Using Observed Spatial Correlation Structures to Increase the Skill of Subseasonal Forecasts.

Model deficiencies limit a subseasonal or seasonal forecast system’s ability to produce accurate predictions. In this paper, an approach for transforming the output of a forecast system into a revised forecast is presented; it is designed to correct for some of the deficiencies in the system (particularly those associated with the spatial correlation structures of the forecasted fields) and thereby increase forecast skill. The approach, based on the joint consideration of the correlation structures present in the observational record and the inherent potential predictability of the model, is tested on a preexisting subseasonal forecast experiment. It is shown to produce modest but significant increases in the accuracy of forecasted precipitation and near-surface air temperature at monthly time scales. [ABSTRACT FROM AUTHOR]