ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-12-3601-2012 Differences between downscaling with spectral and grid nudging using WRF Liu P. 1 Tsimpidi A. P. 2 Hu Y. 2 Stone B. 3 Russell A. G. 2 Nenes A. 1 4 School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA School of Architecture, Georgia Institute of Technology, Atlanta, GA, 30332, USA School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA 16 04 2012 12 8 3601 3610 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/12/3601/2012/acp-12-3601-2012.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/12/3601/2012/acp-12-3601-2012.pdf

Dynamical downscaling has been extensively used to study regional climate forced by large-scale global climate models. During the downscaling process, however, the simulation of regional climate models (RCMs) tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields) and the small-scale features (from the RCMs) has led to the development of "nudging" techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the downscaling of NCEP/NCAR data with the Weather Research and Forecasting (WRF) Model. The simulations are compared against the results with North America Regional Reanalysis (NARR) data set at different scales of interest using the concept of similarity. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.

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