Skilful long‐lead hybrid predictions of the East African short‐rains season. (20th December 2021) - Colman, Andrew
Abstract: We evaluate the skill of predictions of the East African short‐rains (October–December) season, of up to 5 months lead, based on a two‐stage dynamical/statistical hybrid approach. The statistical component is a well‐established, zero‐month lead, method based on observed ocean‐scale patterns of July–September sea surface temperature. A strong advantage of using this method is that its skill is high and also very robustly measured – a correlation of 0.77 between forecast and observed area‐mean rainfall is achieved over the 26 years of real‐time forecasts. Here, we extend the lead time by driving the statistical component with predicted July–September sea surface temperatures available from a number of dynamical model seasonal prediction systems. There is correlation skill near 0.5 for models initialized in May and positive skill from forecasts initialized as early as the previous October. Abstract : A hybrid long forecasting system to predict the East African October–December short rains has been developed by combining state of the art dynamical models with an established statistical method. There is correlation skill near 0.5 for models initialized in the preceding May and positive skill from forecasts initialized as early as the previous October.