Extremes across resolutions

Datasets from different sources or different scientific fields are often monitored on different domains. This entails different spatial scales as well as different time scales. In order to harness all available sources and compound information from different scientific fields, it is important to be able to make meaningful inference across those fields regardless of whether the data are observed on different domains. In this study, we propose how to detect common extremes or possible causalities between extremal events on different domains. The techniques to detect causalities between extremes across different resolutions are illustrated based on potential links between photosynthesis and drought data. In particular, we study the rate at which solar energy is captured in sugar molecules during photosynthesis and the Palmer drought index, a measurement of dryness based on recent precipitation and temperature. While the first data set is available on a longitude and latitude grid, the drought data are observed at different station.