MetObs, to streamline your crowdsourced data processing.

Data from non-traditional measurement networks, such as crowdsourced meteorological data, present unique and complex challenges in data structure, quality, consistency, lack of metadata, and analysis. Observational campaigns often encounter issues such as missing data due to technical failures, power outages, or communication problems. Furthermore, crowdsourced or low-cost sensor data require rigorous quality control to mitigate errors and biases, leading to additional gaps in the data. Additionally, inconsistent storage formats and temporal resolution complicate the use and combination of datasets across different networks. 

To address these challenges, we developed the MetObs-toolkit, an open-source Python package designed to streamline the processing of observational meteorological data. The MetObs-toolkit facilitates the entire workflow, from raw sensor data to a comprehensive analysis. Common use cases are resampling and synchronization of time series, automated and spatial quality control, Google Earth Engine interaction for additional metadata, gap-filling techniques, and analysis tools. Designed for both students and experienced scientists, MetObs offers accessible tutorials and examples, enabling users to gain insights into their observational data. By providing an open-source standardized approach, MetObs improves data usability, ultimately enhancing the impact of non-traditional observational data efforts.

This presentation will demonstrate how the MetObs-toolkit facilitates the analysis of crowdsourced and non-traditional urban climate data.