Benchmarking knowledge-assisted kriging for automated spatial interpolation of wind measurements

We have benchmarked a novel knowledge-assisted kriging algorithm that allows regions of spatial cohesion to be specified and variograms calculated for each region. The variogram calculation itself is automated and spatial regions created via offline automated segmentation of either expert-drawn Google Earth polygons or NASA altitude data. Our use-case is to create interpolated wind maps for input into a bathing water quality model of microbial contamination. We benchmark our knowledge-assisted kriging algorithm against 7 other algorithms on UK met-office wind data (189 sensors). Our wind estimation results are comparable to standard ordinary kriging using variograms created by an expert. When using spatial segmentation we find our kriging error maps reflect better the known spatial features of the interpolated phenomenon. These results are very promising for an automated approach allowing on-demand datasets selection and real-time interpolation of previously unknown measurements. Automation is important in progressing towards a pan-European interpolation service capability.