  Continuous data assimilation (CDA) is successfully implemented for the first
time for efficient dynamical downscaling of a global atmospheric reanalysis. A
comparison of the performance of CDA with the standard grid and spectral
nudging techniques for representing long- and short-scale features in the
downscaled fields using the Weather Research and Forecast (WRF) model is
further presented and analyzed. The WRF model is configured at 25km horizontal
resolution and is driven by 250km initial and boundary conditions from
NCEP/NCAR reanalysis fields. Downscaling experiments are performed over a
one-month period in January, 2016. The similarity metric is used to evaluate
the performance of the downscaling methods for large and small scales.
Similarity results are compared for the outputs of the WRF model with different
downscaling techniques, NCEP reanalysis, and Final Analysis. Both spectral
nudging and CDA describe better the small-scale features compared to grid
nudging. The choice of the wave number is critical in spectral nudging;
increasing the number of retained frequencies generally produced better
small-scale features, but only up to a certain threshold after which its
solution gradually became closer to grid nudging. CDA maintains the balance of
the large- and small-scale features similar to that of the best simulation
achieved by the best spectral nudging configuration, without the need of a
spectral decomposition. The different downscaled atmospheric variables,
including rainfall distribution, with CDA is most consistent with the
observations. The Brier skill score values further indicate that the added
value of CDA is distributed over the entire model domain. The overall results
clearly suggest that CDA provides an efficient new approach for dynamical
downscaling by maintaining better balance between the global model and the
downscaled fields.
