数值预报中自由度的压缩及误差相似性规律

利用历史资料的有用信息提高数值模式预报水平是长期以来人们努力的目标。该文提出了一种在气候吸引子上缩小初始场自由度的相似选取方法,有效滤除了小尺度分量,避开了原有相似选取中自由度太大,相似选取困难的问题。分析表明:相似初值间的模式预报误差存在相似性,依此估计的预报误差与实际预报误差很接近。在空间分布特征上,相似初值间的模式预报误差也有很好的一致性。这为发展相似-动力方法,利用历史资料改进数值模式提供了支持。

Compression for Freedom Degree in Numerical Weather Prediction and the Error Analogy

A substantial improvement in the skill of numerical weather prediction has been achieved in recent years. Useful skill typically extends numerical weather prediction from present-day to about 6 days. But for longer timescale, such as 6-15 days, the forecast quality is poor. It is necessary to improve the quality of the results.Utilizing useful information in historical data to improve the prediction skill of numerical model has been considered as a goal. The analogue-dynamical method is one of these optimum methods to combine dynamical methods and statistical methods together. However, the analogue selection is very hard due to broad freedom in traditional analogue selection methods. So an analogue selection method under small de-gree of freedom is proposed. By the method degree of freedom on low dimensional climate attractor is re-duced, and small scale components are filtered. This method is based on the theory that the high dimen-sional phase space in a forced dissipative nonlinear system eventually evolves into a low dimensional at-tractor, while small-scale high frequency patterns are dissipated. Based on compression for degree of free-dom of the initial field, an analogue index is defined.In order to further develop the analogue-dynamical method, the regularity of the error similarity is in-vestigated. Analyses show that similar initial conditions lead to analogical characteristics of forecast er-rors. Compared with the system error, the error under analogical initial condition is more close to actual forecast error. Meanwhile, in the spatial scale, forecast errors between analogical initial conditions are well consistent. The preliminary results of the forecast experiments on a complicated T63 Dynamical Ex-tended-Range Forecasting model of NCC/CMA show that the analogue-dynamical method can effectively reduce the model error. Results provide support for further developing the analogue-dynamical method.Evidently, further theoretical and more prediction experiments are necessary for improving the per-formance of analogue-dynamical method. Meanwhile, the model predictable components should be reason-ably obtained. These problems will be studied in further work.