Challenges and benefits from crowdsourced atmospheric data for urban climate research using Berlin, Germany, as testbed

Provision of atmospheric data from observational networks at high spatial resolution and over long time periods remains a challenge in urban climate research. Classical observational networks are designed for detection of synoptic atmospheric conditions, and thus are rarely suitable for city-specific and intra-urban analysis. Therefore, using citizens as data provider offers huge potentials, especially in urban areas due to high population density. The concept of citizen science is not new, especially in the field of ecology (Dickinson et al. 2012). This concept relies on active participation of citizens to contribute to research. A number of efforts have been made in recent years concerning atmospheric applications, e.g. mapping of atmospheric aerosols with smartphones (Snik et al., 2014) or involving citizens in observational networks such as “CoCoRaHS” (Community Collaborative Rain, Hail and Snow Network, http://www.cocorahs.org/) or the “Citizen Weather Observer Program” (http://wxqa.com). Another approach to acquire huge amounts of data is the concept of crowdsourcing, defined by Dickinson et al. (2012) as “…getting an undefined public to do work, usually directed by designated individuals or professionals…” For instance, Overeem et al. (2013) took battery-temperature measurements from smartphones to derive urban air temperatures by using data from the smartphone application ‘OpenSignal’ (opensignal.com), while Mass and Madaus (2014) exploited air-pressure measurements from another application called ‘pressureNET’ (pressurenet.cumulonimbus.ca) to simulate an active convection event in the United States. The netatmo urban weather stations (www.netatmo.com) act as an intermediate between active citizen science and crowdsourcing of passively acquired data. The netatmo company develops and distributes weather stations around the world for interested citizens for monitoring the atmospheric conditions inside and outside their buildings. The netatmo weather station is cost-efficient, and Wi-Fi connection serves for data transfer, storage and visualisation via application software. These smart devices upload automatically their data to the netatmo server. They belong to the ‘Internet of things’, which plays an important role for recent innovations in data mining and crowdsourcing (Muller et al. 2015). While netatmo weather stations offer huge potentials due to dense spatial coverage in many urban areas, the question remains if and how crowdsourced data from this source could be suitable for urban climate research. What are the key challenges and benefits? The focus of this contribution is on crowdsourced air temperature (Ta) records.