Performance of the ATMOS41 All-in-One Weather Station for Weather Monitoring

Abstract

Affordable and accurate weather monitoring systems are essential in low-income and developing countries and, more recently, are needed in small-scale research such as precision agriculture and urban climate studies. A variety of low-cost solutions are available on the market, but the use of non-standard technologies raises concerns for data quality. Research-grade all-in-one weather stations could present a reliable, cost effective solution while being robust and easy to use. This study evaluates the performance of the commercially available ATMOS41 all-in-one weather station. Three stations were deployed next to a high-performance reference station over a three-month period. The ATMOS41 stations showed good performance compared to the reference, and close agreement among the three stations for most standard weather variables. However, measured atmospheric pressure showed uncertainties >0.6 hPa and solar radiation was underestimated by 3%, which could be corrected with a locally obtained linear regression function. Furthermore, precipitation measurements showed considerable variability, with observed differences of ±7.5% compared to the reference gauge, which suggests relatively high susceptibility to wind-induced errors. Overall, the station is well suited for private user applications such as farming, while the use in research should consider the limitations of the station, especially regarding precise precipitation measurements.

Towards Increasing Data Availability for Meteorological Services: Inter-Comparison of Meteorological Data from a Synoptic Weather Station and Two Automatic Weather Stations in Kenya

“…This is in agreement with other findings that have shown that meteorological parameters measured from a ground station can compare relatively well with other observations from a reference source e.g. AWS (Dombrowski et al, 2021).…”

“…Whereas the surface pressure was highly correlated between the three stations, some studies have found uncertainties in the comparisons between on ground station pressure and the reference data, e.g. (Dombrowski et al, 2021). The high variability between some meteorological variables at different stations could be due to several factors such as instrument error, change of location, damages or lack of station maintenance (Ford et al, 2020).…”

Exploring the Potential of the Cost-Efficient TAHMO Observation Data for Hydro-Meteorological Applications in Sub-Saharan Africa

“…Particular focus should be laid on high precipitation intensities occurring in tropical regions. Dombrowski et al [17] report about relatively large biases for precipitation and wind speed. Further work should focus on the performance assessment of the ATMOS 41 during extreme precipitation and wind speed as well as the long-term durability and accuracy of the station.…”

IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

“…Intelligent agriculture, based on Internet of Things (IoT) technologies, allows farmers to reduce waste and increase productivity, from irrigation with greater precision to the amount of fertilizer used [2]. In addition, it can help prevent pollution [3], monitoring climatic conditions at low cost [4], and bring solutions by adding artificial intelligence methods [5,6].…”