ADT: The automatic weather station data tool
Climate data are essential in an array of climate research and applications. Climate data also provide the foundation for the provision of climate services. However, in many parts of Africa, weather stations are sparse, and their numbers have been declining over the last half-century. Moreover, the...
| Autores principales: | , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Frontiers Media
2022
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| Acceso en línea: | https://hdl.handle.net/10568/121961 |
| _version_ | 1855513617390108672 |
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| author | Faniriantsoa, Rija Dinku, Tufa |
| author_browse | Dinku, Tufa Faniriantsoa, Rija |
| author_facet | Faniriantsoa, Rija Dinku, Tufa |
| author_sort | Faniriantsoa, Rija |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Climate data are essential in an array of climate research and applications. Climate data also provide the foundation for the provision of climate services. However, in many parts of Africa, weather stations are sparse, and their numbers have been declining over the last half-century. Moreover, the distribution of existing meteorological stations is uneven, with most weather stations located in towns and cities along the main roads. To address these data gaps, efforts over the last decade, largely driven by external donor funding, have focused on expanding meteorological observation networks in many parts of Africa, mainly through the provision of Automatic Weather Stations (AWS) to National Meteorological Services (NMS). While AWS offer a number of advantages over the conventional ones, which include automated reporting at a very fine temporal resolution (15 min, on average), they also have several disadvantages and accompanying challenges to their use. Some of these well-known challenges are the high maintenance requirements and associated costs that arise from the need to procure replacement parts that may not be available locally. However, another major, under-discussed challenge confronting NMS is the disparities between the different station types provided by different donors that has given rise to barriers to pragmatically using the plethora of data collected by AWS in decision-making processes. These disparities include major differences in the way the data from various AWS types are formatted and stored, which result in poorly coordinated, fragmented, and unharmonized datasets coming from different AWS networks. The end result is that while top-of-the-line AWS networks may systematically be collecting highly needed data, the inability of NMS to efficiently, combine, synchronize, and otherwise integrate these data coherently in their databases limits their use. To address these challenges, a free web-based application called Automatic Weather Station Data Tool (ADT) with an easy-to-use graphical user interface was developed to help NMS to access, process, perform quality control, and visualize data from different AWS networks in one place. Now implemented in five African countries (Ethiopia, Ghana, Kenya, Rwanda, and Zambia), ADT also enables real-time monitoring of stations to see which ones are working and which ones are offline. This tool emerged from a wider climate services approach, the Enhancing National Climate Services (ENACTS), recognizing that availability of high-quality climate data does not automatically translate to ease of access or effective use. |
| format | Journal Article |
| id | CGSpace121961 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1219612025-12-08T10:29:22Z ADT: The automatic weather station data tool Faniriantsoa, Rija Dinku, Tufa climate data climate data tool agriculture weather climate-smart agriculture pollution Climate data are essential in an array of climate research and applications. Climate data also provide the foundation for the provision of climate services. However, in many parts of Africa, weather stations are sparse, and their numbers have been declining over the last half-century. Moreover, the distribution of existing meteorological stations is uneven, with most weather stations located in towns and cities along the main roads. To address these data gaps, efforts over the last decade, largely driven by external donor funding, have focused on expanding meteorological observation networks in many parts of Africa, mainly through the provision of Automatic Weather Stations (AWS) to National Meteorological Services (NMS). While AWS offer a number of advantages over the conventional ones, which include automated reporting at a very fine temporal resolution (15 min, on average), they also have several disadvantages and accompanying challenges to their use. Some of these well-known challenges are the high maintenance requirements and associated costs that arise from the need to procure replacement parts that may not be available locally. However, another major, under-discussed challenge confronting NMS is the disparities between the different station types provided by different donors that has given rise to barriers to pragmatically using the plethora of data collected by AWS in decision-making processes. These disparities include major differences in the way the data from various AWS types are formatted and stored, which result in poorly coordinated, fragmented, and unharmonized datasets coming from different AWS networks. The end result is that while top-of-the-line AWS networks may systematically be collecting highly needed data, the inability of NMS to efficiently, combine, synchronize, and otherwise integrate these data coherently in their databases limits their use. To address these challenges, a free web-based application called Automatic Weather Station Data Tool (ADT) with an easy-to-use graphical user interface was developed to help NMS to access, process, perform quality control, and visualize data from different AWS networks in one place. Now implemented in five African countries (Ethiopia, Ghana, Kenya, Rwanda, and Zambia), ADT also enables real-time monitoring of stations to see which ones are working and which ones are offline. This tool emerged from a wider climate services approach, the Enhancing National Climate Services (ENACTS), recognizing that availability of high-quality climate data does not automatically translate to ease of access or effective use. 2022-08-30 2022-09-29T14:16:54Z 2022-09-29T14:16:54Z Journal Article https://hdl.handle.net/10568/121961 en Open Access Frontiers Media Faniriantsoa R, Dinku T. 2022. ADT: The automatic weather station data tool. Frontiers in Climate 4:933543. |
| spellingShingle | climate data climate data tool agriculture weather climate-smart agriculture pollution Faniriantsoa, Rija Dinku, Tufa ADT: The automatic weather station data tool |
| title | ADT: The automatic weather station data tool |
| title_full | ADT: The automatic weather station data tool |
| title_fullStr | ADT: The automatic weather station data tool |
| title_full_unstemmed | ADT: The automatic weather station data tool |
| title_short | ADT: The automatic weather station data tool |
| title_sort | adt the automatic weather station data tool |
| topic | climate data climate data tool agriculture weather climate-smart agriculture pollution |
| url | https://hdl.handle.net/10568/121961 |
| work_keys_str_mv | AT faniriantsoarija adttheautomaticweatherstationdatatool AT dinkutufa adttheautomaticweatherstationdatatool |