Operational Implementation of Displacement Data Assimilation

In meteorology and other geophysical fluid contexts it is often useful to characterize the flow in terms of features - a hurricane, front, mesoscale convective systems, or individual convective storm cells. Differences in the position of a feature especially result in errors with substantial and complex spatial correlations that may be non-Gaussian. Displacement data assimilation solves for the displacements, anticipating that the residual errors after aligning the model background fields will be smaller and more nearly Gaussian.

We report on the migration of an existing variational displacement data assimilation implementation (within the Weather Research and Forecasting Model Data Assimilation, WRFDA, system: Nehrkorn et al. 2015) into the operational National Centers for Environmental Prediction (NCEP) data assimilation scheme (Gridpoint Statistical Interpolation, GSI). Changes to the WRFDA-based software will be described that facilitate migration to the GSI and other DA systems.

We will present preliminary results of the GSI implementation from case studies for convective scale data assimilation. We will outline extensions of the technique suitable for hybrid ensemble-variational assimilation schemes, and present initial plans for a reformulation of displacement data assimilation that is model agnostic and suitable for integration into the Joint Effort for Data Assimilation (JEDI) framework.

Nehrkorn, T., B. Woods, R.N. Hoffman T. Auligne, 2015: Correcting for Position Errors in Variational Data Assimilation, Mon. Wea. Rev., 143 (4), 1368-1381, doi:10.1175/mwr-d-14-00127.1.