摘要
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.
Although extendedrange forecasting has exceeded the limit of daily predictability of weather, there are still partially predicta ble characteristics of meteorological fields in such forecasts. A targeted forecast scheme and strategy for extendedrange pre dictable components is proposed. Based on chaotic characteristics of the atmosphere, predictable components and unpredicta ble random components are separated by using the standpoint of error growth in a numerical model. The predictable compo nents 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 predictabil ity. The aim is to maintain predictable components and avoid the influence of rapidly growing forecast errors on small scales. Meanwhile, the analoguedynamical 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 dy namical extendedrange 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 ultralong waves and synopticscale 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.
基金
supported by National Natural Science Foundation of China (Grant Nos.41105070,40930952 and 41005041)
State Key Program of Science and Technology of China(Grant No.2009BAC51B04)
Meteorological Special Project of China(Grant No.GYHY 201106016)
