Novel data science approaches could drive advances in seasonal to sub-seasonal predictions of precipitation
Predictions at the seasonal to sub-seasonal scale are important for planning and decision-making in a variety of disciplines, and improving understanding and model skill at this timescale is a key research priority. An as yet underexplored approach to sub-seasonal prediction using data science and graph theory methods that are increasingly common to other fields outside of meteorology and climate science shows potential to improve predictions at this challenging timescale.
