See Botto et al. 2018: "When assimilating multiple variables, proper normalization of the measurement error covariance matrices, anomalies of the simulated data, and innovation vectors were performed, using values of 0.6 m, 0.58, and 4.17 × 10−5 m3 s−1 for pressure head, water content and subsurface outflow, respectively. The normalization ensures that in multivariate assimilation scenarios the covariance matrices in the Kalman gain are not ill-conditioned (Evensen, 2003; Camporese et al., 2009b)."
Examples of applications in prep:
[ ] Farming dataset (Carrera et al.)
[ ] Noble dataset (Peruzzo et al.)
[ ] Re della Pietra dataset
[ ] Bousval dataset
[ ] Synthetic dataset Weill
How?
from pyCATHY.DA.cathy_DA import DA
from pyCATHY.DA import perturbate, normalise
from pyCATHY.DA.cathy_DA import perturbate_parm, dictObs_2pd
# We create a DA object
simu = DA(dirName=path2prj, prj_name=prj_name_DA)
# We read all the observations (multiples types)
read_observations()
# Here is the new function to add: need to normalise before creation of the data covariance matrice
normalise.normalise()
make_data_cov()
# Parameters pertubation
perturbate.perturbate()
# run DA simulation
simu.run_DA_sequential()
Improvement suggestion for DA
See Botto et al. 2018: "When assimilating multiple variables, proper normalization of the measurement error covariance matrices, anomalies of the simulated data, and innovation vectors were performed, using values of 0.6 m, 0.58, and 4.17 × 10−5 m3 s−1 for pressure head, water content and subsurface outflow, respectively. The normalization ensures that in multivariate assimilation scenarios the covariance matrices in the Kalman gain are not ill-conditioned (Evensen, 2003; Camporese et al., 2009b)."
Examples of applications in prep:
How?