A systematic method of parameterisation estimation using data assimilation
A systematic method of parameterisation estimation using data assimilation
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DOI：
10.3402/tellusa.v68.29012
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发表时间：
2016-01-01
影响因子：
2
通讯作者：
Browne, Philip
中科院分区：
文献类型：
--
作者：
Lang, Matthew;Van Leeuwen, Peter Jan;Browne, Philip
在数值天气预报中，参数化用于模拟模型中缺失的物理现象。这可能是由于缺乏科学理解或缺乏可用于解决所有已知物理过程的计算能力。参数化是模型中较大不确定性的来源，因为这些参数化中使用的参数值无法直接测量，因此通常不为人所知;参数化本身也是真实大气中存在的过程的近似值。虽然在数据同化（DA）中有许多有效和高效的组合状态/参数估计方法，如状态增强，但这些方法在估计参数化结构时并不有效。提出了一种新的参数化估计方法，该方法使用顺序DA方法来估计每个模型方程在每个时空点处的数值模型中的误差。然后将这些误差拟合到基于先验信息的缺失物理或参数化的预定函数形式。我们将该方法应用到一个一维平流模型与添加剂模型误差，它表明，该方法可以准确地估计参数化，一致的误差估计。此外，它示出该方法如何取决于DA结果的质量。结果表明，该方法是系统模型改进的有力工具。
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.