Pushing the Nyquist-Shannon limit: exploiting multi-fidelity observations to provide high-resolution weather maps for climate services, early warning centres and weather forecasts

Abstract

The pursue of a higher-resolution gridded climate data and weather forecast requires an unprecedented number of surface observations to model the sub-mesoscale. National meteorological services (NMS) have practical and financial limitations to the number of observations it can collect, therefore, opening the door to crowdsourced weather initiatives might be an interesting option to mitigate data scarcity. In recent years, scientists have made remarkable efforts at assessing the quality of crowdsourced collections and determining ways these can add value to the “daily business” of NMS. In this work, we develop and apply a multi-fidelity spatial regression method capable of combining official observations with crowdsourced observations, which enables the creation of high-resolution interpolations of weather variables. The availability of a sheer volume of crowdsourced observations also poses questions on what is the maximum weather complexity that can be modelled with these novel data sources. We include a structured theoretical analysis simulating increasingly complex weather patterns that uses the Shannon-Nyquist limit as a benchmark. Results show that the combination of official and crowdsourced weather observations pushes further the Shannon-Nyquist limit, thus indicating that crowdsourced data contributes at monitoring sub-mesoscale weather processes (e.g. urban scales). We think that this effort illustrates well the potential of crowdsourced data, not only to expand the current range of products and services at NMS, but also opening the door for high-resolution weather forecast and monitoring, issuing local early warnings and advancing towards impact-based analyses.