2016 Volume 94A Pages 47-68
Reanalysis data sets have been widely used in regional climate dynamical downscaling studies. In this study, we test the use of various reanalysis data sets in constraining dynamical downscaling by assessing the reconstruction skill of the Yellow Sea coastal winds using the COSMO model in Climate Mode (CCLM) with 7-km resolution. Four reanalysis forcing data sets are used as lateral boundary conditions and internal large-scale constraints (spectral nudging): the National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis data set (NCEP1) is downscaled to an intermediate domain with 55-km resolution (CCLM_55km), ERA-interim reanalysis data set (ERAint), NCEP climate forecast system reanalysis data set (CFSR), and Japanese 55-year reanalysis data set (JRA55).
Several statistical analysis methods are employed to assess the modeled winds through comparison with observed offshore wind data from 2006, and it is found that the downscaled simulations yield good quality wind speed products. However, they all tend to overestimate observed low wind speeds and underestimate observed high wind speeds. Furthermore, the quality of the modeled wind direction is strongly associated with the wind speed intensities, exhibiting a much better reproduction of wind direction at strong wind speeds than at light wind speeds.
The downscaling simulations driven by ERAint, JRA55, and CFSR are consistent with each other in the reproduction of local wind speed and direction; the simulations driven by ERAint and JRA55 are slightly better for strong winds and those driven by CFSR are better for light winds. All three simulations generate local wind estimates that are superior to those of the simulation driven by CCLM_55km. This superiority reflects the better quality of the CFSR, ERAint, and JRA55 reanalyses with regard to assimilated local observations compared with the CCLM_55km hindcast, which exploits only upper-air large scale NCEP1 wind fields.