Abstract
Abstract. In atmospheric models, due to their computational time or resource limitations, physical processes have to be simulated using reduced (i.e. simplified) models. The use of a reduced model, however, induces errors to the simulation results. These errors are referred to as approximation errors. In this paper, we propose a novel approach to correct these approximation errors. We model the approximation error as an additive noise process in the simulation model and employ the Random Forest (RF) regression algorithm for constructing a computationally low cost predictor for the approximation error. In this way, the overall simulation problem is decomposed into two separate and computationally efficient simulation problems: solution of the reduced model and prediction of the approximation error realisation. The approach is tested for handling approximation errors due to a reduced coarse sectional representation of aerosol size distribution in a cloud droplet formation calculation as well as for compensating the uncertainty caused by the aerosol activation parameterization itself. The results show a significant improvement in the accuracy of the simulation compared to the conventional simulation with a reduced model. The proposed approach is rather general and extension of it to different parameterizations or reduced process models that are coupled to geoscientific models is a straightforward task. Another major benefit of this method is that it can be applied to physical processes that are dependent on a large number of variables making them difficult to be parameterized by traditional methods.
Highlights
In numerical simulations of complicated physical phenomena, one usually has to balance between the model accuracy and the computation time
The approach is tested for handling approximation errors due to a reduced coarse sectional representation of aerosol size distribution in a cloud droplet formation calculation as well as for compensating the uncertainty caused by the aerosol activation parameterization itself
To evaluate the proposed approach, multiple Random Forest (RF) predictor models for the approximation errors corresponding to both approximate ARG parameterizations, with 7 and 4 size sections, were constructed with different RF training parameters
Summary
In numerical simulations of complicated physical phenomena, one usually has to balance between the model accuracy and the computation time. Reduction in computation time is typically obtained by using reduced models for some of the functions in the model. We consider the approximation errors caused by coarse discretization of aerosol size distributions in sectional aerosol models. The continuous aerosol particle size distributions are represented with discrete size sections The accuracy of the description of the size distribution increases with increasing number of size sections. The computational demand of the model, is heavily increased with the number of the sections. A compromise between the model accuracy and the computational time has to be made to construct a feasible model for simulations of atmospheric scale
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