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 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.
| Main Authors: | , |
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| Format: | Report biblioteca |
| Language: | English |
| Published: |
Accelerating Impacts of CGIAR Climate Research for Africa
2022-12
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| Subjects: | agriculture, climate-smart agriculture, climate change, weather, |
| Online Access: | https://hdl.handle.net/10568/126633 |
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| Summary: | 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. |
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