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High-resolution regional climate simulations of precipitation patterns over the western U.S using WRF

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Thursday, 27 January 2011
High-resolution regional climate simulations of precipitation patterns over the western U.S using WRF
Washington State Convention Center
John Mejia, DRI, Reno, NV; and D. Koracin

Poster PDF (1.9 MB)

We have evaluated the overall performance of different Regional Climate Model configurations using the WRF 3.1.1 model for climate change projections and impact studies with respect to a varying horizontal resolution and sensitivity to a selection of physical parameterizations. Two cold seasons with observed precipitation extremes (referring to 2002-03 as dry and 2004-05 as wet years) were selected to evaluate the model under different large-scale patterns forced with the NCEP/NCAR reanalysis products. We tested 3 nested domains with 36, 12 and 4 km horizontal grid resolutions and 16 different physics configurations. No internal nudging was implemented in order to evaluate the model's ability to reproduce the seasonal precipitation extremes. NWS, COOPS and RAWS surface stations were used for evaluation of the surface temperature and precipitation amounts over the Great Basin in the western U.S. Results show that the model moderately responds to the large-scale forcing conditions producing the wet and dry years; however, it shows poor performance in reproducing the details of the regional precipitation patterns. Furthermore, different error metrics reveal that the model substantially improves when reducing the horizontal grid size from 36 to 12 km, but with smaller or even without any improvements for the 4 km horizontal grid size simulations. It appears that the error accumulation associated with model drifting in the inner domain (4 km) overcomes the benefits of the horizontal resolution improvements. The different physics configurations showed systematic biases (with a relatively small spread) of surface temperature and precipitation parameters; however, we have found a set of best options for our regional climate downscaling applications.