  Weather events put human lives at risk mostly when people might reside in
areas susceptible to natural disasters. Weather monitoring is a pivotal task
that is accomplished in vulnerable areas with the support of reliable weather
stations. Such stations are front-end equipment typically mounted on a fixed
mast structure with a set of digital and magnetic weather sensors connected to
a datalogger. While remote sensing from a number of stations is paramount, the
cost of professional weather instruments is extremely high. This imposes a
challenge for large-scale deployment and maintenance of weather stations for
broad natural disaster monitoring. To address this problem, in this paper, we
validate the hypothesis that a Low-Cost Automatic Weather Station system
(LCAWS) entirely developed from commercial-off-the-shelf and open-source IoT
technologies is able to provide data as reliable as a Professional Weather
Station (PWS) of reference for natural disaster monitoring. To achieve data
reliability, we propose an intelligent sensor calibration method to correct
weather parameters. From the experimental results of a 30-day uninterrupted
observation period, we show that the results of the calibrated LCAWS sensors
have no statistically significant differences with the PWS's results. Together
with The Brazilian National Center for Monitoring and Early Warning of Natural
Disasters (Cemaden), LCAWS has opened new opportunities towards reducing
maintenance cost of its weather observational network.
