A preliminary analysis on data accuracy for IoT rainfall measurements

Rainfall is one of the most difficult hydrometeorological variable to measure due to its high variability in space and time. The rain gauges network is the oldest traditional method to collect point-related rainfall records. However, retrieving long time series for reliable estimates of rainfall could be tricky, and the rain gauge stations are not equally spread among the globe, resulting in large ungauged areas. Thus, rainfall information are often obtained by satellite-based observations and radar dataset. In this work we analyze the reliability of one of the novelties in the data monitoring for hydrological purposes. With the advent of the Internet-of-Thing (IoT), the latest generation of rainfall measurements is represented by Personal Meteorological Stations (PMSs) that automatically provide near-real-time data on online platforms. The question that normally arises is: are stations reliable enough to obtain rainfall estimates eligible for hydrological modeling? In this work we perform a preliminary analysis testing the performance of a Netatmo PMS located in Rome, compared to a rain gauge managed by the Italian Civil Protection. This preliminary analysis points out the potentiality of this new frontier of measurements, that, without any correction, provide promising results for the recorded rainfall at daily time scale.