This function provides complete support for the multi-core and multi-node computation on the general HPC system. Thus, this script will be more computationally efficient, making it possible to run SDA over thousands of locations.
Usage
sda.enkf_local(
settings,
obs.mean,
obs.cov,
Q = NULL,
pre_enkf_params = NULL,
ensemble.samples = NULL,
outdir = NULL,
control = list(TimeseriesPlot = FALSE, OutlierDetection = FALSE, send_email = NULL,
keepNC = TRUE, forceRun = TRUE, MCMC.args = NULL)
)Arguments
- settings
PEcAn settings object
- obs.mean
Lists of date times named by time points, which contains lists of sites named by site ids, which contains observation means for each state variables of each site for each time point.
- obs.cov
Lists of date times named by time points, which contains lists of sites named by site ids, which contains observation covariances for all state variables of each site for each time point.
- Q
Process covariance matrix given if there is no data to estimate it.
- pre_enkf_params
Used for passing pre-existing time-series of process error into the current SDA runs to ignore the impact by the differences between process errors.
- ensemble.samples
Pass ensemble.samples from outside to avoid GitHub check issues.
- outdir
physical path to the folder that stores the SDA outputs. Default is NULL.
- control
List of flags controlling the behavior of the SDA. `TimeseriesPlot` for post analysis examination; `OutlierDetection` decide if we want to execute the outlier detection each time after the model forecasting; `send_email` contains lists for sending email to report the SDA progress; `keepNC` decide if we want to keep the NetCDF files inside the out directory; `forceRun` decide if we want to proceed the Bayesian MCMC sampling without observations; `MCMC.args` include lists for controling the MCMC sampling process (iteration, nchains, burnin, and nthin.).
Author
Dongchen Zhang zhangdc@bu.edu