SmaRD-AI aims at supporting „Environmental Data Science“ by providing a suitable middle layer between data publication/management and data analyses. The latter frequently require combning different data sources with a variety of spatial and temporal resolutions and uncertainties to a coherent data basis. However, virtual research environments and data management systems which can handle this task are rare.
The virtual research environment V-FOR-WaTer provides an existing prototype of such a system. We want to extend this system and use the example of the variety in precipitation estimates to accommodate complex 4-D data sets from rainfall radar measurements. Additional to standard precipitation estimates from rain gauges run by official weather services, we also want to test the information gain when including more unusual measurements such as derived water vapour content from GNSS data. The workflows which are developed within SmaRD-AI will be transferrable to other complex environmental datasets and their integration with standard measurements.