How useful are crowdsourced air temperature observations? An assessment of Netatmo stations and quality control schemes over the United Kingdom

Abstract

Observations of the real-time state of the atmosphere are required in order to initialize numerical weather prediction (NWP) models. As NWP resolution improves, more observations are needed, to better capture regional variations in atmospheric conditions. In particular, surface observations are necessary to reflect conditions experienced on the surface. One proposed opportunity to increase the number of surface observations available for assimilation into NWP is to crowdsource the data from home weather stations. This study investigates the outdoor air temperature measurements made by Netatmo home weather stations, through validation against a calibrated laboratory chamber and by evaluating quality control schemes that are applied to a UK-wide network of Netatmo stations. In a series of controlled lab experiments, it was found that the Netatmo temperature sensor was accurate to 0.3 C. The response to fluctuations in temperature is lagged, with τ (the time taken for 63% of the change to be measured) calculated as 12.7 min for a nearinstantaneous decrease in temperature. Netatmo temperature observations were compared with Met Office MIDAS hourly weather observations. A warm bias in excess of 1 C was present in the Netatmo temperature observations, which was lessened by the three quality control schemes tested, but still in excess of 0.5 C. Hence, Netatmo temperature measurements have potential to be assimilated in NWP in the United Kingdom, but work is required to find a suitable agreed quality control scheme to filter out anomalous observations in the United Kingdom.

The importance of crowdsourced observations for urban climate services

“…Netatmo is a manufacturer network that has the considerable advantages of standardized equipment and a high density of sites. However, studies have shown that the instrument has an inbuilt lag and that if it is not positioned in shade then it is vulnerable to radiative overheating from direct sunlight (Büchau, 2018; Coney et al, 2022; Meier et al, 2017). Moreover, if the owner does not specify their location, a false location may be assigned using the IP address of the assigned wireless network (Madelin & Dupuis, 2020; Meier et al, 2017).…”

“…The immediate challenge that is faced when attempting to use such citizen science observations is the need for an adequate quality control process. The observations may suffer from any combination of shortcomings: the equipment design may introduce systematic biases (Bell et al, 2015), systematic lags (Büchau, 2018; Coney et al, 2022) or permit radiative over‐heating (Bell, 2014; Cornes et al, 2020); the reporting may be intermittent or short‐term; the metadata may be false or fail to record changes in position (Madelin & Dupuis, 2020). The task of quality controlling these observations has been tackled using both a temporal approach (Meier et al, 2015; Napoly et al, 2018) and a spatial approach (Alerskans et al, 2022; Nipen et al, 2020).…”

Improved mesoscopic meteorological modelling of the urban climate for building physics applications

“…It also needs no additional static input data. One possible future application would be to use crowdsourcing public measurement networks like Netatmo as input dataset for ML [12].…”

Utility of thermal remote sensing for evaluation of a high‐resolution weather model in a city

“…Standard WMO site observations, such as those located in urban parks, represent grassed areas rather than the mix of buildings and vegetation that occur in different neighbourhoods. Citizen science weather stations—for example, Netatmo (Chapman et al, 2017; Fenner et al, 2021) and WOW (Kirk et al, 2021)—and WMO (2018b) recommended urban sites may better represent the mix of land covers in their source areas (Coney et al, 2022; Cornes et al, 2019; Muller et al, 2015; Vesala et al, 2008). If urban canopy layer observations are used for model evaluation, appropriate downscaling of variables from the inertial sublayer to within the urban canopy layer is required (e.g., Blunn et al, 2022; Tang et al, 2021; Theeuwes et al, 2019; Wang, 2014).…”