NEDAS.assim_tools.assimilators.serial module

class NEDAS.assim_tools.assimilators.serial.SerialAssimilator(c: Context)

Bases: Assimilator

Subclass for serial assimilation algorithms

assim_mode: str = 'serial'

init_partitions(c: Context) → list

Generate spatial partitioning of the domain

assign_obs(c: Context)

Assign the observation sequence to each partition par_id

Parameters:

c (Context) – the runtime context object

Returns:

Indices in the full obs_seq for the subset of obs that belongs to partition par_id

Return type:

dict[ObsRecordID, dict[PartitionID, np.ndarray]]

distribute_partitions(c: Context)

Distribute partitions across processors

assimilation_algorithm(c: Context)

Implementation of the serial assimilation algorithm.

Notes

serial assimilation goes through the list of observations one by one for each obs the near by state variables are updated one by one. so each update is a scalar problem, which is solved in 2 steps: obs_increment, update_ensemble

abstractmethod obs_increment(obs_prior, obs, obs_err) → ndarray

Compute observation-space analysis increments.

Parameters:

• obs_prior (np.ndarray) – Observation priors, 1-D float array of length nens

• obs (float) – The real observation value

• obs_err (float) – Observation error std

Returns:

observation-space analysis increments

Return type:

ndarray

abstractmethod update_local_state(state_prior, obs_prior, obs_incr, state_h_dist, state_v_dist, state_t_dist, hroi, vroi, troi, h_local_func, v_local_func, t_local_func) → None

Update the local state vector with the analysis increments.

Parameters:

• state_data (np.ndarray) – Local state vector, shape (nens, nfld, nloc)

• obs_prior (np.ndarray) – Observation priors, shape (nens,)

• obs_incr (np.ndarray) – Analysis increments, shape (nens,)

abstractmethod update_local_obs(obs_data, used, obs_prior, obs_incr, h_dist, v_dist, t_dist, hroi, vroi, troi, h_local_func, v_local_func, t_local_func) → None

Update the local observations with analysis increments.

Parameters:

• obs_data (np.ndarray) – obs prior ensemble, shape (nens, nlobs)

• used (np.ndarray) – boolean mask of already-assimilated obs

transform_ens_state_forward(state_data)

transform_ens_state_backward(state_data)

transform_ens_obs_forward(obs_data)

transform_ens_obs_backward(obs_data)