The automatic weather station provides real-time climate data for the Kikuletwa catchment in Kilimanjaro, Tanzania. Data is accessible online and updated every 10 seconds through the EMS Brno website at http://www.emsbrno.cz/p.axd/en/Kikuletwa.Kilimanjaro.NM_t_AIST.html.
The Carnegie funding through Future Africa at the University of Pretoria attracted more funding to the project, where I acquired two more automatic weather stations. The automatic weather stations were installed through a coordinated effort by the local government, farmers, Pangani basin authority and agricultural extension officers.
Noteworthy achievements
Real-Time Data Access
The AWS provides data every 10 seconds, ensuring continuous and up-to-date monitoring of weather conditions. Data can be accessed online through the EMS Brno website, allowing remote monitoring and analysis from any location.
Enhanced Climate Monitoring
The AWS delivers precise measurements of various climate parameters, including temperature, humidity, wind speed, solar radiation, and rainfall. The strategic placement of the AWS at multiple sites enhances spatial coverage, providing a comprehensive view of different climate patterns.
Support for Research and Decision-Making
The high-frequency data supports advanced climate research, enabling detailed trend analysis and the study of microclimates. I am drafting a paper using the data “Prediction of Maize Evapotranspiration of the semi-arid region of Northern Tanzania Using Machine Learning.”
The AWS data helps in early detection and response to extreme weather events, improving disaster preparedness and resilience. The weather stations will be implemented on our developed website.
Educational and Training Opportunities
The AWS is a practical tool for students and researchers at NM-AIST, providing hands-on experience with meteorological instruments and data analysis.
Collaboration and Networking
The project has strengthened collaborations with local government, water basin authority and local community, fostering knowledge exchange and resource sharing. The AWS installation has facilitated engagement with local communities, NGOs, and government agencies, promoting a collaborative approach to climate monitoring.
The project aimed to develop a remote sensing and machine learning system for drought detection in the Pangani basin. We have developed the Smart Climate Advisor website (http://www.smartclimateadvisor.org/#reviews).
This online platform is designed to provide comprehensive climate information, focusing on predicting drought conditions across the country. The website is a critical tool for farmers, agricultural extension officers, and policymakers, offering timely and accurate climate data to inform decision-making and proactive adaptation strategies.
Developed by Anna Msigwa | https://www.scienceblog.africa/fellows/dr-anna-msigwa
