Assessing and Improving the Local Added Value of WRF for Wind Downscaling

Abstract

imited area models (LAMs) are widely used tools to downscale the wind speed forecasts issued by general circulation models. However, only a few studies have systematically analyzed the value added by the LAMs to the coarser-resolution-model wind. The goal of the present work is to investigate how added value depends on the resolution of the driving global model. With this aim, the Weather Research and Forecasting (WRF) Model was used to downscale three different global datasets (GFS, ERA-Interim, and NCEP?NCAR) to a 9-km-resolution grid for a 1-yr period. Model results were compared with a large set of surface observations, including land station and offshore buoy data. Substantial biases were found at this resolution over mountainous terrain, and a slight modification to the subgrid orographic drag parameterization was introduced to alleviate the problem. It was found that, at this resolution, WRF is able to produce significant added value with respect to the NCEP?NCAR reanalysis and ERA-Interim but only a small amount of added value with respect to GFS forecasts. Results suggest that, as model resolution increases, traditional skill scores tend to saturate. Thus, adding value to high-resolution global models becomes significantly more difficult.