MeteoTrackers (MT) in Citizens Science -A New Era in Micrometeorology or just an Instrument for education?Lessons from MT operations within I-CHANGE EU project

The I-CHANGE (Individual Change of HAbits Needed for Green European transition, 2021-2025) project promotes the active participation of citizens to address climate change. It engages citizens and local stakeholders to take part in science initiatives and support more sustainable behaviour. To this aim, a set of Living Labs located in very different eight cities of socio-economic contexts (Amsterdam, Barcelona, Bologna, Dublin, Genova, Hasselt, Jerusalem and Ouagadougou), were chosen. The I-CHANGE Living Labs address different environmental issues all employing Meteotrackers (MT) in order to perform high-resolution meteorological measurements.

With recent emergence of new types of near-surface meteorological data that are exploding in their big numbers and cover much higher resolution than classical or World Meteorological Organization (WMO) data, much interest is naturally given to the quality and validation of this crowdsourcing data. The present note focuses on MeteoTracker (in brevity, MT) data collected by citizens walking or biking and travelling.

The present note suggests a practical methodology for operating MTs, along with the suggestion of the potential emergence of a new era in micrometeorological measurements that allows high resolution, both spatial and temporal. Micrometeorology in the sense of obtaining data on the scales of ~1 m, ~1 min and ~0.1 deg for temperature etc. A great challenge in such measurements is that there are a multitude of factors influencing surface observations and it is a complex task to define which factors, as well as their potential synergies, are involved, or just which are dominant. Thus, allowing better understanding of the synergies among several microscale factors. Such factors include, among many others, land cover temporal/spatial variations of agriculture, water, soil moisture, trees, urban area, isolated buildings, as well as topographical variations, solar insolation, cloudiness, aerosols, mesoscale dynamical effects, synoptics.

The basic concept here is that although these new data types are still involved with operation challenges and several error types, the very large amounts of MT data compensate when compared to classical measurements. A few examples, based on many measured days, are demonstrated here.

I-CHANGE is funded by EU Horizon 2020 grant 101037193.