Abstract
The frequency and severity of extreme weather events have increased over the last 30 years, making predictability of weather a challenge. Weather extreme events often cause adverse impacts to lives and property. Thus, accurate and timely provision of weather data is becoming crucial to improve the skill of weather prediction and to strengthen resilience to the impacts of the adverse weather conditions. Uganda and many developing countries have challenges in acquiring accurate and timely weather data due to their sparse weather observation networks. The sparse weather observation networks are in part attributed to the high cost of acquiring an Automatic Weather Station (AWS) and limited funding to national meteorological services of the respective countries. The inability of developing countries to manufacture their own AWSs leads to high recurring costs accruing from importation and maintenance. In this study, we propose an AWS based on Wireless Sensor Networks. We plan to design three generations of the AWS prototype, the first being the subject of this paper. The purpose of this paper is therefore to evaluate the first-generation AWS prototype and to propose improvements for the second-generation, based on needs and requirements. Results from the AWS prototype data suggest improving non-functional requirements such as reliability, data accuracy, power consumption and data transmission in order to have an operational AWS. The non-functional requirements combined with cost reduction produces a robust and affordable AWS. Therefore, developing countries like Uganda will be able to acquire the AWSs in reasonable quantities, hence improvement in weather forecasts.
Highlights
Background and motivationWeather is the present condition of the atmosphere over a given place and time, measured in terms of variables including precipitation, temperature, wind speed and direction and humidity among others (K. et al, 2010)
We evaluated the Automatic Weather Station (AWS) reliability through assessing the ratio of downtime and uptime
Automatic Weather Stations play a major role in weather information management since they provide timely and reliable data, higher chances of accurate weather predictions
Summary
Background and motivationWeather is the present condition of the atmosphere over a given place and time, measured in terms of variables including precipitation, temperature, wind speed and direction and humidity among others (K. et al, 2010). Weather patterns were predictable based on indigenous knowledge e.g. one would tell in which months of the year rains were expected for a given place. Such methods of weather prediction have become unreliable (Hansen et al, 2012). A general global warming and heavy rains have been observed, leading to increased flooding and droughts in different parts of the world, which suggested changing climate and is of present concern globally (Hansen et al, 2012; Dube et al, 2016). The study on the economic impact of drought on agriculture in Uganda indicated a decline of 5% in the Gross Domestic Product (GDP), which resulted in losses in the industrial sector (Kilimani et al, 2016)
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