摘要
This paper summarizes recent progress at the State Key Laboratory of Numerical Modeling for Atmospheric Sci- ences and Geophysical Fluid Dynamics （LASG）, Institute of Atmospheric Physics, Chinese Academy of Sciences in studies on targeted observations, data assimilation, and ensemble prediction, which are three effective strategies to re- duce the prediction uncertainties and improve the forecast skill of weather and climate events. Considering the limita- tions of traditional targeted observation approaches, LASG researchers have developed a conditional nonlinear op- timal perturbation-based targeted observation strategy to optimize the design of the observing network. This strategy has been employed to identify sensitive areas for targeted observations of the E1 Nifio-Southern Oscillation, Indian Ocean dipole, and tropical cyclones, and has been demonstrated to be effective in improving the forecast skill of these events. To assimilate the targeted observations into the initial state of a numerical model, a dimension-reduced- projection-based four-dimensional variational data assimilation （DRP-4DVar） approach has been proposed and is used operationally to supply accurate initial conditions in numerical forecasts. The performance of DRP-4DVar is good, and its computational cost is much lower than the standard 4DVar approach. Besides, ensemble prediction, which is a practical approach to generate probabilistic forecasts of the future state of a particular system, can be used to reduce the prediction uncertainties of single forecasts by taking the ensemble mean of forecast members. In this field, LASG researchers have proposed an ensemble forecast method that uses nonlinear local Lyapunov vectors （NLLVs） to yield ensemble initial perturbations. Its application in simple models has shown that NLLVs are more useful than bred vectors and singular vectors in improving the skill of the ensemble forecast. Therefore, NLLVs rep- resent a candidate for possible development as an ensemble method in operational forecasts. Despite the consider- able efforts made towards developing these methods to reduce prediction uncertainties, much challenging but highly important work remains in terms of improving the methods to further increase the skill in forecasting such weather and climate events.
This paper summarizes recent progress at the State Key Laboratory of Numerical Modeling for Atmospheric Sci- ences and Geophysical Fluid Dynamics （LASG）, Institute of Atmospheric Physics, Chinese Academy of Sciences in studies on targeted observations, data assimilation, and ensemble prediction, which are three effective strategies to re- duce the prediction uncertainties and improve the forecast skill of weather and climate events. Considering the limita- tions of traditional targeted observation approaches, LASG researchers have developed a conditional nonlinear op- timal perturbation-based targeted observation strategy to optimize the design of the observing network. This strategy has been employed to identify sensitive areas for targeted observations of the E1 Nifio-Southern Oscillation, Indian Ocean dipole, and tropical cyclones, and has been demonstrated to be effective in improving the forecast skill of these events. To assimilate the targeted observations into the initial state of a numerical model, a dimension-reduced- projection-based four-dimensional variational data assimilation （DRP-4DVar） approach has been proposed and is used operationally to supply accurate initial conditions in numerical forecasts. The performance of DRP-4DVar is good, and its computational cost is much lower than the standard 4DVar approach. Besides, ensemble prediction, which is a practical approach to generate probabilistic forecasts of the future state of a particular system, can be used to reduce the prediction uncertainties of single forecasts by taking the ensemble mean of forecast members. In this field, LASG researchers have proposed an ensemble forecast method that uses nonlinear local Lyapunov vectors （NLLVs） to yield ensemble initial perturbations. Its application in simple models has shown that NLLVs are more useful than bred vectors and singular vectors in improving the skill of the ensemble forecast. Therefore, NLLVs rep- resent a candidate for possible development as an ensemble method in operational forecasts. Despite the consider- able efforts made towards developing these methods to reduce prediction uncertainties, much challenging but highly important work remains in terms of improving the methods to further increase the skill in forecasting such weather and climate events.
基金
Supported by the China Meteorological Administration Special Public Welfare Research Fund(GYHY201306018)
National Natural Science Foundation of China(41525017 and 41506032)
