The Potential of a Smartphone as an Urban Weather Station—An Exploratory Analysis

Abstract

The ongoing urbanization requires enhanced understanding of the local meteorological and climatological conditions within the urban environment for multiple applications, concerning energy demand, human health, and spatial planning. Identifying areas with harmful meteorological conditions enables citizens and local authorities to take actions to optimize quality of life for urban dwellers. At the moment cities have (in general) limited networks of meteorological monitoring stations. To overcome this lack of observations, the use of non-traditional data sources is rapidly increasing. However, the use of such data sources without enough prior verification has become a controversial topic in the scientific community. This study aims to verify and assess one of the main non-traditional data sources, i.e. smartphones. The goal is to research the potential of smartphones (using the Samsung Galaxy S4 as an example phone model) to correctly sense air temperature, relative humidity, and solar radiation, and to determine to what extent environmental conditions negatively affect their performance. The smartphone readings were evaluated against observations from reference instrumentation at a weather station and a mobile measurement platform. We test the response time of the smartphone thermometer and hygrometer, and the light sensor’s cosine response. In a lab setting, we find that a smartphone can provide reliable temperature information when it is not exposed to direct solar radiation. The smartphone’s hygrometer performs better at low relative humidity levels while it can over-saturate at higher levels. The light sensor records show substantial correlation with global radiation observations, and short response times. Measurements along an urban transect of 10 km show the smartphone’s ability to react to fast changes of temperature in the field, both in time and space. However, a bias correction (dependent on wind speed and radiation) is required to represent the reference temperature. Finally we show that after such a bias correction, a smartphone record can successfully capture spatial variability over a transect as well.

The role of citizen science mobile apps in facilitating a contemporary digital agora

“…Therefore, it requires methods for data filtering and data accuracy testing, in some cases AI models (Annis and Nardi, 2019;Brigham et al 2018;Castro-Gutiérrez et al 2022;Omodior et al 2021;Pepe et al 2021). It also needs methods for testing the accuracy of mobile devices (Blair et al 2018;Cabrera et al 2021).…”

Digital Weather Information in an Embodied World

“…People who obtain present weather information from smart devices should remember that once their device is geolocated, an app will supply weather information that is taken from the nearest NWS observing site. While hardware and apps are available to turn a smartphone into a mini observing station [100,101], most people do not obtain their weather information that way. Similarly, maps and other graphics may be zoomable to the location of the smart device, but apps often fill-in (interpolate) and smooth data.…”

Transformational IoT sensing for air pollution and thermal exposures

“…Although this methodology provides an unprecedented dataset on city-scale thermal environment, the uncertainties of data collection are of concern and increase in cities with more extreme weather and higher precipitation when the smartphone is most likely to be held enclosed (Martilli et al, 2017). A closer comparison of mobile measurements with scientific-grade sensors reveals that, even when smartphones are exposed to ambient air, uncertainties are increased with higher humidity (where the smartphone hygrometer is saturated) and bias corrections are needed for Sun exposure and high wind speeds (Cabrera et al, 2021), which is yet to be developed for realistic and dynamic data measurements. In the third category, mobile sensors mounted on vehicles, bicycles, and public transit systems have been used to make strategic transects through cities to observe thermal comfort variables, providing valuable real-time data, particularly in the face of extreme events in cities (Heusinkveld et al, 2014;Castell et al, 2015;Anjomshoaa et al, 2018).…”