Open AccessUsing a Scale-Selective Filter for Dynamical Downscaling with the Conformal Cubic Atmospheric Model
Author(s) -
Marcus Thatcher,
John L. McGregor
Publication year - 2009
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/2008mwr2599.1
Subject(s) - downscaling , scale (ratio) , filter (signal processing) , conformal map , convolution (computer science) , computation , meteorology , mathematics , computer science , algorithm , topology (electrical circuits) , mathematical analysis , physics , artificial intelligence , computer vision , precipitation , quantum mechanics , combinatorics , artificial neural network
This article examines dynamical downscaling with a scale-selective filter in the Conformal Cubic Atmospheric Model (CCAM). In this study, 1D and 2D scale-selective filters have been implemented using a convolution-based scheme, since a convolution can be readily evaluated in terms of CCAM’s native conformal cubic coordinates. The downscaling accuracy of 1D and 2D scale-selective filters is evaluated after downscaling NCEP Global Forecast System analyses for 2006 from 200-km resolution to 60-km resolution over Australia. The 1D scale-selective filter scheme was found to downscale the analyses with similar accuracy to a 2D filter but required significantly fewer computations. The 1D and 2D scale-selective filters were also found to downscale the analyses more accurately than a far-field nudging scheme (i.e., analogous to a boundary-value nudging approach). It is concluded that when the model is required to reproduce the host model behavior above a specified length scale then the use of an appropriately designed 1D scale-selective filter can be a computationally efficient approach to dynamical downscaling for models having a cube-based geometry.