中文 |
中文

Research Progress

Home / Newsroom / News Archive / Research Progress / 2017

A New Method to Generate Ensemble Initial Perturbations

Oct 28, 2017

The atmosphere is a chaotic system, and negligible initial errors will give rise to gradual deviation of the forecast state from the true path, eventually resulting in chaos. This means that the weather has a predictability limit beyond which forecasts will lose all skills. Therefore, any single forecast is simply an estimate of the future state of the atmosphere within a stochastic framework, and provides no information regarding its reliability.

Ensemble prediction offers one approach to generate probabilistic forecasts of the future state of the system based on a statistical sampling approach. In the past two decades, ensemble forecasting has been developed substantially to become a powerful approach that improves numerical weather prediction. Data assimilation schemes were further combined with the dynamical methods to better sample the analysis uncertainties, such as in the ensemble transform Kalman filter (ETKF) scheme.

In a paper featured on the front cover of Advances in Atmospheric Sciences, Dr. DING Ruiqiang at Institute of Atmospheric Physics of Chinese Academy of Sciences and co-authors extended the definition of the nonlinear local Lyapunov exponents (NLLE) from one- to n-dimensional spectra, and demonstrated the superiority of the NLLE spectrum in estimating the predictability of chaotic systems, as compared to the traditional Lyapunov exponent spectrum. In addition to estimating the predictability of chaotic systems, another important application of the NLLE method is to provide initial perturbations for ensemble forecasting.
The results indicated that the NLLE scheme has similar ensemble forecasting skill to the ETKF scheme, both of which demonstrate better ensemble forecast skill compared with the bred vector (BV) and the singular vector (SV) schemes. Despite the similar forecasting skills of the nonlinear local Lyapunov vector (NLLV) and ETKF schemes, the generation of the NLLVs is significantly more time-saving and easier to implement, as compared to the ETKF scheme.
"We expect the NLLE scheme to be effective in generating ensemble perturbations in a high-dimensional numerical model," said DING. "In future works, we intend to further investigate the performance of the NLLE through comparison with various methods in more complex models, and our ultimate goal is to apply the NLLE method in operational weather forecasts."

 

The concept of ensemble forecasting is depicted schematically in the center, in which it is demonstrated that the initial error will cause the forecast uncertainty. The Lorenz attractor, shown scattered in green, just like the wings of a butterfly, indicates the initial uncertainty. (Image by AAS)

Contact Us

  • 86-10-68597521 (day)

    86-10-68597289 (night)

  • 52 Sanlihe Rd., Xicheng District,

    Beijing, China (100864)

Copyright © 2002 - Chinese Academy of Sciences