Simple Extensions of an NWP Model

Abstract

This paper presents a study on simple and inexpensive techniques for extension of NMC's Medium Range Forecasting (MRF) model. Three control forecasts are tested to make 1-day extensions of 500-mb height fields initiated from the MRF at days 0?9. They are persistence (PER), a divergent anomaly vorticity advection model (dAVA), and the empirical wave propagation (EWP) method. First the traditional 1?10-day global forecasts made by the MRF and the three controls from a common set of 361 initial conditions are discussed. Taking this as a basis, 1-day extension control forecasts starting from MRF prediction over four successive winters are examined next. Experiments show that regardless of the presence or absence of the systematic error in the MRF model output, there exists some point (T0 = n) into the forecast after which the 1-day extension of the day n MRF out to day n + 1 by a control forecast is as good as or better than the continued integration of the full blown MRF model. In particular, the EWP provides a 1-day extension that beats the MRF most consistently after about 6 days in the Northern Hemisphere. Decomposition of the forecasts in terms of zonal harmonics further indicates that the skill improvement over the MRF is primarily in the long waves, but contributions from shorter waves are not negligible. Efforts have been made to understand the mechanisms by which simple methods are superior to complicated models for low-frequency prediction at extended range. It seems that at least two simplifications made in one or all of the control forecasts are crucial in outperforming the MRF beyond day 6. The first one is well known, that is, the contaminating effects of synoptic-scale baroclinic eddies have been filtered out in the simple models considered. More generally, the nonlinear terms (whether barotropic or baroclinic) contribute to skill deterioration beyond day 6. The second reason is the explicit elimination of the divergence process in the control forecasts, as the MRF model may contain significant errors in forecasting the divergence.