Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting

arXiv:2211.15856v3 Announce Type: replace
Abstract: Producing high-quality forecasts of key climate variables, such as temperature and precipitation, on subseasonal time scales has long been a gap in operational forecasting. This study explores an application of machine learning (ML) models as post-processing tools for subseasonal forecasting. Lagged numerical ensemble forecasts (i.e., an ensemble where the members have different initial dates) and observational data, including relative humidity, pressure at sea level, and geopotential height, are incorporated into various ML methods to predict …

abstract application arxiv beyond climate cs.lg ensemble forecasting gap key machine machine learning numerical physics.ao-ph post-processing precipitation processing quality study tools type variables