtrainingatanacim AT hansenjames automaticweatherstationdatatooladtinstallationandtrainingatanacim |