Crowdsourcing air temperature from citizen weather stations for urban climate research

Provision of accurate air temperature data in urban environments with high spatial and temporal resolution over long time periods remains a challenge in atmospheric research. Crowdsourcing, i.e., collection of atmospheric data from non-traditional sources like citizen weather stations (CWS), is an alternative and cost-efficient method for exploration and monitoring of urban climates. This study examines the suitability of crowdsourced air temperature (T-crowd) measurements from CWS by comparing T-crowd from up to 1500 stations with reference air temperature (T-ref) in Berlin and surroundings for a period of twelve months (Jan-Dec 2015). Comprehensive quality assessment of T-crowd reveals that erroneous metadata, failure of data collection, and unsuitable exposure of sensors lead to a reduction of data availability by 53%. Spatially aggregated raw data of T-crowd already provide a robust estimate of hourly and daily urban air temperature in the study area. Quality-checked T-crowd observations showspatio-temporal characteristics of the urban heat island in Berlin with higher spatial variability than T-ref in built-up areas. Spatial density of T-crowd in Berlin exceeds that of the reference monitoring network by far. However, rigorous data quality assessment is the key challenge in order to fully benefit from this novel data set for urban climate research. (C) 2017 Elsevier B.V. All rights reserved.