Forecast 4D‐Var: Exploiting Model Output Statistics

Abstract

AbstractForecasts of certain weather elements are improved by linearly relating observed elements to past observations, climatological information, and numerical weather‐prediction‐model output. Model Output Statistics (MOS) is a statistical post‐processing of model output that is capable of forecasting some subgrid scale, synoptically‐forced events and of correcting some systematic, but state dependent, model bias. This tendency of accounting for model error is exploited by feeding MOS forecasts back into the state estimation problem. MOS forecasts and their associated uncertainty are treated as ‘observations’ of the future system state, and a four‐dimensional variational assimilation procedure is employed to improve the original analysis and resulting model forecast. In a simple‐model scenario, it is found that this approach has a small negative impact on the magnitude of forecast errors relative to MOS with a decrease in the number of very good forecasts, but a large positive impact on the variance about the forecast errors: forecast busts are reduced. As a further step, a second round of MOS is performed on the new model forecasts in a manner identical to the original MOS approach. This second application of MOS results in a significant reduction in both the forecast errors and the variance about those errors relative to the first application of MOS, and includes an increase in very good forecasts. Copyright © 2003 Royal Meteorological Society