Hydro-climatic forecasting using sea surface temperatures: methodology and application for the southeast US

摘要: Sea surface temperatures (SSTs) are often used for the development of hydro-climatic variable forecasts based on teleconnection methods. Such methods rely projections or linear combinations indices [e.g. El Nino-Southern Oscillation (ENSO)] and other predictor fields. This study introduces a new forecasting method identifying SST “dipole” predictors motivated by major patterns. An dipole is defined as function average anomalies over two oceanic areas specific sizes geographic locations. optimization algorithm developed to search most significant an external series Gerrity Skill Score. The dipoles cross-validated generate multiple forecast values. applied seasonal precipitation southeast US. Hindcasting results show that related ENSO well prominent patterns at different lead times can indeed be identified. also compares favorably with existing statistical schemes respect skill measures. Furthermore, operational framework able produce ensemble traces uncertainty intervals support regional water resources planning management developed.