Forecast scheme and strategy for extended-range predictable components

Abstract

Although extended-range forecasting has exceeded the limit of daily predictability of weather, there are still partially predictable characteristics of meteorological fields in such forecasts. A targeted forecast scheme and strategy for extended-range predictable components is proposed. Based on chaotic characteristics of the atmosphere, predictable components and unpredictable random components are separated by using the standpoint of error growth in a numerical model. The predictable components are defined as those with slow error growth at a given range, which are not sensitive to small errors in initial conditions. A numerical model for predictable components (NMPC) is established, by filtering random components with poor predictability. The aim is to maintain predictable components and avoid the influence of rapidly growing forecast errors on small scales. Meanwhile, the analogue-dynamical approach (ADA) is used to correct forecast errors of predictable components, to decrease model error and statistically take into account the influence of random components. The scheme is applied to operational dynamical extended-range forecast (DERF) model of the National Climate Center of China Meteorological Administration (NCC/CMA). Prediction results show that the scheme can improve forecast skill of predictable components to some extent, especially in high predictability regions. Forecast skill at zonal wave zero is improved more than for ultra-long waves and synoptic-scale waves. Results show good agreement with predictability of spatial scale. As a result, the scheme can reduce forecast errors and improve forecast skill, which favors operational use.