Efficient dynamical downscaling of general circulation models using continuous data assimilation Academic Article uri icon

abstract

  • © 2019 Royal Meteorological Society 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 analysed. 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 (2,000 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. 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.

author list (cited authors)

  • Desamsetti, S., Dasari, H. P., Langodan, S., Titi, E. S., Knio, O., & Hoteit, I.

citation count

  • 8

publication date

  • August 2019

publisher