A systematic method of parameterisation estimation using data assimilation

• Published in: Tellus. Series A, Dynamic meteorology and oceanography Vol. 68; no. 1; pp. 29012 - 10
• Main Authors: Lang, Matthew, Jan Van Leeuwen, Peter, Browne, Philip
• Format: Journal Article
• Language: English
• Published: Stockholm Taylor & Francis 01.12.2016; Ubiquity Press; Stockholm University Press
• Subjects: Additives; data assimilation; Data collection; Errors; Mathematical models; Methods; Parameter estimation; parameterisation estimation; Physics; Weather forecasting
• ISSN: 1600-0870; 1600-0870
• DOI: 10.3402/tellusa.v68.29012

In numerical weather prediction, parameterisations are used to simulate missing physics in the model. These can be due to a lack of scientific understanding or a lack of computing power available to address all the known physical processes. Parameterisations are sources of large uncertainty in a model as parameter values used in these parameterisations cannot be measured directly and hence are often not well known; and the parameterisations themselves are also approximations of the processes present in the true atmosphere. Whilst there are many efficient and effective methods for combined state/parameter estimation in data assimilation (DA), such as state augmentation, these are not effective at estimating the structure of parameterisations. A new method of parameterisation estimation is proposed that uses sequential DA methods to estimate errors in the numerical models at each space-time point for each model equation. These errors are then fitted to pre-determined functional forms of missing physics or parameterisations that are based upon prior information. We applied the method to a one-dimensional advection model with additive model error, and it is shown that the method can accurately estimate parameterisations, with consistent error estimates. Furthermore, it is shown how the method depends on the quality of the DA results. The results indicate that this new method is a powerful tool in systematic model improvement.