Efficient Dynamical Downscaling of General Circulation Models Using Continuous Data Assimilation
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 0.25°×0.25° horizontal resolution and is driven by 2.5°×2.5° 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 (2000 km) and small (300 km) scales. Similarity results are compared for the outputs of the WRF model with different downscaling techniques, NCEP/NCAR reanalysis, and NCEP Final Analysis (FNL, available at 0.25°×0.25° horizontal resolution). 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 with increasing wave number. CDA maintains the balance of large- and small-scale features similar to that of the best simulation achieved using spectral nudging, without the need of a spectral decomposition. The downscaled different atmospheric variables including rainfall distribution with CDA is most consistent with the observations. The overall, results clearly suggest that CDA provides an efficient new approach for dynamical downscaling by better maintaining the balance between the global model and the downscaled fields.
Prof. Ibrahim Hoteit King Abdullah University of Science and Technology