A Dynamic Statistical Subseasonal Forecast Model for OLR Over Tropical Pacific Region - Wang, Kuo; Fan, Gao-Feng; Feng, Guo-Lin (Frontiers in Physics, 2022)
How to improve the subseasonal forecast skill of dynamic model has always been an important issue in atmospheric science and service. This study proposes a new dynamical-statistical forecasts method, stable components dynamic statistical forecast(STsDSF) for subseasonal outgoing long-wave radiation(OLR) over tropical Pacific region in January-February from 2004 to 2008. Comparing with 11 advanced multi-model ensemble(MME) daily forecast, STsDSF model can capture the change characteristics of OLR better when the lead time beyond 30 days in 2005 and 2006. The average pattern correlation coefficients(PCC) of STsDSF are 0.24 and 0.16 in 2005 and 2006, while MME are 0.10 and 0.05, respectively. In addition, the average value of PCC of STsDSF model in five years is higher than MME in 7-11 pentads. Although both STsDSF model and MME show a similar temporal correlation coefficient(TCC) pattern over tropical Pacific region, the STsDSF model error grows more slowly than MME error during 8-12 pentads in January 2005. This phenomenon demonstrates that STsDSF can reduce dynamical model error in some situations. According to the comparison of subseasonal forecasts between STsDSF and MME in five years, STsDSF model skill depends strictly on the predictability of dynamical model. The STsDSF model will show some advantages when the dynamical model can not forecast well above a certain level. In this study, STsDSF model can be used as an effective reference for subseasonal forecast, and seems feasible in real-time forecast business in the future.