Spatial Interpolation for Ground Station-based Environment Data in Oklahoma

The first case study focuses on spatial interpolation for ground station-based environmental data. Spatial interpolation is a critical process in environmental science, where data from ground stations are used to estimate values in unmeasured locations. This case study showcases how our Common Workflow Language (CWL) based Workflow Management System (WfMS) can handle complex spatial data processes, ensuring accurate and efficient interpolation. In this case study, two spatial interpolation models were implemented. IDW (Inverse Distance Weighting) is a deterministic method for spatial interpolation, which estimates the values at unknown points using the values from known points weighted by the inverse of their distances. Kriging is a geostatistical interpolation technique that not only considers the distance between known and unknown points but also models the spatial autocorrelation among the measured points. It provides more accurate estimations by using a weighted average of known points, where the weights are determined based on the spatial structure of the data.