Scientists Develop and Test a New Method that Helps Reduce the Uncertainty in Numerical Models


The uncertainty related to physical parameters is a major challenge in numerical modeling. However, due to the large number of such parameters in numerical models, reducing the uncertainty for all of them would be extremely expensive in terms of manpower and resources. Therefore, it is critical to be able to identify and study the most important and sensitive physical parameters and parameter combinations in numerical models.
To address this issue, CAS member Mu Mu and his team from Fudan University and the Institute of Atmospheric Physics at the Chinese Academy of Sciences first analyzed the limitations of the traditional (variance-based) approach for parameter sensitivity analysis, which seems unable to consider extreme events because of the statistical influence of discrete parameter samples, and then developed a novel approach from the deterministic point of view. They subsequently applied this new method, named "conditional nonlinear optimal perturbations sensitivity analysis" (CNOPSA), to a grassland ecosystem model to test its feasibility. Their findings have recently been published in Advances in Atmospheric Sciences.
According to the team's results, the CNOPSA method is capable of fully considering the nonlinear synergistic effects of the parameters and can deterministically estimate the maximum effect on the model output due to the uncertainties in the physical parameters. Thus, the greater the maximum effect on the model output due to parameter uncertainty, the more important and sensitive the parameter is. Numerical results showed that the CNOPSA method was effective in identifying the sensitivity of the physical parameters in the tested grassland ecosystem model. These parameters shifted the modeled wilted biomass, which affected the transformation of the grassland state in the ecosystem. By comparison, the variance-based approach underestimated the parameter sensitivity because it failed to consider the effects of all parameters in the parameter space.
"In future work, we intend to employ even more complex land surface process models to validate the usefulness and effectiveness of the CNOPSA method", says Prof. Mu.
Citation: Ren, Q. J., M. Mu, G. D. Sun, and Q. Wang. 2022: A new sensitivity analysis approach using conditional nonlinear optimal perturbations and its preliminary application. Adv. Atmos. Sci., 
© 2014-2024 IAP/CAS, All rights reserved.
No. 81 Beichen West Road, Chaoyang District, P. O. Box 9804, Beijing 100029, P. R. China
Tel: +86-10-82995251 Fax: +86-10-82995180 E-mail: Technical Support:Qingyun Software
京ICP备14024088号-6 京公网安备:110402500041