Erik Smith

May 23, 2023 | journals.ametsoc.org | Cameron Lee |Erik Smith |Scott C. Sheridan |Omon A. Obarein

Abstract Accurate sub-seasonal to seasonal (S2S) weather forecasts are crucial to making important decisions in many sectors. However, significant gaps exist between the needs of society and what forecasters can produce, especially at weekly and longer lead times. We hypothesize that by clustering atmospheric states into a number of predefined categories, the noise can be reduced, and consequently, medium-range forecasts can be improved.