NEDAS.assim_tools.assimilators.batch module

class NEDAS.assim_tools.assimilators.batch.BatchAssimilator(c: Context)

Bases: Assimilator

assim_mode: str = 'batch'

init_partitions(c: Context) → list

Generate spatial partitioning of the domain partitions: dict[par_id, tuple(istart, iend, di, jstart, jend, dj)] for each partition indexed by par_id, the tuple contains indices for slicing the domain Using regular slicing is more efficient than fancy indexing (used in irregular grid)

assign_obs(c: Context) → dict

Assign the observation sequence to each partition par_id

assign_obs_to_tiles(c, state, obs, obs_rec_id)

distribute_partitions(c: Context)

Distribute partitions across processors

assimilation_algorithm(c: Context)

batch assimilation solves the matrix version EnKF analysis for each local state, the local states in each partition are processed in parallel

abstractmethod local_analysis(c, loc_id, ind, hlfactor, state_data, obs_data)

Local analysis scheme for each model state variable (grid point) to be implemented by derived classes