pyPDAF.PDAF.omi_localize_covar_iso

pyPDAF.PDAF.omi_localize_covar_iso()

The function has to be called in localize_covar_OBTYPE in each observation module. It applies the covariance localisation in stochastic EnKF. This is used for isotropic localisation where the localisation radius is the same in all directions. See https://pdaf.awi.de/trac/wiki/PDAFomi_localize_covar

Parameters:

• i_obs (int) – index of observation type

• locweight (int) – Types of localization function 0) unit weight; 1) exponential; 2) 5-th order polynomial; 3) 5-th order polynomial with regulatioin using mean variance; 4) 5-th order polynomial with regulatioin using variance of single observation point;

• cradius (float) – Vector of localization cut-off radii; observation weight=0 if distance > cradius

• sradius (float) – Vector of support radii of localization function. It has no impact if locweight=0; weight = exp(-d / sradius) if locweight=1; weight = 0 if d >= sradius else f(sradius, distance) if locweight in [2,3,4].

• coords (ndarray[tuple[ncoord, dim_p], np.float64]) –

  Coordinates of state vector elements

  The 1st-th dimension ncoord is number of coordinate dimension The 2nd-th dimension dim_p is State dimension

• HP (ndarray[tuple[dim_obs, dim_p], np.float64]) –

  Matrix HP, dimension (nobs, dim)

  The 1st-th dimension dim_obs is Observation dimension The 2nd-th dimension dim_p is State dimension

• HPH (ndarray[tuple[dim_obs, dim_obs], np.float64]) –

  Matrix HPH, dimension (nobs, nobs)

  The 1st-th dimension dim_obs is Observation dimension The 2nd-th dimension dim_obs is Observation dimension

Returns:

• HP (ndarray[tuple[dim_obs, dim_p], np.float64]) – Matrix HP, dimension (nobs, dim)

  The 1st-th dimension dim_obs is Observation dimension The 2nd-th dimension dim_p is State dimension

• HPH (ndarray[tuple[dim_obs, dim_obs], np.float64]) – Matrix HPH, dimension (nobs, nobs)

  The 1st-th dimension dim_obs is Observation dimension The 2nd-th dimension dim_obs is Observation dimension