Parallel implementations of ensemble data assimilation for atmospheric prediction

The model state can be represented by a grid of discrete values; subsets of grid points are assigned to tasks for parallel solution. Data assimilation algorithms are used to combine information from a model forecast with atmospheric observations to produce an improved state estimate.

Observations are irregular in space and time, for instance following the track of a polar orbiting satellite. Ensemble assimilation algorithms use statistics from a set (ensemble) of forecasts to update the model state. All the challenges of heterogeneous grid computing and partitioning for atmospheric models are in play.